Computer Sciences Commons^™

Low Pitch Significantly Reduces Helical Artifacts In Abdominal Ct, Moiz Ahmad, Peng Sun, Christine B Peterson, Marcus R Anderson, Xinming Liu, Ajaykumar C Morani, Corey T Jensen

Faculty, Staff and Student Publications

Purpose: High helical pitch scanning minimizes scan times in CT imaging, and thus also minimizes motion artifact and mis-synchronization with contrast bolus. However, high pitch produces helical artifacts that may adversely affect diagnostic image quality. This study aims to determine the severity and incidence of helical artifacts in abdominal CT imaging and their relation to the helical pitch scan parameter.

Methods: To obtain a dataset with varying pitch values, we used CT exam data both internal and external to our center. A cohort of 59 consecutive adult patients receiving an abdomen CT examination at our center with an accompanying prior …

Quantifying Balance: Computational And Learning Frameworks For The Characterization Of Balance In Bipedal Systems, Kubra Akbas

Dissertations

In clinical practice and general healthcare settings, the lack of reliable and objective balance and stability assessment metrics hinders the tracking of patient performance progression during rehabilitation; the assessment of bipedal balance plays a crucial role in understanding stability and falls in humans and other bipeds, while providing clinicians important information regarding rehabilitation outcomes. Bipedal balance has often been examined through kinematic or kinetic quantities, such as the Zero Moment Point and Center of Pressure; however, analyzing balance specifically through the body's Center of Mass (COM) state offers a holistic and easily comprehensible view of balance and stability.

Building upon …

Reconstructing 42 Years (1979–2020) Of Great Lakes Surface Temperature Through A Deep Learning Approach, Miraj Kayastha, Tao Liu, Daniel Titze, Timothy C. Havens, Chenfu Huang, Pengfei Xue

Michigan Tech Publications

Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (<30 >years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at …

Sctiger: A Deep-Learning Method For Inferring Gene Regulatory Networks From Case Versus Control Scrna-Seq Datasets., Madison Dautle, Shaoqiang Zhang, Yong Chen

College of Science & Mathematics Departmental Research

Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among …

Online Data Transmission Reduction Scheme For Energy Conservation In Wireless Video Sensor Networks, Iman Kadhum Abbood, Ali Kadhum Idrees

Karbala International Journal of Modern Science

Wireless Video Sensor Networks (WVSNs) are networks of low-cost, low-power camera sensor nodes. These nodes communicate locally and process information to meet an application's goal. WVSNs are extensively used in diverse monitoring applications, such as security, military, industrial, medical, and environmental monitoring. However, the transmission of large amounts of data collected by video sensor nodes in WVSNs poses challenges in terms of energy consumption, bandwidth usage, and network congestion. Reducing energy for processing and transmitting data in WVSNs is difficult due to the huge amount of sensed data in real-time. To address this issue, this paper proposes an Online Data …

Biogenesis Synthesis Of Zno Nps: Its Adsorption And Photocatalytic Activity For Removal Of Acid Black 210 Dye, Zahraa A. Najm, Mohammed A. Atiya, Ahmed K. Hassan

Karbala International Journal of Modern Science

This study investigated the treatment of textile wastewater contaminated with Acid Black 210 dye (AB210) using zinc oxide nanoparticles (ZnO NPs) through adsorption and photocatalytic techniques. ZnO NPs were synthesized using a green synthesis process involving eucalyptus leaves as reducing and capping agents. The synthesized ZnO NPs were characterized using UV-Vis spectroscopy, SEM, EDAX, XRD, BET, Zeta potential, and FTIR techniques. The BET analysis revealed a specific surface area and total pore volume of 26.318 m²/g. SEM images confirmed the crystalline and spherical nature of the particles, with a particle size of 73.4 nm. A photoreactor was designed …

Ultrasound Assisted Comparative Study Of Fucolam And So-Dium Alginate And Impact On Their Physiochemical Proper-Ties Using Box-Behnken Design, Uday Bagale, Ammar Kadi, Artem Malinin, Varisha Anjum, Irina Potoroko

