Physical Sciences and Mathematics Commons^™

Mitochondrial Genome Polymorphism In Lolium Perenne, Y Shimamoto, Y Tominaga, M Sato, T Mikami

IGC Proceedings (1997-2023)

The restriction fragment length polymorphisms (RFLPs) of mitochondrial DNA (mtDNA) of perennial ryegrass (Lolium perenne L.) were investigated to elucidate the genetic relatedness among the 128 cultivars including diploid and tetraploid. Many patterns of RFLPs were observed and allowed assigning of the cultivars into the main eight haplotypes of mitochondrial genome relatedness. The American cultivars were classified into haplotype I and VIII which were remote at the mitochondrial genome from each other, the European ones were distributed to all haplotypes and the tetraploid ones were mostly assigned into the haplotype V. The assessment of mtDNA RFLPs may be a …

Prediction Of Sumoylation Sites In Proteins From Language Model Representations, Evgenii Sidorov

Dissertations, Master's Theses and Master's Reports

Sumoylation is an essential post-translational modification intimately involved in a diverse range of eukaryotic cellular mechanisms and plays a significant role in DNA repair. Some researchers hypothesize that a high level of SUMOylation events in cancer cells improves cells' chances for survival under stress conditions by regulating tumor-related proteins.

This study belongs to a booming field of harnessing computational power to the domain of life. Prediction of protein structure, its molecular function, and the design of new drugs are just a few examples of the applications within this exciting area of research. By leveraging computational power, researchers can analyze vast …

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …

Artificial Intelligence For Para Rubber Identification Combining Five Machine Learning Methods, Chairote Yaiprasert Ph.D.

Karbala International Journal of Modern Science

This study aims to identify Para rubber species using a combination of five machine learning techniques to classify leaf images. The learning process is defined using a dataset for each classification method. Approximately 1,472 leaf images are prepared consisting of various sizes, shapes, quality provided for the model. The classification indicators are defined with the help of an algorithm to identify at least three of the top five potential classification outcomes. The algorithm accurately predicts 100% of the five classification methods. Methods can provide precise and rapid classification of large quantities, without the need for image preprocessing prior to classification.

Deep Learning Applications In Medical Bioinformatics, Ziad Omar

Electronic Theses and Dissertations

After a patient’s breast cancer diagnosis, identifying breast cancer lymph node metastases is one of the most important and critical factor that is directly related to the patient’s survival. The traditional way to examine the existence of cancer cells in the breast lymph nodes is through a lymph node procedure, biopsy. The procedure process is time-consuming for the patient and the provider, costly, and lacks accuracy as not every lymph node is examined. The intent of this study is to develop an artificial neural network (ANNs) that would map genetic biomarkers to breast lymph node classes using ANNs. The neural …

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 …

Plant Species Identification In The Wild Based On Images Of Organs, Meghana Kovur

Graduate Theses, Dissertations, and Problem Reports

Image-based plant species identification in the wild is a difficult problem for several reasons. First, the input data is subject to a very high degree of variability because it is captured under fully unconstrained conditions. The same plant species may look very different in different images, while different species can often appear very similar, challenging even the recognition skills of human experts in the field. The large intra-class and small inter-class image variability makes this a fine-grained visual classification problem. One way to cope with this variability and to reduce image background noise is to predict species based on the …

Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii

Masters Theses

“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …

Classification Of Rangeland Resource Types Based On Multi‐Source Remote Sensing Data, Xiaoqing Sui, Shazhou An, Kun Wang

IGC Proceedings (1997-2023)

No abstract provided.

Relation Between Vegetation And Soil In West Azarbaijan Rangelands Of Iran, J. Torkan, S. Gholinejad, A. Alijanpoor

IGC Proceedings (1997-2023)

No abstract provided.

