Medicine and Health Sciences Commons^™

Length Of Stay In A Homeless Shelter And Mitigating Homelessness, Uwemedimo S. Etteyit

Walden Dissertations and Doctoral Studies

AbstractHomelessness is a major public health issue in the United States. Every night, thousands of people have no residence to call their own. Most homeless persons turn to homeless shelters for help. Despite the homeless shelters, the problem of homelessness persists. This study examined the concept that the length of time spent at a homeless shelter is related to the homeless persons mitigating their homelessness through home placement, jobs, and healthcare access. Homelessness was examined using the socioecological model with its attendant levels of influence. On the intrapersonal level, socioeconomic status, education, old age, veteran status, and disability were factors. …

Eye Movement And Pupil Measures: A Review, Bhanuka Mahanama, Yasith Jayawardana, Sundararaman Rengarajan, Gavindya Jayawardena, Leanne Chukoskie, Joseph Snider, Sampath Jayarathna

Computer Science Faculty Publications

Our subjective visual experiences involve complex interaction between our eyes, our brain, and the surrounding world. It gives us the sense of sight, color, stereopsis, distance, pattern recognition, motor coordination, and more. The increasing ubiquity of gaze-aware technology brings with it the ability to track gaze and pupil measures with varying degrees of fidelity. With this in mind, a review that considers the various gaze measures becomes increasingly relevant, especially considering our ability to make sense of these signals given different spatio-temporal sampling capacities. In this paper, we selectively review prior work on eye movements and pupil measures. We first …

Completing Single-Cell Dna Methylome Profiles Via Transfer Learning Together With Kl-Divergence, Sanjeeva Dodlapati, Zongliang Jiang, Jiangwen Sun

Computer Science Faculty Publications

The high level of sparsity in methylome profiles obtained using whole-genome bisulfite sequencing in the case of low biological material amount limits its value in the study of systems in which large samples are difficult to assemble, such as mammalian preimplantation embryonic development. The recently developed computational methods for addressing the sparsity by imputing missing have their limits when the required minimum data coverage or profiles of the same tissue in other modalities are not available. In this study, we explored the use of transfer learning together with Kullback-Leibler (KL) divergence to train predictive models for completing methylome profiles with …

Refinement Of Alphafold2 Models Against Experimental And Hybrid Cryo-Em Density Maps, Maytha Alshammari, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Recent breakthroughs in deep learning-based protein structure prediction show that it is possible to obtain highly accurate models for a wide range of difficult protein targets for which only the amino acid sequence is known. The availability of accurately predicted models from sequences can potentially revolutionise many modelling approaches in structural biology, including the interpretation of cryo-EM density maps. Although atomic structures can be readily solved from cryo-EM maps of better than 4 Å resolution, it is still challenging to determine accurate models from lower-resolution density maps. Here, we report on the benefits of models predicted by AlphaFold2 (the best-performing …

Loss Of Acta2 In Cardiac Fibroblasts Does Not Prevent The Myofibroblast Differentiation Or Affect The Cardiac Repair After Myocardial Infarction, Yuxia Li, Chaoyang Li, Qianglin Liu, Leshan Wang, Adam X. Bao, Jangwook P. Jung, Sanjeev Dodlapati, Jingwen Sun, Peidong Gao, Xujia Zhang, Joseph Francis, Jeffery D. Molkentin, Xing Fu

Computer Science Faculty Publications

In response to myocardial infarction (MI), quiescent cardiac fibroblasts differentiate into myofibroblasts mediating tissue repair. One of the most widely accepted markers of myofibroblast differentiation is the expression of Acta2 which encodes smooth muscle alpha-actin (SMαA) that is assembled into stress fibers. However, the requirement of Acta2/SMαA in the myofibroblast differentiation of cardiac fibroblasts and its role in post-MI cardiac repair remained unknown. To answer these questions, we generated a tamoxifen-inducible cardiac fibroblast-specific Acta2 knockout mouse line. Surprisingly, mice that lacked Acta2 in cardiac fibroblasts had a normal post-MI survival rate. Moreover, Acta2 deletion did …

Development Of A New Spe Uplc–Ms/Ms Method For Extraction And Quantitation Of Toluene Diamine On Gloves Following Toluene Diisocyanate Exposure, Asma Daoud, Pierre-Luc Cloutier, Sébastien Gagné, Livain Breau, Jacques Lesage