Karbala International Journal of Modern Science

The article discusses about the possibility of comparing the impact of ultrasonic treatment on fucolam and sodium algi-nate. The purpose was to study the effect of micronization on the sulfated heteropolysaccharide fucolam, analyzing its dispersed state and accessibility by reducing its molecular weight and increasing antioxidant activity. The optimization of the micronization process was carried out using the Box-Behnken Design (BBD) method, with a sonication time ranging from 15 to 45 min, power ranging from 50 to 100 W/cm2, and temperature between 30 °C and 40 °C. The fixed lower fucolam concentration was 0.1%. The results illustrated that sonochemical treatment …

Deep Learning-Based Cad System For Predicting The Covid-19 X-Ray Images, Aqeel R. Talib, Hana’ M. Ali

Karbala International Journal of Modern Science

According to World Health Organization data, Coronavirus (COVID-19) has infected about 660, 378, 145 patients around the world. It is nonetheless difficult for physicians to detect COVID-19 infections out of CT or X-ray radiographs. Thus, several computer-aided diagnosis (CAD) systems based on deep learning and radiographs were developed to detect COVID-19 infections. However, the majority of approaches considered small datasets, which is ineligible to provide diverse COVID-19 radiographs. This work utilizes a massive number of X-ray radiographs, and compared standard CNN, DenseNet-121, and GoogLeNet for isolating COVID-19 infections out from normal and other pneumonia radiographs. The dataset in this work …

Self-Supervised Pretraining And Transfer Learning On Fmri Data With Transformers, Sean Paulsen

Dartmouth College Ph.D Dissertations

Transfer learning is a machine learning technique founded on the idea that knowledge acquired by a model during “pretraining” on a source task can be transferred to the learning of a target task. Successful transfer learning can result in improved performance, faster convergence, and reduced demand for data. This technique is particularly desirable for the task of brain decoding in the domain of functional magnetic resonance imaging (fMRI), wherein even the most modern machine learning methods can struggle to decode labelled features of brain images. This challenge is due to the highly complex underlying signal, physical and neurological differences between …

Representing Quantum Spins In Different Coordinate Systems For Modelling Rigid Body Orientation, Nadjet Zioui, Aicha Mahmoudi, Mohamed Tadjine

Karbala International Journal of Modern Science

Various methods for representing the spatial orientation and rotation of objects are presented and compared with the quantum bit state representation. By contrasting spherical, Euler angle, quaternion, and quantum spin coordinate systems, this work highlights important concepts regarding the rotation axis of the X gate. Several ambiguities and incomplete definitions associated with the qubit state representation are discussed, such as the spin around the qubit itself and the explanation of the considered rotation angles and signs. A mathematical analysis of the physical meaning of each eigenstate is provided along with a new comprehensive and meaningful YPR-based 3D representation of a …

Quercetin As An Anticancer Candidate For Glioblastoma Multiforme By Targeting Akt1, Mmp9, Abcb1, And Vegfa: An In Silico Study, Muhammad Hermawan Widyananda, Setyaki Kevin Pratama, Arif Nur Muhammad Ansori, Yulanda Antonius, Viol Dhea Kharisma, Ahmad Affan Ali Murtadlo, Vikash Jakhmola, Maksim Rebezov, Mars Khayrullin, Marina Derkho, Emdad Ullah, Raden Joko Kuncoroningrat Susilo, Suhailah Hayaza, Alexander Patera Nugraha, Annise Proboningrat, Amaq Fadholly, Mada Triandala Sibero, Rahadian Zainul

Karbala International Journal of Modern Science

Quercetin, a natural compound present in various fruits and vegetables, shows promise as a potential inhibitor for glioblastoma multiforme (GBM) development. This study aims to examine the anti-GBM potential of Quercetin. The protein target of Quercetin is identified and analyzed using databases such as NCBI, SEA, CTD, and STRING. Protein-protein interaction (PPI) and functional annotation are carried out based on the obtained target proteins. Molecular docking and dynamics simulations are employed using AutoDock Vina and WebGro tools to analyze the interaction between Quercetin and its target proteins. The prediction of protein targets reveals that Quercetin directly targets four proteins associated …

