Medicine and Health Sciences Commons^™

Assessing The Reidentification Risks Posed By Deep Learning Algorithms Applied To Ecg Data, Arin Ghazarian, Jianwei Zheng, Daniele Struppa, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

ECG (Electrocardiogram) data analysis is one of the most widely used and important tools in cardiology diagnostics. In recent years the development of advanced deep learning techniques and GPU hardware have made it possible to train neural network models that attain exceptionally high levels of accuracy in complex tasks such as heart disease diagnoses and treatments. We investigate the use of ECGs as biometrics in human identification systems by implementing state-of-the-art deep learning models. We train convolutional neural network models on approximately 81k patients from the US, Germany and China. Currently, this is the largest research project on ECG identification. …

Biophysical Insight Into The Sars-Cov2 Spike–Ace2 Interaction And Its Modulation By Hepcidin Through A Multifaceted Computational Approach, Hamid Hadi-Alijanvand, Luisa Di Paola, Guang Hu, David M. Leitner, Gennady M. Verkhivker, Peixin Sun, Humanath Poudel, Alessandro Giuliani

Mathematics, Physics, and Computer Science Faculty Articles and Research

At the center of the SARS-CoV2 infection, the spike protein and its interaction with the human receptor ACE2 play a central role in the molecular machinery of SARS-CoV2 infection of human cells. Vaccine therapies are a valuable barrier to the worst effects of the virus and to its diffusion, but the need of purposed drugs is emerging as a core target of the fight against COVID19. In this respect, the repurposing of drugs has already led to discovery of drugs thought to reduce the effects of the cytokine storm, but still a drug targeting the spike protein, in the infection …

Computer Simulations And Network-Based Profiling Of Binding And Allosteric Interactions Of Sars-Cov-2 Spike Variant Complexes And The Host Receptor: Dissecting The Mechanistic Effects Of The Delta And Omicron Mutations, Gennady M. Verkhivker, Steve Agajanian, Ryan Kassab, Keerthi Krishnan

Mathematics, Physics, and Computer Science Faculty Articles and Research

In this study, we combine all-atom MD simulations and comprehensive mutational scanning of S-RBD complexes with the angiotensin-converting enzyme 2 (ACE2) host receptor in the native form as well as the S-RBD Delta and Omicron variants to (a) examine the differences in the dynamic signatures of the S-RBD complexes and (b) identify the critical binding hotspots and sensitivity of the mutational positions. We also examined the differences in allosteric interactions and communications in the S-RBD complexes for the Delta and Omicron variants. Through the perturbation-based scanning of the allosteric propensities of the SARS-CoV-2 S-RBD residues and dynamics-based network centrality and …

Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen

Engineering Faculty Articles and Research

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …

A Push For Inclusive Data Collection In Stem Organizations, Nicholas P. Burnett, Alyssa M. Hernandez, Emily E. King, Richelle L. Tanner, Kathryn Wilsterman

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Professional organizations in STEM (science, technology, engineering, and mathematics) can use demographic data to quantify recruitment and retention (R&R) of underrepresented groups within their memberships. However, variation in the types of demographic data collected can influence the targeting and perceived impacts of R&R efforts - e.g., giving false signals of R&R for some groups. We obtained demographic surveys from 73 U.S.-affiliated STEM organizations, collectively representing 712,000 members and conference-attendees. We found large differences in the demographic categories surveyed (e.g., disability status, sexual orientation) and the available response options. These discrepancies indicate a lack of consensus regarding the demographic groups that …

Dissecting Mutational Allosteric Effects In Alkaline Phosphatases Associated With Different Hypophosphatasia Phenotypes: An Integrative Computational Investigation, Fei Xiao, Ziyun Zhou, Xingyu Song, Mi Gan, Jie Long, Gennady M. Verkhivker, Guang Hu

Mathematics, Physics, and Computer Science Faculty Articles and Research

Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the …

A High Precision Machine Learning-Enabled System For Predicting Idiopathic Ventricular Arrhythmia Origins, Jianwei Zheng, Guohua Fu, Daniele Struppa, Islam Abudayyeh, Tahmeed Contractor, Kyle Anderson, Huimin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Background: Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy.