A Systematic Mapping Study On The Risk Factors Leading To Type Ii Diabetes Mellitus, Karar N. J Musafer, Fahrul Zaman Huyop, Mufeed J Ewadh, Eko Supriyanto, Mohammad Rava

Karbala International Journal of Modern Science

Diabetes is one of the most common diseases that has had devastating effects on the general population. It is also among the most popular research trends in modern medicine. Thus, due to the complexity and desirability of this particular affliction, there is a lot of demand towards understanding this disease better, so that it can pave the way towards better solutions in combating diabetes. The aim of this review is to provide a categorization of the risk factors leading to Type II Diabetes. In order to provide a justification for the type of diabetes, an explanation is provided which covers …

A Novel Method For Detecting Morphologically Similar Crops And Weeds Based On The Combination Of Contour Masks And Filtered Local Binary Pattern Operators, Vi Nguyen Thanh Le, Selam Ahderom, Beniamin Apopei, Kamal Alameh

Research outputs 2014 to 2021

Background: Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. Results: We propose a novel method using a combination of filtered features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k. The classification of …

Coral Reef Change Detection In Remote Pacific Islands Using Support Vector Machine Classifiers, Justin J. Gapper, Hesham El-Askary, Erik Linstead, Thomas Piechota

Mathematics, Physics, and Computer Science Faculty Articles and Research

Despite the abundance of research on coral reef change detection, few studies have been conducted to assess the spatial generalization principles of a live coral cover classifier trained using remote sensing data from multiple locations. The aim of this study is to develop a machine learning classifier for coral dominated benthic cover-type class (CDBCTC) based on ground truth observations and Landsat images, evaluate the performance of this classifier when tested against new data, then deploy the classifier to perform CDBCTC change analysis of multiple locations. The proposed framework includes image calibration, support vector machine (SVM) training and tuning, statistical assessment …

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 …

Evaluation Of Spatial Generalization Characteristics Of A Robust Classifier As Applied To Coral Reef Habitats In Remote Islands Of The Pacific Ocean, Justin J. Gapper, Hesham El-Askary, Erik J. Linstead, Thomas Piechota

Mathematics, Physics, and Computer Science Faculty Articles and Research

This study was an evaluation of the spectral signature generalization properties of coral across four remote Pacific Ocean reefs. The sites under consideration have not been the subject of previous studies for coral classification using remote sensing data. Previous research regarding using remote sensing to identify reefs has been limited to in-situ assessment, with some researchers also performing temporal analysis of a selected area of interest. This study expanded the previous in-situ analyses by evaluating the ability of a basic predictor, Linear Discriminant Analysis (LDA), trained on Depth Invariant Indices calculated from the spectral signature of coral in one location …

Assessment Of The Ponderosa Woodlands In Nebraska's Wildcat Hills: Implications For Juniperus Encroachment And Management, Allie Victoria Schiltmeyer

School of Natural Resources: Dissertations, Theses, and Student Research

Ponderosa pine (Pinus ponderosa) is a dominant tree species across western North America. Its eastern distribution includes three populations in western Nebraska. This study assesses the distribution, structure and age of ponderosa pine woodlands in one of those regions, the Wildcat Hills. The Wildcat Hills have escaped severe wildfires seen in recent decades in other ponderosa pine regions. Nevertheless, the Wildcat Hills woodlands face multiple threats including climate change, wildfire, drought, pine beetles, and invasive species. Key to these threats is the stand structure of pine woodlands, which have increased in density across much of ponderosa pine’s range. …

Deep Learning-Based Framework For Autism Functional Mri Image Classification, Xin Yang, Saman Sarraf, Ning Zhang

Journal of the Arkansas Academy of Science

The purpose of this paper is to introduce deep learning-based framework LeNet-5 architecture and implement the experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.

Unsupervised Biomedical Named Entity Recognition, Omid Ghiasvand

Theses and Dissertations

Named entity recognition (NER) from text is an important task for several applications, including in the biomedical domain. Supervised machine learning based systems have been the most successful on NER task, however, they require correct annotations in large quantities for training. Annotating text manually is very labor intensive and also needs domain expertise. The purpose of this research is to reduce human annotation effort and to decrease cost of annotation for building NER systems in the biomedical domain. The method developed in this work is based on leveraging the availability of resources like UMLS (Unified Medical Language System), that contain …

Statistical Methods For Assessing Individual Oocyte Viability Through Gene Expression Profiles, Michael O. Bishop, John R. Stevens, S. Clay Isom