Improving The Burt’S Sensitivity Using Noise Calibration Unit Via Crab Nebula Observations, Uday E. Jallod, Lana T. Ali, Hareth S. Mahdi, Kamal M. Abood

Karbala International Journal of Modern Science

Radio observations from astronomical sources like supernovae became one the most important sources of information about the physical properties of those objects. However, such radio observations are affected by various types of noise such as those from sky, background, receiver, and the system itself. Therefore, it is essential to eliminate or reduce these undesired noise from the signals in order to ensure accurate measurements and analysis of radio observations. One of the most commonly used methods for reducing the noise is to use a noise calibrator. In this study, the 3-m Baghdad University Radio Telescope (BURT) has been used to …

Activation Of The Renin–Angiotensin–Aldosterone System Is Attenuated In Hypertensive Compared With Normotensive Pregnancy, Robin C. Shoemaker, Marko Poglitsch, Hong Huang, Katherine Vignes, Aarthi Srinivasan, Cynthia Cockerham-Morris, Aric Schadler, John Anthony Bauer, John O'Brien

UK CARES Faculty Publications

Hypertension during pregnancy increases the risk of adverse maternal and fetal outcomes, but the mechanisms of pregnancy hypertension are not precisely understood. Elevated plasma renin activity and aldosterone concentrations play an important role in the normal physiologic adaptation to pregnancy. These effectors are reduced in patients with pregnancy hypertension, creating an opportunity to define the features of the renin–angiotensin–aldosterone system (RAAS) that are characteristic of this disorder. In the current study, we used a novel LC-MS/MS-based methodology to develop comprehensive profiles of RAAS peptides and effectors over gestation in a cohort of 74 pregnant women followed prospectively for the development …

Visual Complexity Of The Time-Frequency Image Pinpoints The Epileptogenic Zone: An Unsupervised Deep-Learning Tool To Analyze Interictal Intracranial Eeg, Sarvagya Gupta

Graduate Masters Theses

Epilepsy, a prevalent neurological disorder characterized by recurrent seizures, continues to pose significant challenges in diagnosis and treatment, particularly among children. Despite substantial advancements in medical technology and treatment modalities, localization of the part of brain that causes seizures (Epileptogenic Zone) remains a difficult task. Intracranial EEG (iEEG) is often used to estimate the epileptogenic zone (EZ) in children with drugresistant epilepsy (DRE) and target it during surgery. Conventionally, iEEG signals are inspected in the time domain by human experts aiming to locate epileptiform activity.

Visual scrutiny of the iEEG time-frequency (TF) images can be an alternative way to review …

Structures Of The Insecticidal Toxin Complex Subunit Xpta2 Highlight Roles For Flexible Domains, Cole L. Martin, David W. Chester, Christopher D. Radka, Lurong Pan, Zhengrong Yang, Rachel C. Hart, Elad M. Binshtein, Zhao Wang, Lisa Nagy, Lawrence J. Delucas, Stephen G. Aller

Markey Cancer Center Faculty Publications

The Toxin Complex (Tc) superfamily consists of toxin translocases that contribute to the targeting, delivery, and cytotoxicity of certain pathogenic Gram-negative bacteria. Membrane receptor targeting is driven by the A-subunit (TcA), which comprises IgG-like receptor binding domains (RBDs) at the surface. To better understand XptA2, an insect specific TcA secreted by the symbiont X. nematophilus from the intestine of entomopathogenic nematodes, we determined structures by X-ray crystallography and cryo-EM. Contrary to a previous report, XptA2 is pentameric. RBD-B exhibits an indentation from crystal packing that indicates loose association with the shell and a hotspot for possible receptor binding or a …

Generative Pre-Trained Transformers (Gpt) And Space Health: A Potential Frontier In Astronaut Health During Exploration Missions, Ethan Waisberg, Joshua Ong, Mouayad Masalkhi, Nasif Zaman, Sharif Amit Kamran, Prithul Sarker, Andrew G Lee, Alireza Tavakkoli

Faculty, Staff and Student Publications

In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health.