Études primaires

Rationale

Toluene diisocyanate (TDI) is a highly reactive isocyanate commonly used as a mixture of 2,4- and 2,6- isomers in the production of flexible foams. Exposure to TDI occurs primarily through vapour inhalation in workplaces where TDI is produced or used, but dermal exposure is also possible during some tasks. To ensure workplace safety, accurate monitoring of TDI and toluene diamine (TDA) levels is required. Methods of quantifying field effectiveness of gloves in preventing dermal exposure have not been established. Therefore, there is a need to develop a new practical method for assessing glove effectiveness for TDI/TDA.

Method

A new …

Morphologic Clustering Of Earcanals Using Deep Learning Algorithm To Design Artificial Ears Dedicated To Earplugs Attenuation Measurement, Bastien Poissenot-Arrigoni, Chun Hong Law, Djamal Berbiche, Franck Sgard, Olivier Doutres

Études primaires

Designing earplugs adapted for the widest number of earcanals requires acoustical test fixtures (ATFs) geometrically representative of the population. Most existing ATFs are equipped with unique sized straight cylindrical earcanals, considered representative of average human morphology, and are therefore unable to assess how earplugs can fit different earcanal morphologies. In this study, a methodology to cluster earcanals as a function of their morphologies with the objective of designing artificial ears dedicated to sound attenuation measurement is developed and applied to a sample of Canadian workers’ earcanals. The earcanal morphologic indicators that correlate with the attenuations of six models of commercial …

Exploring Factors That Predict Marketing Of E-Cigarette Products On Twitter: Infodemiology Approach Using Time Series, Nnamdi C. Ezike, Allison Ames Boykin, Page D. Dobbs, Huy Mai, Brian A. Primack

Health, Human Performance and Recreation Faculty Publications and Presentations

Background:

Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use.

Objective:

This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques.

Methods:

We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected …

An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan-Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong-Wen Deng, Chaoyang Zhang

Faculty, Staff and Student Publications

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a …

It Permeated Everything: A Lived Experience Of Slow Violence And Toxicological Disaster, Tara Jo Holmberg

Antioch University Dissertations & Theses

Impacts of disasters on individuals are dependent on numerous factors: local to international political dynamics, socioeconomics, geography, educational background, and outside support among others. Currently, much of disaster research focuses on those of natural origin, acute and large-scale environmental events, emergency management, and the ability of individuals, communities, and societies to prepare for, and recover from, likely known disasters in their region. However, there is a lack of data about individual experiences through ‘invisible’ anthropogenic disasters, especially those that fall under the umbrella of slow environmental violence (Davies, 2019; Rice, 2016). Through critical phenomenological autoethnography, I examine an individual experience …

Frequent Mental Distress Among Adults In The United States And Its Association With Socio-Demographic Characteristics, Unhealthy Lifestyle, And Chronic Physical Health Status, Mamunur Rashid, M. Mazharul Islam, Aiping Li, Naima Shifa

Mathematics Faculty Publications

Frequent mental distress (FMD) is a measure of poor mental health days for at least 14 days out of 30 days. It is one of the important dimensions of the health-related quality of life. The underlying causes of FMD are diverse. However, the issue has not been explored extensively due to the lack of reliable data on mental health. The aim of this study was to examine the level and trends of FMD among the adults of the United States (US) and identify the socio-demographic, lifestyles, and chronic health outcomes related correlates of FMD. The data for the study was …

Peripherally Restricted Opioid Conjugates And Its Use As Pharmacological Probes And Potential Therapeutics, Md Tariqul Haque Tuhin

University of the Pacific Theses and Dissertations

Opioid-induced constipation (OIC) is one of the major adverse effects of opioid analgesics used by millions of patients each year. While progress has been made, there remains a significant unmet medical need in the treatment of OIC. Major gaps remain in our understanding of the role of the gastrointestinal tract and central nervous system (CNS) in precipitating OIC. For the last four decades, numerous investigations to study the sites of action of opioid analgesics have utilized peripherally acting mu-opioid receptor antagonists (PAMORAs), which have been incorrectly believed to have limited penetration across the blood-brain barrier (BBB). Several preclinical and clinical …