Conference on Applied Statistics in Agriculture

In vivo derived oocytes are held as the gold standard for viability, other known origination methods are sub-par by comparison. Due to the low-viability of oocytes originating from these alternate methods, research was conducted to determine and quantify the validity of these alternate origination methods. However, the larger question of viability is on the individual oocyte level. We propose and compare methods of measurement based on gene expression profiles (GEPs) in order to assess oocyte viability, independent of oocyte origin. The first is based on a previously published wRMSD quantification of GEP differences. We also consider three novel methods: a …

A Novel Approach For Classifying Gene Expression Data Using Topic Modeling, Soon Jye Kho, Himi Yalamanchili, Michael L. Raymer, Amit Sheth

Kno.e.sis Publications

Understanding the role of differential gene expression in cancer etiology and cellular process is a complex problem that continues to pose a challenge due to sheer number of genes and inter-related biological processes involved. In this paper, we employ an unsupervised topic model, Latent Dirichlet Allocation (LDA) to mitigate overfitting of high-dimensionality gene expression data and to facilitate understanding of the associated pathways. LDA has been recently applied for clustering and exploring genomic data but not for classification and prediction. Here, we proposed to use LDA inclustering as well as in classification of cancer and healthy tissues using lung cancer …

A Framework For The Statistical Analysis Of Mass Spectrometry Imaging Experiments, Kyle Bemis

Open Access Dissertations

Mass spectrometry (MS) imaging is a powerful investigation technique for a wide range of biological applications such as molecular histology of tissue, whole body sections, and bacterial films , and biomedical applications such as cancer diagnosis. MS imaging visualizes the spatial distribution of molecular ions in a sample by repeatedly collecting mass spectra across its surface, resulting in complex, high-dimensional imaging datasets. Two of the primary goals of statistical analysis of MS imaging experiments are classification (for supervised experiments), i.e. assigning pixels to pre-defined classes based on their spectral profiles, and segmentation (for unsupervised experiments), i.e. assigning pixels to newly …

Computerized Classification Of Surface Spikes In Three-Dimensional Electron Microscopic Reconstructions Of Viruses, Younes Benkarroum

Dissertations, Theses, and Capstone Projects

The purpose of this research is to develop computer techniques for improved three-dimensional (3D) reconstruction of viruses from electron microscopic images of them and for the subsequent improved classification of the surface spikes in the resulting reconstruction. The broader impact of such work is the following.

Influenza is an infectious disease caused by rapidly-changing viruses that appear seasonally in the human population. New strains of influenza viruses appear every year, with the potential to cause a serious global pandemic. Two kinds of spikes – hemagglutinin (HA) and neuraminidase (NA) – decorate the surface of the virus particles and these proteins …

Automated Detection Of Deep-Sea Animals, Dallas J. Hollis, Duane Edgington, Danelle Cline

STAR Program Research Presentations

The Monterey Bay Aquarium Research Institute routinely deploys remotely operated underwater vehicles equipped with high definition cameras for use in scientific studies. Utilizing a video collection of over 22,000 hours and the Video Annotation and Reference System, we have set out to automate the detection and classification of deep-sea animals. This paper serves to explore the pitfalls of automation and suggest possible solutions to automated detection in diverse ecosystems with varying field conditions. Detection was tested using a saliency-based neuromorphic selective attention algorithm. The animals that were not detected were then used to tune saliency parameters. Once objects are detected, …

Can Acute Dermal Systemic Toxicity Tests Be Replaced With Oral Tests? A Comparison Of Route-Specific Systemic Toxicity And Hazard Classifications Under The Globally Harmonized System Of Classification And Labelling Of Chemicals (Ghs), Nigel P. Moore, David J. Andrew, Donald L. Bjerke, Stuart Creton, David Dreher, Thomas Holmes, Pilar Prieto, Troy Seidle, Tim G. Rowan

Troy Seidle, PhD

Acute systemic toxicity data (LD50 values) and hazard classifications derived in the rat following oral administration and dermal application have been analysed to examine whether or not orally-derived hazard classification or LD50 values can be used to determine dermal hazard classification. Comparing the oral and dermal classifications for 335 substances derived from oral and dermal LD50 values respectively revealed 17% concordance, and indicated that 7% of substances would be classified less severely while 76% would be classified more severely if oral classifications were applied directly to the dermal route. In contrast, applying the oral LD50 values within the dermal classification …

