Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Synthesis, Characterisation And Biological Evaluation Of Tyramine Derived Schiff Base Ligand And Its Transition Metal(Ii) Complexes, Abdul Khader Jailani, N.S.K. Gowthaman, Mookkandi Palsamy Kesavan

Karbala International Journal of Modern Science

In this study, a new tyramine derived Schiff base ligand (L) (L=1,3-phenylene-bis-4-aminoantipyrinyl-4-aminoethylphenol) and its derived transition metal(II) complexes [Cu(L)Cl₂](1), [Ni(L)Cl₂](2), [Co(L)Cl₂] (3) and [Zn(L)Cl₂] (4) have been synthesized and well characterized by the way of different spectroscopic and analytical techniques. Analytical and spectroscopic studies result suggests that metal(II) complexes more probably have octahedral geometry. DNA binding tendency of L and metal(II) complexes 1-4 have been assessed by probing their ability to bind with Calf Thymus DNA (CT-DNA) via electronic absorption and cyclic voltammetry titration methods. The results clearly reveal that the metal(II) …

Variability In The Analysis Of A Single Neuroimaging Dataset By Many Teams, Rotem Botvinik-Nezer, Tom Schonberg, Russell A. Poldrack, Zachary J. Cole, Matthew R. Johnson, Phui Cheng Lim, Evan N. Linz, Douglas H. Schultz, Joshua E. Zosky, Narps Management Team, Jean M. Vettel, More Than 100 Other Co-Authors

Department of Psychology: Faculty Publications

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, …

A Review Of Major Role Of Vitamin D3 In Human Immune System And Its Possible Use For Novel Corona Virus Treatment, Florentin Smarandache, Victor Christianto

Branch Mathematics and Statistics Faculty and Staff Publications

the evidences showing major role of Vitamin D3 in human immune system and its potential use for novel corona virus treatment. Our argument is based on research finding that corona virus has viral envelope glycoproteins. In this regard, Vitamin D3 proves to offer various beneficial effects, including immunomodulatory effect, in order to break the glycoproteins envelope of the virus. One of the greatest benefit of vitamin D3 is the fact that it is easy to get 10,000 - 20,000 IU of daily intake requirement, by sunbathing for more or less twenty minutes. Such a method is likely applicable in many …

Finding Trends In Big City Health Issues With Data Visualization, Shridhar Kulkarni

Dissertations and Theses

In recent years, data visualization has become one of the most effective tools to understand and identify unseen features of the large datasets available. An open source data set available for health issues for big cities across the United States was obtained. There are numerous indicators presented in the dataset including Demographics, Chronic Health Diseases, Social and Economic Factors, Food Safety, Mortality Rates, Cancer and Life Expectancy Rates. The dataset encompassed myriad of demographics as well as specific data for a number of US cities. The data was explored in different methods in Data points in terms of the demographic …

The Effectiveness Of Transfer Learning Systems On Medical Images, James Boit

Masters Theses & Doctoral Dissertations

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned …

Artificial Intelligence: A New Paradigm In Obstetrics And Gynecology Research And Clinical Practice, Pulwasha Iftikhar, Marcela V. Kuijpers, Azadeh Khayyat, Aqsa Iftikhar, Maribel Degouvia De Sa

Publications and Research

Artificial intelligence (AI) is growing exponentially in various fields, including medicine. This paper reviews the pertinent aspects of AI in obstetrics and gynecology (OB/GYN) and how these can be applied to improve patient outcomes and reduce the healthcare costs and workload for clinicians.

Herein, we will address current AI uses in OB/GYN, and the use of AI as a tool to interpret fetal heart rate (FHR) and cardiotocography (CTG) to aid in the detection of preterm labor, pregnancy complications, and review discrepancies in its interpretation between clinicians to reduce maternal and infant morbidity and mortality. AI systems can be used …

Optimal Multi-Stage Arrhythmia Classification Approach, Jianwei Zhang, Huimin Chu, Daniele Struppa, Jianming Zhang, Sir Magdi Yacoub, Hesham El-Askary, Anthony Chang, Louis Ehwerhemuepha, Islam Abudayyeh, Alexander Barrett, Guohua Fu, Hai Yao, Dongbo Li, Hangyuan Guo, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead …

Color-Based Template Selection For Detection Of Gastric Abnormalities In Video Endoscopy, Hussam Ali, Muhammad Sharif, Mussarat Yasmin, Mubashir Husain Rehmani

Publications

Computer-aided diagnosis of gastric diseases from endoscopy frames is an important task. It facilitates both the patient and gastroenterologist in terms of time, money and most important health. Colors are the basic visual features of endoscopic images and also provide clues about abnormal regions in endoscopy frames. A variety of color spaces available for representation of color frames. However, we are not certain about which color space is more suitable for representing color features of gastric images. This paper presents a comparison of color features in different color spaces for detection of abnormal areas in chromoendoscopy (CH) frames. In addition, …