Application Of Artificial Intelligence To Plasma Metabolomics Profiles To Predict Response To Neoadjuvant Chemotherapy In Triple-Negative Breast Cancer, Ehsan Irajizad, Ranran Wu, Jody Vykoukal, Eunice Murage, Rachelle Spencer, Jennifer B Dennison, Stacy Moulder, Elizabeth Ravenberg, Bora Lim, Jennifer Litton, Debu Tripathym, Vicente Valero, Senthil Damodaran, Gaiane M Rauch, Beatriz Adrada, Rosalind Candelaria, Jason B White, Abenaa Brewster, Banu Arun, James P Long, Kim Anh Do, Sam Hanash, Johannes F Fahrmann

Faculty, Staff and Student Publications

There is a need to identify biomarkers predictive of response to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). We previously obtained evidence that a polyamine signature in the blood is associated with TNBC development and progression. In this study, we evaluated whether plasma polyamines and other metabolites may identify TNBC patients who are less likely to respond to NACT. Pre-treatment plasma levels of acetylated polyamines were elevated in TNBC patients that had moderate to extensive tumor burden (RCB-II/III) following NACT compared to those that achieved a complete pathological response (pCR/RCB-0) or had minimal residual disease (RCB-I). We further applied …

The Vagabond Flourine Atom Revisited: Dissociative Photoionization Of Tri- And Pentafluoropropene, Jessica K. De La Cruz

University of the Pacific Theses and Dissertations

Photoelectron Photoion Coincidence (PEPICO) Spectroscopy studies on two unsaturated hydrofluorocarbons (HFCs), also known as hydrofluoroolefines (HFOs), are presented here. Previously, the Sztáray group has studied the dissociation of trans-1,3,3,3-tetrafluoroprop-1-ene (ElixClean), which is a fourth-generation refrigerant and propellant and has lower global warming potential than its precursors. My study is an extension of the ElixClean study as it aims to explore how the different number of fluorine atoms impact the dissociation reactions of these molecules. Both 3,3,3-trifluoropropene (TFP) and cis-1,2,3,3,3- pentafluoropropene (PFP) are also utilized as propellants and refrigerants.Measurements were carried out with remote access to the CRF-PEPICO (combustion reactions followed …

Gc/Ms And Lc/Ms Approaches To Chemical Communication In Plants And Marine Cyanobacteria, Carolyn Keim

University of the Pacific Theses and Dissertations

Natural products are small organic compounds synthesized by living organisms including plants, animals, and microbes. These fall into one of two categories, primary metabolites are required for survival, but secondary metabolites usually play a more accessory role. Extracts from organisms have proven to be useful to humans throughout history. Prior to evidence-based western medicine practices, natural products were the only source of medicine and remain as a treatment source for underdeveloped nations. Today, natural products have either been approved by or have served as the template for many FDA approved drugs. Based on a survey of all FDA approved drugs …

Estimating Weighted Panel Sizes For Primary Care Providers: An Assessment Of Clustering And Novel Methods Of Panel Size Estimation On Electronic Medical Records, Martin A. Lavallee

Theses and Dissertations

Primary Care is on the frontlines of healthcare, thus they see the most diverse set of patients. In order to achieve high functioning primary care, a practice must establish empanelment, the pairing of patients to providers. Enumeration of empanelment, or estimating panel sizes, helps ensure that the demands of the patients demand the supply of providers and optimize the balance of primary care resources to improve quality of care. Further we can adjust panel sizes by using patient-level data on healthcare utilization and complexity extracted from the electronic medial record to determine the amount of care or burden of work …

Mathematical Models Of Infection Prevention Programs In Hospital Settings, Kelly A. Reagan

Theses and Dissertations

Hospitals play a vital role in providing for the healthcare needs of a community. Patients can develop hospital-acquired infections (HAIs) during their hospitalization due to exposure to foreign bacteria, viruses, and fungi. Infection prevention programs target and reduce HAIs, but implementing the infection prevention programs often comes with a cost. The goal of my research is to use mathematical models to quantify the impact of infection prevention programs on cases of HAIs and total healthcare costs. First, I use a Markov chain model to quantify how one infection prevention program reduces general HAIs in the hospital. Then, I calculate the …