Statistical Methods For Proteomic Biomarker Discovery Based On Feature Extraction Or Functional Modeling Approaches, Jeffrey S. Morris

Jeffrey S. Morris

In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational …

Classification And Visualization Of Neural Patterns Using Subspace Analysis Statistical Methods, Jun Xia, Marius Osan, Emilia Titan, Riana Nicolae, Remus Osan

Neuroscience Institute Faculty Publications

The size and complexity of neural data is increasing at a dramatic pace due to rapid advances in experimental technologies. As a result, the data analysis techniques are shifting their focus from single-units to neural populations. The goal is to investigate complex temporal and spatial patterns, as well as to present the results in an intuitive way, allowing for detection and monitoring of relevant neural patterns.

Cellulose- And Xylan-Degrading Thermophilic Anaerobic Bacteria From Biocompost, M. V. Sizova, J. A. Izquierdo, N. S. Panikov, L. R. Lynd

Dartmouth Scholarship

Nine thermophilic cellulolytic clostridial isolates and four other noncellulolytic bacterial isolates were isolated from self-heated biocompost via preliminary enrichment culture on microcrystalline cellulose. All cellulolytic isolates grew vigorously on cellulose, with the formation of either ethanol and acetate or acetate and formate as principal fermentation products as well as lactate and glycerol as minor products. In addition, two out of nine cellulolytic strains were able to utilize xylan and pretreated wood with roughly the same efficiency as for cellulose. The major products of xylan fermentation were acetate and formate, with minor contributions of lactate and ethanol. Phylogenetic analyses of 16S …

Class Discovery And Prediction Of Tumor With Microarray Data, Bo Liu

All Graduate Theses, Dissertations, and Other Capstone Projects

Current microarray technology is able take a single tissue sample to construct an Affymetrix oglionucleotide array containing (estimated) expression levels of thousands of different genes for that tissue. The objective is to develop a more systematic approach to cancer classification based on Affymetrix oglionucleotide microarrays. For this purpose, I studied published colon cancer microarray data. Colon cancer, with 655,000 deaths worldwide per year, has become the fourth most common form of cancer in the United States and the third leading cause of cancer - related death in the Western world. This research has been focuses in two areas: class discovery, …

Barcoding Of Arrow Worms (Phylum Chaetognatha) From Three Oceans: Genetic Diversity And Evolution Within An Enigmatic Phylum, Robert M. Jennings, Ann Bucklin, Annelies Pierrot-Bults

Biology Faculty Publication Series

Arrow worms (Phylum Chaetognatha) are abundant planktonic organisms and important predators in many food webs; yet, the classification and evolutionary relationships among chaetognath species remain poorly understood. A seemingly simple body plan is underlain by subtle variation in morphological details, obscuring the affinities of species within the phylum. Many species achieve near global distributions, spanning the same latitudinal bands in all ocean basins, while others present disjunct ranges, in some cases with the same species apparently found at both poles. To better understand how these complex evolutionary and geographic variables are reflected in the species makeup of chaetognaths, we analyze …

Development Of A Regional Habitat Classification Scheme For The Amirante Islands, Seychelles, Sarah Hamylton, Tom Spencer, Annelise Hagan

Faculty of Science - Papers (Archive)

A collaborative expedition between Khaled bin Sultan Living Oceans Foundation, Cambridge Coastal Research Unit and Seychelles Centre for Marine Research and Technology – Marine Parks Authority (SCMRT-MPA) was conducted to the southern Seychelles, western Indian Ocean, in January 2005. This resulted in a series of habitat maps of the reefs and reef islands of the Amirantes Archipelago, derived from remotely-sensed Compact Airborne Spectrographic Imager (CASI) data. The procedures used in map development, image processing techniques and field survey methods are outlined. Habitat classification, and regional-scale comparisons of relative habitat composition are described. The study demonstrates the use of remote sensing …