Method: A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, …

Structural And Computational Studies Of The Sars-Cov-2 Spike Protein Binding Mechanisms With Nanobodies: From Structure And Dynamics To Avidity-Driven Nanobody Engineering, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Nanobodies provide important advantages over traditional antibodies, including their smaller size and robust biochemical properties such as high thermal stability, high solubility, and the ability to be bioengineered into novel multivalent, multi-specific, and high-affinity molecules, making them a class of emerging powerful therapies against SARS-CoV-2. Recent research efforts on the design, protein engineering, and structure-functional characterization of nanobodies and their binding with SARS-CoV-2 S proteins reflected a growing realization that nanobody combinations can exploit distinct binding epitopes and leverage the intrinsic plasticity of the conformational landscape for the SARS-CoV-2 S protein to produce efficient neutralizing and mutation resistant characteristics. Structural …

Allosteric Determinants Of The Sars-Cov-2 Spike Protein Binding With Nanobodies: Examining Mechanisms Of Mutational Escape And Sensitivity Of The Omicron Variant, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Structural and biochemical studies have recently revealed a range of rationally engineered nanobodies with efficient neutralizing capacity against the SARS-CoV-2 virus and resilience against mutational escape. In this study, we performed a comprehensive computational analysis of the SARS-CoV-2 spike trimer complexes with single nanobodies Nb6, VHH E, and complex with VHH E/VHH V nanobody combination. We combined coarse-grained and all-atom molecular simulations and collective dynamics analysis with binding free energy scanning, perturbation-response scanning, and network centrality analysis to examine mechanisms of nanobody-induced allosteric modulation and cooperativity in the SARS-CoV-2 spike trimer complexes with these nanobodies. By quantifying energetic and allosteric …

Escherichia Coli Alanyl-Trna Synthetase Maintains Proofreading Activity And Translational Accuracy Under Oxidative Stress, Arundhati Kavoor, Paul Kelly, Michael Ibba

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Aminoacyl-tRNA synthetases (aaRSs) are enzymes that synthesize aminoacyl-tRNAs to facilitate translation of the genetic code. Quality control by aaRS proofreading and other mechanisms maintains translational accuracy, which promotes cellular viability. Systematic disruption of proofreading, as recently demonstrated for alanyl-tRNA synthetase (AlaRS), leads to dysregulation of the proteome and reduced viability. Recent studies showed that environmental challenges such as exposure to reactive oxygen species can also alter aaRS synthetic and proofreading functions, prompting us to investigate if oxidation might positively or negatively affect AlaRS activity. We found that while oxidation leads to modification of several residues in Escherichia coli AlaRS, unlike …

Ambient Air Pollution Exposure And Increasing Depressive Symptoms In Older Women: The Mediating Role Of The Prefrontal Cortex And Insula, Andrew J. Petkus, Susan M. Resnick, Xinhui Wang, Daniel P. Beavers, Mark A. Espeland, Margaret Gatz, Tara Gruenewald, Joshua Millstein, Helena C. Chui, Joel D. Kaufman, Joann E. Manson, Gregory A. Wellenius, Eric A. Whitsel, Keith Widaman, Diana Younan, Jiu-Chiuan Chen

Psychology Faculty Articles and Research

Exposures to fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) have been associated with the emergence of depressive symptoms in older adulthood, although most studies used cross-sectional outcome measures. Elucidating the brain structures mediating the adverse effects can strengthen the causal role between air pollution and increasing depressive symptoms. We evaluated whether smaller volumes of brain structures implicated in late-life depression mediate associations between ambient air pollution exposure and changes in depressive symptoms. This prospective study included 764 community-dwelling older women (aged 81.6 ± 3.6 in 2008–2010) from the Women's Health Initiative Memory Study (WHIMS) Magnetic …

Applications Of Unsupervised Machine Learning In Autism Spectrum Disorder Research: A Review, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis R. Dixon, Erik J. Linstead

Engineering Faculty Articles and Research

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of …

Feel And Touch: A Haptic Mobile Game To Assess Tactile Processing, Ivonne Monarca, Monica Tentori, Franceli L. Cibrian

Engineering Faculty Articles and Research

Haptic interfaces have great potential for assessing the tactile processing of children with Autism Spectrum Disorder (ASD), an area that has been under-explored due to the lack of tools to assess it. Until now, haptic interfaces for children have mostly been used as a teaching or therapeutic tool, so there are still open questions about how they could be used to assess tactile processing of children with ASD. This article presents the design process that led to the development of Feel and Touch, a mobile game augmented with vibrotactile stimuli to assess tactile processing. Our feasibility evaluation, with 5 children …

Spatial Distribution Of Pm2.5-Related Premature Mortality In China, Sheng Zheng, Uwe Schlink, Kin-Fai Ho, Ramesh P. Singh, Andrea Pozzer