The Role Of The Family In Confronting The Excessive Use Of Modern Technology Among Children "Therapeutic Alternatives", Khaled Mikhlif Al-Jenfawi

Journal of Police and Legal Sciences

This study aimed to identify the role of the family in confronting the excessive use of technology and social media programs from the view point of social workers and psychologists working for the Juvenile Welfare Department of the Ministry of Social Affairs and Labor in Kuwait, in the light of some variables (sex , and practical experience)

The studywas a descriptive analytical study. It used the social survey method. A questionnaire consisting of (39) items was built and designed, and its validity and reliability were tested. Among the most important results of the study: The level of the family's role …

Watermark Hiding In Hdr Image Based On Visual Saliency And Tucker Decomposition, Roa'a M. Al-Airaji, Ibtisam A. Aljazaery, Haider Th. Salim Alrikabi

Karbala International Journal of Modern Science

Recently, great attention has been paid to high dynamic range (HDR) images because of their richly detailed and high dynamic range of intensity. The need for pre-processing of the HDR image format with tone mapping (TM) operators makes it unique; the TM operators are provided on display with a low dynamic range (LDR). On the other hand, TM can be regarded as an inevitable attack when protecting HDR image ownership is considered. An adaptive approach for concealing watermarks based on visual saliency and Tucker decomposition has been presented in this article. In the first step, three feature maps were produced …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

The Metabolomics Workbench File Status Website: A Metadata Repository Promoting Fair Principles Of Metabolomics Data, Christian D. Powell, Hunter N. B. Moseley

Markey Cancer Center Faculty Publications

Background: An updated version of the mwtab Python package for programmatic access to the Metabolomics Workbench (MetabolomicsWB) data repository was released at the beginning of 2021. Along with updating the package to match the changes to MetabolomicsWB’s ‘mwTab’ file format specification and enhancing the package’s functionality, the included validation facilities were used to detect and catalog file inconsistencies and errors across all publicly available datasets in MetabolomicsWB.

Results: The MetabolomicsWB File Status website was developed to provide continuous validation of MetabolomicsWB data files and a useful interface to all found inconsistencies and errors. This list of detectable issues/errors include format …

Application Of Agile And Simulation Approaches For The Maximal Benefits Of Reduced Turnaround Time From The Point Of Care Testing, Yusta W. Simwita, Berit I. Helgheim

Tanzania Journal of Science

This paper uses simulation to explore improvement opportunities in the orthopaedic care process. Secondly, this study used a combination of simulation and agile strategies to explore agile operational plans that can be adopted  to maximize the benefits of reduced turnaround time. Data used for this study was collected by observing the entire orthopaedic care process. A total of 635 observations were obtained, with 179 out of these observations undergoing the whole care process. The actual collected patient data were compared with the simulation output to validate the developed simulation. Different scenarios were developed to test operational plans for the maximal …

Development And Issues Of Biotech Seed Industry In China, Peijuan Chi, Hualing Xie, Ping Zhao, Fang Chen, Ning Wu, Zhixi Tian, Weicai Yang, Yanping Yang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Biotech seed industry is a strategic core industry. Biotechnology combined with digital technology has promoted the seed industry into an intelligent era, and the breeding paradigm has changed from “experimental selection” to “computational selection”. Biotech seed industry has become a research and development intensive industry, and the market is highly concentrated, which is controlled by large multinational enterprises. The scientific and technological output of China and the United States is in the first echelon, and the number of papers and authorized patents ranks among the top two in the world. From the perspective of core competitiveness, the United States is …

Attention Visual, Baris Dingil

College of Computing and Digital Media Dissertations

This research presents an innovative approach to improving visual-spatial attention using a research tool based on the web. Recognizing the significant role visual-spatial attention plays in everyday life and cognitive function for humans, this research was undertaken with the aim of developing a user-friendly, accessible web-based tool called Attention Visual (attentionvisual.com) to enhance this crucial cognitive skill. This tool also facilitates data collection, potentially accelerating the pace and enhancing the quality of related research. Both qualitative and quantitative methods were utilized for data collection and analysis. In order to stimulate improvements in visual-spatial attention, the tool’s algorithm was structured to …

Utilizing Few-Shot Meta Learning Algorithms For Medical Image Segmentation, Nick Littlefield

Thinking Matters Symposium

Deep learning models can be difficult to train because they require large amounts of data, which we usually do not have or are too expensive to get or annotate. To overcome this problem, we can use few-shot meta-learning, which allows us to train deep learning models with little data. Using a few examples, meta-learning, or learning-to-learn, aims to use the experience learned during training to generalize to unknown tasks. Medical imaging is an industry where it is particularly useful, as there is limited publicly available data due to patient privacy concerns and annotating costs.