Robotically Steered Needles: A Survey Of Neurosurgical Applications And Technical Innovations, Michel A. Audette, Stéphane P.A. Bordas, Jason E. Blatt

Computational Modeling & Simulation Engineering Faculty Publications

This paper surveys both the clinical applications and main technical innovations related to steered needles, with an emphasis on neurosurgery. Technical innovations generally center on curvilinear robots that can adopt a complex path that circumvents critical structures and eloquent brain tissue. These advances include several needle-steering approaches, which consist of tip-based, lengthwise, base motion-driven, and tissue-centered steering strategies. This paper also describes foundational mathematical models for steering, where potential fields, nonholonomic bicycle-like models, spring models, and stochastic approaches are cited. In addition, practical path planning systems are also addressed, where we cite uncertainty modeling in path planning, intraoperative soft tissue …

Multi-Modal Medical Imaging Analysis With Modern Neural Networks, Gongbo Liang

Theses and Dissertations--Computer Science

Medical imaging is an important non-invasive tool for diagnostic and treatment purposes in medical practice. However, interpreting medical images is a time consuming and challenging task. Computer-aided diagnosis (CAD) tools have been used in clinical practice to assist medical practitioners in medical imaging analysis since the 1990s. Most of the current generation of CADs are built on conventional computer vision techniques, such as manually defined feature descriptors. Deep convolutional neural networks (CNNs) provide robust end-to-end methods that can automatically learn feature representations. CNNs are a promising building block of next-generation CADs. However, applying CNNs to medical imaging analysis tasks is …

A Short Remark On Multipurpose Laser Therapy “Helios” In Ukraine And Its Potential Application For Treatment Of Neurology Disorders, Florentin Smarandache, Volodymyr Krasnoholovets, Victor Christianto, Rizha Vitania, The Houw Liong

Branch Mathematics and Statistics Faculty and Staff Publications

However, a truly multipurpose laser therapy method is very rarely available. Here we introduce a multipurpose laser therapy device in Ukraine, which is capable to take care a multitude of diseases. It is called “Helios”, by one of us (VK). We also give a case where a patient who suffered from Covid-19 has been treated successfully until he is recovered to healthy condition. In the last section we also discuss potential future application of Helios for other fields; i.e. neurology disorders.

Quantifying The Varying Predictive Value Of Physical Activity Measures Obtained From Wearable Accelerometers On All-Cause Mortality Over Short To Medium Time Horizons In Nhanes 2003-2006, Lucia Tabacu, Mark Ledbetter, Andrew Leroux, Ciprian Crainiceanu, Ekaterina Smirnova

Mathematics & Statistics Faculty Publications

Physical activity measures derived from wearable accelerometers have been shown to be highly predictive of all-cause mortality. Prediction models based on traditional risk factors and accelerometry-derived physical activity measures are developed for five time horizons. The data set contains 2978 study participants between 50 and 85 years old with an average of 13.08 years of follow-up in the NHANES 2003–2004 and 2005–2006. Univariate and multivariate logistic regression models were fit separately for five datasets for one- to five-year all-cause mortality as outcome (number of events 46, 94, 155, 218, and 297, respectively). In univariate models the total activity count (TAC) …

Adaptive Background Correction Of Crystal Image Datasets: Towards Automated Process Control, Luke Kiernan, Ian Jones, Lauri Kurki, Patrick J. Cullen, Toufic El Arnaout

Articles

Improving the data descriptor calculation of crystal’s physical properties requires sophisticated imaging techniques and algorithms. It has been possible to construct 2D population balance models benefiting from characteristic measurements of both crystal’s length and width, compared to the single representative sizes used in 1D models. Our aim is to ameliorate the procedure of determining shape (and not only size) factors, in an automated fashion and directly from the process, for implementation in future models. Here, approaches suitable for real-time applications were employed including engineered imaging sensors and adaptive algorithms. We described the latter in detail for varying 2D image datasets. …

Mechanisms Of Value-Biased Prioritization In Fast Sensorimotor Decision Making, Kivilcim Afacan-Seref

Dissertations and Theses

In dynamic environments, split-second sensorimotor decisions must be prioritized according to potential payoffs to maximize overall rewards. The impact of relative value on deliberative perceptual judgments has been examined extensively, but relatively little is known about value-biasing mechanisms in the common situation where physical evidence is strong but the time to act is severely limited. This research examines the behavioral and electrophysiological indices of how value biases split-second perceptual decisions and the possible mechanisms underlying the process. In prominent decision models, a noisy but statistically stationary representation of sensory evidence is integrated over time to an action-triggering bound, and value-biases …