Antiviral Effects Of Metalloshielding: Differential Antiviral Activity Of Polynuclear Platinum And Cobalt Compounds, Mary Zoepfl

Theses and Dissertations

The majority of antiviral drug development has focused on virus-specific discovery targeting discrete steps in the individual life cycles. Although great strides have been made for a number of clinically relevant diseases such as human immunodeficiency virus, influenza virus, and hepatitis B, broad spectrum antivirals do not exist. Broad spectrum antivirals would offer (1) treatment for viruses without specifically-targeted antivirals, (2) treatment for viruses which have developed resistance to their available treatments, and (3) a rapidly deployable treatment option in viral epidemics. Many viruses including human cytomegalovirus (HCMV), HIV, and SARS-CoV-2. rely on heparan sulfate (HS), a highly sulfated glycosaminoglycan …

Multi-Modality Automatic Lung Tumor Segmentation Method Using Deep Learning And Radiomics, Siqiu Wang

Theses and Dissertations

Delineation of the tumor volume is the initial and fundamental step in the radiotherapy planning process. The current clinical practice of manual delineation is time-consuming and suffers from observer variability. This work seeks to develop an effective automatic framework to produce clinically usable lung tumor segmentations. First, to facilitate the development and validation of our methodology, an expansive database of planning CTs, diagnostic PETs, and manual tumor segmentations was curated, and an image registration and preprocessing pipeline was established. Then a deep learning neural network was constructed and optimized to utilize dual-modality PET and CT images for lung tumor segmentation. …

Behavioral Screening And Chiral Bioanalysis Of Emerging Stimulant-Type Drugs In Rats, Tyson R. Baird

Theses and Dissertations

The epidemic of drug use in the United States and elsewhere in the world has resulted in tragic loss of life and substantial economic costs. Novel psychoactive substances (NPS) are one of the contributors to this problem, and the lack of information about many of these drugs compounds their risk. This dissertation proposes a strategy to use intracranial self-stimulation (ICSS) as a behavioral screening tool to assess emerging drugs of abuse for their abuse potential in order to generate a proactive threat assessment. A series of stimulant-type drugs including methcathinone, α-pyrrolidinohexanophenone (α-PHP), cocaine, and the phenyltropane analogs of cocaine WIN35428 …

Introducing A Real-Time Advanced Eye Movements Analysis Pipeline, Gavindya Jayawardana

Computer Science Faculty Publications

Real-Time Advanced Eye Movements Analysis Pipeline (RAEMAP) is an advanced pipeline to analyze traditional positional gaze measurements as well as advanced eye gaze measurements. The proposed implementation of RAEMAP includes real-time analysis of fixations, saccades, gaze transition entropy, and low/high index of pupillary activity. RAEMAP will also provide visualizations of fixations, fixations on AOIs, heatmaps, and dynamic AOI generation in real-time. This paper outlines the proposed architecture of RAEMAP.

Smart Covid-3d-Scnn: A Novel Method To Classify X-Ray Images Of Covid-19, Ahed Abugabah, Atif Mehmood, Ahmad Ali Al Zubi, Louis Sanzogni

All Works

The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. …

Classification Of Parkinson Disease Based On Patient’S Voice Signal Using Machine Learning, Imran Ahmed, Sultan Aljahdali, Muhammad Shakeel Khan, Sanaa Kaddoura

All Works

Parkinson’s disease (PD) is a nervous system disorder first described as a neurological condition in 1817. It is one of the more prevalent diseases in the elderly, and Alzheimer’s is the second most common neurodegenerative illness. It impacts the patient’s movement. Symptoms start gradually with tremors, stiffness in movement, and speech and voice disorders. Researches proved that 89% of patients with Parkinson’s has speech disorder including uncertain articulation, hoarse and breathy voice and monotone pitch. The cause behind this voice change is the reduction of dopamine due to damage of neurons in the substantia nigra responsible for dopamine production. In …

Biomolecular Corona Stability In Association With Plasma Cholesterol Level, Duong N. Trinh, Meda Radlinskaite, Jack Cheeseman, Gunther Kuhnle, Helen M.I. Osborn, Paula Meleady, Daniel I.R. Spencer, Marco P. Monopoli