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

PM_2.5 is a major component of air pollution in China and has a serious threat to public health. It is very important to quantify spatial characteristics of the health effects caused by outdoor PM_2.5 exposure. This study analyzed the spatial distribution of PM_2.5 concentration (45.9 μg/m³ national average in 2016) and premature mortality attributed to PM_2.5 in cities at the prefectural level and above in China in 2016. Using the Global Exposure Mortality Model (GEMM), the total premature mortality in China was estimated to be 1.55 million persons, and the per capita mortality was 11.2 …

Cyclic Peptide-Gadolinium Nanocomplexes As Sirna Delivery Tools, Amir Nasrolahi Shirazi, Muhammad Imran Sajid, Dindyal Mandal, David Stickley, Stephanie Nagasawa, Joshua Long, Sandeep Lohan, Keykavous Parang, Rakesh Kumar Tiwari

Pharmacy Faculty Articles and Research

We have recently reported that a cyclic peptide containing five tryptophan, five arginine, and one cysteine amino acids [(WR)₅C], was able to produce peptide-capped gadolinium nanoparticles, [(WR)₅C]-GdNPs, in the range of 240 to 260 nm upon mixing with an aqueous solution of GdCl₃. Herein, we report [(WR)₅C]-GdNPs as an efficient siRNA delivery system. The peptide-based gadolinium nanoparticles (50 µM) did not exhibit significant cytotoxicity (~93% cell viability at 50 µM) in human leukemia T lymphoblast cells (CCRF-CEM) and triple-negative breast cancer cells (MDA-MB-231) after 48 h. Fluorescence-activated cell sorting (FACS) analysis indicated …

Atomistic Simulations And In Silico Mutational Profiling Of Protein Stability And Binding In The Sars-Cov-2 Spike Protein Complexes With Nanobodies: Molecular Determinants Of Mutational Escape Mechanisms, Gennady M. Verkhivker, Steve Agajanian, Deniz Yasar Oztas, Grace Gupta

Mathematics, Physics, and Computer Science Faculty Articles and Research

Structure-functional studies have recently revealed a spectrum of diverse high-affinity nanobodies with efficient neutralizing capacity against SARS-CoV-2 virus and resilience against mutational escape. In this study, we combine atomistic simulations with the ensemble-based mutational profiling of binding for the SARS-CoV-2 S-RBD complexes with a wide range of nanobodies to identify dynamic and binding affinity fingerprints and characterize the energetic determinants of nanobody-escaping mutations. Using an in silico mutational profiling approach for probing the protein stability and binding, we examine dynamics and energetics of the SARS-CoV-2 complexes with single nanobodies Nb6 and Nb20, VHH E, a pair combination VHH E + …

Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter

Computational and Data Sciences (MS) Theses

Advancements in genetic sequencing methods for microbiomes in recent decades have permitted the collection of taxonomic and functional profiles of microbial communities, accelerating the discovery of the functional aspects of the microbiome and generating an increased interest among clinicians in applying these techniques with patients. This advancement has coincided with software and hardware improvements in the field of machine learning and deep learning. Combined, these advancements implicate further potential for progress in disease diagnosis and treatment in humans. The ability to classify a human microbiome profile into a disease category, and additionally identify the differentiating factors within the profile between …

Ganglioside Alters Phospholipase Trafficking, Inhibits Nf-Κb Assembly, And Protects Tight Junction Integrity, John J. Miklavcic, Qun Li, Jordan Skolnick, Alan B. R. Thomson, Vera C. Mazurak, Michael Tom Clandinin

Food Science Faculty Articles and Research

Background and Aims: Dietary gangliosides are present in human milk and consumed in low amounts from organ meats. Clinical and animal studies indicate that dietary gangliosides attenuate signaling processes that are a hallmark of inflammatory bowel disease (IBD). Gangliosides decrease pro-inflammatory markers, improve intestinal permeability, and reduce symptoms characteristic in patients with IBD. The objective of this study was to examine mechanisms by which dietary gangliosides exert beneficial effects on intestinal health.

Methods: Studies were conducted in vitro using CaCo-2 intestinal epithelial cells. Gangliosides were extracted from milk powder and incubated with differentiated CaCo-2 cells after exposure to pro-inflammatory stimuli. …

Landscape-Based Mutational Sensitivity Cartography And Network Community Analysis Of The Sars-Cov-2 Spike Protein Structures: Quantifying Functional Effects Of The Circulating D614g Variant, Gennady M. Verkhivker, Steve Agajanian, Deniz Yasar Oztas, Grace Gupta