Electrospinning Piezoelectric Fibers For Biocompatible Devices, Bahareh Azimi, Mario Milazzo, Andrea Lazzeri, Stefano Berrettini, M. Jasim Uddin, Zhao Qin, Markus J. Buehler, Serena Danti

School of Integrative Biological & Chemical Sciences (Formerly Dept. of Chemistry)

The field of nanotechnology has been gaining great success due to its potential in developing new generations of nanoscale materials with unprecedented properties and enhanced biological responses. This is particularly exciting using nanofibers, as their mechanical and topographic characteristics can approach those found in naturally occurring biological materials. Electrospinning is a key technique to manufacture ultrafine fibers and fiber meshes with multifunctional features, such as piezoelectricity, to be available on a smaller length scale, thus comparable to subcellular scale, which makes their use increasingly appealing for biomedical applications. These include biocompatible fiber-based devices as smart scaffolds, biosensors, energy harvesters, and …

Connectivity Differences Between Gulf War Illness (Gwi) Phenotypes During A Test Of Attention, Tomas Clarke, Jessie Jamieson, Patrick Malone, Rakib U. Rayhan, Stuart Washington, John W. Vanmeter, James N. Baraniuk

Department of Mathematics: Faculty Publications

One quarter of veterans returning from the 1990–1991 Persian Gulf War have developed Gulf War Illness (GWI) with chronic pain, fatigue, cognitive and gastrointestinal dysfunction. Exertion leads to characteristic, delayed onset exacerbations that are not relieved by sleep. We have modeled exertional exhaustion by comparing magnetic resonance images from before and after submaximal exercise. One third of the 27 GWI participants had brain stem atrophy and developed postural tachycardia after exercise (START: Stress Test Activated Reversible Tachycardia). The remainder activated basal ganglia and anterior insulae during a cognitive task (STOPP: Stress Test Originated Phantom Perception). Here, the role of attention …

Image Restoration Using Automatic Damaged Regions Detection And Machine Learning-Based Inpainting Technique, Chloe Martin-King

Computational and Data Sciences (PhD) Dissertations

In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm facilitates precise mask creation, particularly useful for images containing damage that is tedious to annotate or difficult to geometrically define. The main contribution of this dissertation is the development and utilization of a new inpainting technique, region hiding, to repair a single image by training a convolutional neural network on various transformations of that image. Region hiding is also …

Artificial Intelligence And The Challenge For Rural Medicine, James Denvir

Marshall Journal of Medicine

Recent advances in artificial intelligence, machine learning, and deep learning are beginning to have an impact on everyday experiences, from natural language processing used in automated telephone call centers to semi-autonomous vehicles. These techniques have also been applied to medical care. In this editorial we discuss applications of AI to medicine and argue for a proactive approach to include rural medicine in this paradigm shift.

I Spy With My Little Eye … A Knee About To Go 'Pop'? Can Coaches And Sports Medicine Professionals Predict Who Is At Greater Risk Of Acl Rupture?, Anne Inger Mørtvedt, Tron Krosshaug, Roald Bahr, Erich Petushek

Michigan Tech Publications, Part 1

BACKGROUND: The vertical drop jump (VDJ) test is widely used for clinical assessment of ACL injury risk, but it is not clear whether such assessments are valid.

AIM: To examine if sports medicine professionals and coaches are able to identify players at risk of sustaining an ACL injury by visually assessing player performance during a VDJ test.

METHODS: 102 video clips of elite female handball and football players performing a baseline VDJ test were randomly extracted from a 738-person prospective cohort study that tracked ACL injuries. Of the sample, 20 of 102 went on to suffer an ACL injury. These …

Development Of A Screening Method For Drugs Of Abuse By Direct Analysis Of Dried Urine Spots Coupled To Mass Spectrometry, Melanie Goldstein

Student Theses

Fast and easy screening procedures are essential in any forensic toxicology laboratory to differentiate negative samples from presumptive positive cases. Immunoassay techniques, such as enzyme multiplied immunoassay technique (EMIT), are routinely employed as screening procedures. However, these techniques lack specificity (only differentiate group of drugs and not individual compounds) and it is difficult to add new compounds to the panel. Direct analysis of dried urine spots (DUS) by mass spectrometry (MS) offers a novel strategy to overcome these issues. DUS offer an improved storage alternative for biological samples, reducing costs and space requirements. In this work, an original method to …

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

In Vitro Evaluation Of Ovarian Cancer Tumorigenesis As A Function Of Quinone Oxidoreducatse-1 And Cell Phenotype, Milcah S. Jackson