Articles

Biomolecular corona is spontaneously formed on the surface of nanoparticles (NPs) when they are in contact with biological fluids. It plays an important role in the colloidal stability of NPs, which is of importance for most of their medical applications and toxicity assessment. While typical studies use either blood plasma or serum from a pooled biobank, it is unclear whether differences in the media, such as cholesterol level or protein concentration, might affect the NP colloidal stability and corona composition. In this study, the silica corona was prepared at particularly low plasma concentrations (3%, v/v–1.98 mg/mL) to identify the critical …

A Highly Conductive, Flexible, And 3d-Printable Carbon Nanotube-Elastomer Ink For Additive Bio-Manufacturing, Andy Shar, Phillip Glass, Daeha Joung Ph.D.

UROP Posters

The synthesis of a highly conductive, flexible, 3D-printable, and biocompatible ink has been of great interest in the field of bio-based additive manufacturing. Various applications include ultra-sensitive, microscale tactile sensors, patient-customizable scaffolds for cardiac and nerve tissue regeneration, and flexible electrocardiogram (ECG) electrodes. Here, a novel elastomeric carbon nanocomposite is presented consisting of amino-functionalized carbon nanotubes (CNT-NH₂) homogenously dispersed in a one-part room-temperature vulcanizing (RTV) silicone matrix. The use of acetone as a swelling solvent aids in electrical percolation through the elastomer matrix. CNT-NH₂ ratios can be tuned to fit various needs; higher tensile strength is favored …

A Participatory Group Process To Collect And Disseminate Covid-19 Needs Assessment Data, Areebah Ahmed

UROP Posters

The Richmond, VA COVID-19 Needs Assessment Survey (RVA CoNA) was created in March 2020 to identify behaviors and needs related to COVID-19 in Richmond area adults ages 18 and over. Results are being used to inform support, strategic efforts, and educational outreach of local community organizations. The purpose of this study is to (1) summarize the process used to develop the RVA CoNA, (2) summarize preliminary survey results from a second phase of data collection as well as initial feedback from community partners, and (3) summarize initial conclusions and results dissemination strategies.Community partners and researchers at Virginia Commonwealth University jointly …

A Domain Adaptation Approach For Segmenting Cell Instances In Microscopy Data, Matthew R. Keaton

Graduate Theses, Dissertations, and Problem Reports (ETD)

Automated cellular instance segmentation is a process that has been utilized for accelerating biological research since before the deep learning era, and recent advancements have produced higher quality results with less effort from the biologist. Most current endeavors focus on completely cutting the researcher out of the picture by generating highly generalized models. However, these models invariably fail when faced with novel data and effectively opt to miss out on the full capabilities of deep learning in pursuit of this goal. In our work, we demonstrate how, with even a minimal amount of annotated data, dominant approaches in this space …

Artificial Intelligence In The Pediatric Echocardiography Laboratory: Automation, Physiology, And Outcomes, Minh B Nguyen, Olivier Villemain, Mark K Friedberg, Lasse Lovstakken, Craig G Rusin, Luc Mertens

Faculty, Staff and Students Publications

Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better …

Perceptions And Needs Of Artificial Intelligence In Health Care To Increase Adoption: Scoping Review, Han Shi Jocelyn Chew, Palakorn Achananuparp

Research Collection School Of Computing and Information Systems

Background: Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care. Objective: This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care. Methods: A systematic scoping review was conducted according to the 5-stage framework by Arksey and O’Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, …

Comparison Of The Mental Burden On Nursing Care Providers With And Without Mat-Type Sleep State Sensors At A Nursing Home In Tokyo, Japan: Quasi-Experimental Study, Sakiko Itoh, Hwee-Pink Tan, Kenichi Kudo, Yasuko Ogata

Research Collection School Of Computing and Information Systems

Background: Increasing need for nursing care has led to the increased burden on formal caregivers, with those in nursing homes having to deal with exhausting labor. Although research activities on the use of internet of things devices to support nursing care for older adults exist, there is limited evidence on the effectiveness of these interventions among formal caregivers in nursing homes. Objective: This study aims to investigate whether mat-type sleep state sensors for supporting nursing care can reduce the mental burden of formal caregivers in a nursing home. Methods: This was a quasi-experimental study at a nursing home in Tokyo, …