Mathematics, Physics, and Computer Science Faculty Articles and Research

We developed and applied a computational approach to simulate functional effects of the global circulating mutation D614G of the SARS-CoV-2 spike protein. All-atom molecular dynamics simulations are combined with deep mutational scanning and analysis of the residue interaction networks to investigate conformational landscapes and energetics of the SARS-CoV-2 spike proteins in different functional states of the D614G mutant. The results of conformational dynamics and analysis of collective motions demonstrated that the D614 site plays a key regulatory role in governing functional transitions between open and closed states. Using mutational scanning and sensitivity analysis of protein residues, we identified the stability …

Computational Analysis Of Protein Stability And Allosteric Interaction Networks In Distinct Conformational Forms Of The Sars Cov 2 Spike D614g Mutant: Reconciling Functional Mechanisms Through Allosteric Model Of Spike Regulation, Gennady M. Verkhivker, Steve Agajanian, Deniz Oztas, Grace Gupta

Mathematics, Physics, and Computer Science Faculty Articles and Research

In this study, we used an integrative computational approach to examine molecular mechanisms underlying functional effects of the D614G mutation by exploring atomistic modeling of the SARS-CoV-2 spike proteins as allosteric regulatory machines. We combined coarse-grained simulations, protein stability and dynamic fluctuation communication analysis with network-based community analysis to examine structures of the native and mutant SARS-CoV-2 spike proteins in different functional states. Through distance fluctuations communication analysis, we probed stability and allosteric communication propensities of protein residues in the native and mutant SARS-CoV-2 spike proteins, providing evidence that the D614G mutation can enhance long-range signaling of the allosteric spike …

Optimal Analytical Methods For High Accuracy Cardiac Disease Classification And Treatment Based On Ecg Data, Jianwei Zheng

Computational and Data Sciences (PhD) Dissertations

This work constitutes six projects. In the first project, a newly inaugurated research database for 12-lead electrocardiogram signals was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine). This database aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. In the second project, we created a new 12-lead ECG database under the auspices of Chapman University and Ningbo First Hospital of Zhejiang University that aims to provide high quality data enabling detection of the distinctions between idiopathic ventricular arrhythmia from right ventricular outflow tract …

Data For "Subtype-Selective Positive Modulation Of Sk Channels Depends On The Ha/Hb Helices", Miao Zhang, Meng Cui

Pharmacy Faculty Data Sets

In the activated state of small-conductance Ca²⁺-activated potassium (SK) channels, calmodulin interacts with the HA/HB helices and the S4-S5 linker. CyPPA potentiates SK2a and SK3 channel activity but not the SK1 and IK subtypes. Here, we report that the subtype-selectivity of CyPPA relies on the HA/HB helices. Mutating residues in the HA (V420) and HB (K467) helices of SK2a channels to their equivalent residues in IK channels diminished the potency of CyPPA. CyPPA elicited prominent responses on mutant IK channels with an arginine residue in the HB helix substituted for its equivalent lysine residue in the SK2a channels …

A High-Precision Machine Learning Algorithm To Classify Left And Right Outflow Tract Ventricular Tachycardia, Jianwei Zhang, Guohua Fu, Islam Abudayyeh, Magdi Yacoub, Anthony Chang, William Feaster, Louis Ehwerhemuepha, Hesham El-Askary, Xianfeng Du, Bin He, Mingjun Feng, Yibo Yu, Binhao Wang, Jing Liu, Hai Yao, Hulmin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Introduction: Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.

Methods: We randomly sampled training, validation, and testing …

Label‑Free Spectral Imaging To Study Drug Distribution And Metabolism In Single Living Cells, Qamar Alshammari, Rajasekharreddy Pala, Nir Katzir, Surya M. Nauli

Pharmacy Faculty Articles and Research

During drug development, evaluation of drug and its metabolite is an essential process to understand drug activity, stability, toxicity and distribution. Liquid chromatography (LC) coupled with mass spectrometry (MS) has become the standard analytical tool for screening and identifying drug metabolites. Unlike LC/MS approach requiring liquifying the biological samples, we showed that spectral imaging (or spectral microscopy) could provide high-resolution images of doxorubicin (dox) and its metabolite doxorubicinol (dox’ol) in single living cells. Using this new method, we performed measurements without destroying the biological samples. We calculated the rate constant of dox translocating from extracellular moiety into the cell and …

The Mechanism Of Β-N-Methylamino-L-Alanine Inhibition Of Trna Aminoacylation And Its Impact On Misincorporation, Nien-Ching Han, Tammy J. Bullwinkle, Kaeli F. Loeb, Kym F. Faull, Kyle Mohler, Jesse Rinehart, Michael Ibba