LSU Doctoral Dissertations

In vitro multicellular spheroids are attractive model systems for assessing genetic and epigenetic changes that occur in diseased tissues. Understanding how such alterations in gene and subsequent protein expression affect disease progression and metastasis, drug resistance, and recurrence is of great interest in cancer research. In this regard, examining expression and activity of proteins, such as those with cytoprotective ability that are overexpressed in cancer cells, in addition to cell phenotype (i.e., stem-like, epithelial, mesenchymal, or mixed), are two ways to evaluate genetic and epigenetic changes. Moreover, determining the impact that cytoprotective proteins and cell phenotype have on tumor formation …

Crystal Structure Of Zymonic Acid And A Redetermination Of Its Precursor, Pyruvic Acid, Dominik Heger, Alexis J. Eugene, Sean R. Parkin, Marcelo I. Guzman

Chemistry Faculty Publications

The structure of zymonic acid (systematic name: 4-hy­droxy-2-methyl-5-oxo-2,5-di­hydro­furan-2-carb­oxy­lic acid), C₆H₆O₅, which had previously eluded crystallographic determination, is presented here for the first time. It forms by intra­molecular condensation of parapyruvic acid, which is the product of aldol condensation of pyruvic acid. A redetermination of the crystal structure of pyruvic acid (systematic name: 2-oxo­propanoic acid), C₃H₄O₃, at low temperature (90 K) and with increased precision, is also presented [for the previous structure, see: Harata et al. (1977). Acta Cryst. B33, 210–212]. In zymonic acid, the hy­droxy­lactone ring …

Differential Estimation Of Audiograms Using Gaussian Process Active Model Selection, Trevor Larsen

McKelvey School of Engineering Graduate Student Theses & Dissertations

Classical methods for psychometric function estimation either require excessive resources to perform, as in the method of constants, or produce only a low resolution approximation of the target psychometric function, as in adaptive staircase or up-down procedures. This thesis makes two primary contributions to the estimation of the audiogram, a clinically relevant psychometric function estimated by querying a patient’s for audibility of a collection of tones. First, it covers the implementation of a Gaussian process model for learning an audiogram using another audiogram as a prior belief to speed up the learning procedure. Second, it implements a use case of …

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …

Quantifying Iron Overload Using Mri, Active Contours, And Convolutional Neural Networks, Andrea Sajewski, Stacey Levine

Undergraduate Research and Scholarship Symposium

Iron overload, a complication of repeated blood transfusions, can cause tissue damage and organ failure. The body has no regulatory mechanism to excrete excess iron, so iron overload must be closely monitored to guide therapy and measure treatment response. The concentration of iron in the liver is a reliable marker for total body iron content and is now measured noninvasively with magnetic resonance imaging (MRI). MRI produces a diagnostic image by measuring the signals emitted from the body in the presence of a constant magnetic field and radiofrequency pulses. At each pixel, the signal decay constant, T2*, can be calculated, …

Measuring Birth Trauma Rates In Maine Using Public Data, Mike Lapika

Thinking Matters Symposium Archive

An increasing number of states are creating databases that collect and organize health insurance claims from public and private health care payers. Since December 2016, at least 18 states have these “all-payer claims databases” (APCDs), including Maine. APCDs are intended to inform cost containment and quality improvement by increasing transparency and informing consumer choice. For this project, we assessed how Maine’s APCD data might be used to produce standardized quality measures across facilities in the state. Specifically, we tested a birth outcome quality measure developed by the Agency for Healthcare Research and Quality (AHRQ), Birth Trauma – Injury to Neonate …

Estimating Variations In Metabolic Cost Within The Stride Cycle During Level And Uphill Walking, Arash Mohammadzadeh Gonabadi

UNO Student Research and Creative Activity Fair

Indirect calorimetry provides the average cost of a stride cycle and prevents from identifying which part of the gait cycle causes increased metabolic cost in patients, however, recent simulation methods allow estimating the time profile of metabolic cost within the stride cycle. In this study, we compare the estimations of the time profile of the metabolic cost of two simulation methods for level and uphill walking. We used kinematic, kinetic and electromyography data from level and uphill walking (one participant) to estimate the time profiles of metabolic cost using the muscle-level metabolic model of Umberger using electromyography and kinematic data …

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead

Engineering Faculty Articles and Research

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …

Healthcare Robotics: Key Factors That Impact Robot Adoption In Healthcare, Sujatha Alla, Pilar Pazos

Engineering Management & Systems Engineering Faculty Publications

In the current dynamic business environment, healthcare organizations are focused on improving patient satisfaction, performance, and efficiency. The healthcare industry is considered a complex system that is highly reliant of new technologies to support clinical as well as business processes. Robotics is one of such technologies that is considered to have the potential to increase efficiency in a wide range of clinical services. Although the use of robotics in healthcare is at the early stages of adoption, some studies have shown the capacity of this technology to improve precision, accessibility through less invasive procedures, and reduction of human error during …