This project examines how meta-learning performs …

A New Method Using Deep Transfer Learning On Ecg To Predict The Response To Cardiac Resynchronization Therapy, Zhuo He, Hongjin Si, Xinwei Zhang, Qing-Hui Chen, Jiangang Zou, Weihua Zhou

Michigan Tech Publications

Background: Cardiac resynchronization therapy (CRT) has emerged as an effective treatment for heart failure patients with electrical dyssynchrony. However, accurately predicting which patients will respond to CRT remains a challenge. This study explores the application of deep transfer learning techniques to train a predictive model for CRT response. Methods: In this study, the short-time Fourier transform (STFT) technique was employed to transform ECG signals into two-dimensional images. A transfer learning approach was then applied on the MIT-BIT ECG database to pre-train a convolutional neural network (CNN) model. The model was fine-tuned to extract relevant features from the ECG images, and …

Predicting Location And Training Effectiveness (Plate), Erik Rolf Bruenner

Master's Theses

Abstract Predicting Location and Training Effectiveness (PLATE)
Erik Bruenner

Physical activity and exercise have been shown to have an enormous impact on many areas of human health and can reduce the risk of many chronic diseases. In order to better understand how exercise may affect the body, current kinesiology studies are designed to track human movements over large intervals of time. Procedures used in these studies provide a way for researchers to quantify an individual’s activity level over time, along with tracking various types of activities that individuals may engage in. Movement data of research subjects is often collected through …

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Neural Tabula Rasa: Foundations For Realistic Memories And Learning, Patrick R. Perrine

Master's Theses

Understanding how neural systems perform memorization and inductive learning tasks are of key interest in the field of computational neuroscience. Similarly, inductive learning tasks are the focus within the field of machine learning, which has seen rapid growth and innovation utilizing feedforward neural networks. However, there have also been concerns regarding the precipitous nature of such efforts, specifically in the area of deep learning. As a result, we revisit the foundation of the artificial neural network to better incorporate current knowledge of the brain from computational neuroscience. More specifically, a random graph was chosen to model a neural system. This …

Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young

Electronic Theses and Dissertations

While marker-based motion capture remains the gold standard in measuring human movement, accuracy is influenced by soft-tissue artifacts, particularly for subjects with high body mass index (BMI) where markers are not placed close to the underlying bone. Obesity influences joint loads and motion patterns, and BMI may not be sufficient to capture the distribution of a subject’s weight or to differentiate differences between subjects. Subjects in need of a joint replacement are more likely to have mobility issues or pain, which prevents exercise. Obesity also increases the likelihood of needing a total joint replacement. Accurate movement data for subjects with …

Machine Learning And Network Embedding Methods For Gene Co-Expression Networks, Niloofar Aghaieabiane

Dissertations

High-throughput technologies such as DNA microarrays and RNA-seq are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed into Gene Co-expression Networks (GCNs). GCNs are analyzed to discover gene modules. GCN construction and analysis is a well-studied topic, for nearly two decades. While new types of sequencing and the corresponding data are now available, the software package WGCNA and its most recent variants are still widely used, contributing to biological discovery.

The discovery of biologically significant modules of genes from raw expression data is …

Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi

Dissertations

Mechanistic modeling and machine learning methods are powerful techniques for approximating biological systems and making accurate predictions from data. However, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. This dissertation constructs Deep Hybrid Models that address these shortcomings by combining deep learning with mechanistic modeling. In particular, this dissertation uses Generative Adversarial Networks (GANs) to provide an inverse mapping of data to mechanistic models and identifies the distributions of mechanistic model parameters coherent to the data.

Chapter 1 provides background information on …