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

β-N-methylamino-l-alanine (BMAA) is a nonproteinogenic amino acid that has been associated with neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and Alzheimer's disease (AD). BMAA has been found in human protein extracts; however, the mechanism by which it enters the proteome is still unclear. It has been suggested that BMAA is misincorporated at serine codons during protein synthesis, but direct evidence of its cotranslational incorporation is currently lacking. Here, using LC-MS–purified BMAA and several biochemical assays, we sought to determine whether any aminoacyl-tRNA synthetase (aaRS) utilizes BMAA as a substrate for aminoacylation. Despite BMAA's previously predicted misincorporation at serine …

Differential Modulation Of Sk Channel Subtypes By Phosphorylation, Young-Woo Nam, Dezhi Kong, Dong Wang, Razan Orfali, Rinzhin T. Sherpa, Jennifer Totonchy, Surya M. Nauli, Miao Zhang

Pharmacy Faculty Articles and Research

Small-conductance Ca²⁺-activated K⁺ (SK) channels are voltage-independent and are activated by Ca²⁺ binding to the calmodulin constitutively associated with the channels. Both the pore-forming subunits and the associated calmodulin are subject to phosphorylation. Here, we investigated the modulation of different SK channel subtypes by phosphorylation, using the cultured endothelial cells as a tool. We report that casein kinase 2 (CK2) negatively modulates the apparent Ca²⁺ sensitivity of SK1 and IK channel subtypes by more than 5-fold, whereas the apparent Ca²⁺ sensitivity of the SK3 and SK2 subtypes is only reduced by ∼2-fold, when heterologously …

Coevolution, Dynamics And Allostery Conspire In Shaping Cooperative Binding And Signal Transmission Of The Sars-Cov-2 Spike Protein With Human Angiotensin-Converting Enzyme 2, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Binding to the host receptor is a critical initial step for the coronavirus SARS-CoV-2 spike protein to enter into target cells and trigger virus transmission. A detailed dynamic and energetic view of the binding mechanisms underlying virus entry is not fully understood and the consensus around the molecular origins behind binding preferences of SARS-CoV-2 for binding with the angiotensin-converting enzyme 2 (ACE2) host receptor is yet to be established. In this work, we performed a comprehensive computational investigation in which sequence analysis and modeling of coevolutionary networks are combined with atomistic molecular simulations and comparative binding free energy analysis of …

Investigating The Significance Of Aerosols In Determining The Coronavirus Fatality Rate Among Three European Countries, Wenzhao Li, Rejoice Thomas, Hesham El-Askary, Thomas Piechota, Daniele Struppa, Khaled A. Abdel Ghaffar

Mathematics, Physics, and Computer Science Faculty Articles and Research

The coronavirus pandemic has not only gripped the scientific community in the search for a vaccine or a cure but also in attempts using statistics and association analysis—to identify environmental factors that increase its potency. A study by Ogen (Sci Total Environ 726:138605, 2020a) explored the possible correlation between coronavirus fatality and high nitrogen dioxide exposure in four European countries—France, Germany, Italy and Spain. Meanwhile, another study showed the importance of nitrogen dioxide along with population density in determining the coronavirus pandemic rate in England. In this follow-up study, Aerosol Optical Depth (AOD) was introduced in conjunction with other variables …

Design And Synthesis Of Core–Shell Microgels With One‐Step Clickable Crosslinked Cores And Ultralow Crosslinked Shells, Molla R. Islam, Chelsea Nguy, Sanika Pandit, L. Andrew Lyon

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The present study is conducted to explore the engineering of core–shell microgels such that the core can be rapidly labeled with a variety of fluorophores, while the shell retains the softness needed in specific biomedical applications. Azide containing crosslinked core particles based on a crosslinked poly(N‐isopropylacrylamide) particle, using a one‐pot, multistep polymerization is synthesized. A core–shell microgel is then synthesized by growing a crosslinker‐free poly(N‐isopropylacrylamide)‐co‐acrylic acid (ULC10AAc) shell through a two‐step seed and feed polymerization. A simple “click” reaction between the azide present on the core and dibenzocyclooctyne containing fluorophores to make dyed core–shell …

Exploring The Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, And Autism Quotient To Identify Eating Disorder Vulnerability: A Cluster Analysis, Natalia Stewart Rosenfield, Erik Linstead

Engineering Faculty Articles and Research

Eating disorders are very complicated and many factors play a role in their manifestation. Furthermore, due to the variability in diagnosis and symptoms, treatment for an eating disorder is unique to the individual. As a result, there are numerous assessment tools available, which range from brief survey questionnaires to in-depth interviews conducted by a professional. One of the many benefits to using machine learning is that it offers new insight into datasets that researchers may not previously have, particularly when compared to traditional statistical methods. The aim of this paper was to employ k-means clustering to explore the Eating Disorder …