Medicine and Health Sciences Commons^™

Apigenin Alleviates Autistic-Like Stereotyped Repetitive Behaviors And Mitigates Brain Oxidative Stress In Mice, Petrilla Jayaprakash, Dmytro Isaev, Keun-Hang Susan Yang, Rami Beiram, Murat Oz, Bassem Sadek

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Studying the involvement of nicotinic acetylcholine receptors (nAChRs), specifically α7-nAChRs, in neuropsychiatric brain disorders such as autism spectrum disorder (ASD) has gained a growing interest. The flavonoid apigenin (APG) has been confirmed in its pharmacological action as a positive allosteric modulator of α7-nAChRs. However, there is no research describing the pharmacological potential of APG in ASD. The aim of this study was to evaluate the effects of the subchronic systemic treatment of APG (10–30 mg/kg) on ASD-like repetitive and compulsive-like behaviors and oxidative stress status in the hippocampus and cerebellum in BTBR mice, utilizing the reference drug aripiprazole (ARP, 1 …

Accurate Characterization Of Binding Kinetics And Allosteric Mechanisms For The Hsp90 Chaperone Inhibitors Using Ai-Augmented Integrative Biophysical Studies, Chao Xu, Xianglei Zhang, Lianghao Zhao, Gennady M. Verkhivker, Fang Bai

Mathematics, Physics, and Computer Science Faculty Articles and Research

The binding kinetics of drugs to their targets are gradually being recognized as a crucial indicator of the efficacy of drugs in vivo, leading to the development of various computational methods for predicting the binding kinetics in recent years. However, compared with the prediction of binding affinity, the underlying structure and dynamic determinants of binding kinetics are more complicated. Efficient and accurate methods for predicting binding kinetics are still lacking. In this study, quantitative structure–kinetics relationship (QSKR) models were developed using 132 inhibitors targeting the ATP binding domain of heat shock protein 90α (HSP90α) to predict the dissociation rate …

Β-Sheets Mediate The Conformational Change And Allosteric Signal Transmission Between The Aslov2 Termini, Sian Xiao, Mayar Terek Ibrahim, Gennady M. Verkhivker, Brian D. Zoltowski, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

Avena sativa phototropin 1 light-oxygen-voltage 2 domain (AsLOV2) is a model protein of Per-Arnt-Sim (PAS) superfamily, characterized by conformational changes in response to external environmental stimuli. This conformational change begins with the unfolding of the N-terminal A'α helix in the dark state followed by the unfolding of the C-terminal Jα helix. The light state is characterized by the unfolded termini and the subsequent modifications in hydrogen bond patterns. In this photoreceptor, β-sheets are identified as crucial components for mediating allosteric signal transmission between the two termini. Through combined experimental and computational investigations, the Hβ …

De Novo Drug Design Using Transformer-Based Machine Translation And Reinforcement Learning Of An Adaptive Monte Carlo Tree Search, Dony Ang, Cyril Rakovski, Hagop S. Atamian

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder–Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards …

Mutational Analysis Of The Nitrogenase Carbon Monoxide Protective Protein Cown Reveals That A Conserved C‑Terminal Glutamic Acid Residue Is Necessary For Its Activity, Dustin L. Willard, Joshuah J. Arellano, Mitch Underdahl, Terrence M. Lee, Avinash S. Ramaswamy, Gabriella Fumes, Agatha Kliman, Emily Y. Wong, Cedric P. Owens

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Nitrogenase is the only enzyme that catalyzes the reduction of nitrogen gas into ammonia. Nitrogenase is tightly inhibited by the environmental gas carbon monoxide (CO). Many nitrogen fixing bacteria protect nitrogenase from CO inhibition using the protective protein CowN. This work demonstrates that a conserved glutamic acid residue near the C-terminus of Gluconacetobacter diazotrophicus CowN is necessary for its function. Mutation of the glutamic acid residue abolishes both CowN’s protection against CO inhibition and the ability of CowN to bind to nitrogenase. In contrast, a conserved C-terminal cysteine residue is not important for CO protection by CowN. Overall, this work …

One Font Doesn’T Fit All: The Influence Of Digital Text Personalization On Comprehension In Child And Adolescent Readers, Shannon M. Sheppard, Susanne L. Nobles, Anton Palma, Sophie Kajfez, Marjorie Jordan, Kathy Crowley, Sofie Beier

Communication Sciences and Disorders Faculty Articles and Research

Reading comprehension is an essential skill. It is unclear whether and to what degree typography and font personalization may impact reading comprehension in younger readers. With advancements in technology, it is now feasible to personalize digital reading formats in general technology tools, but this feature is not yet available for many educational tools. The current study aimed to investigate the effect of character width and inter-letter spacing on reading speed and comprehension. We enrolled 94 children (kindergarten–8th grade) and compared performance with six font variations on a word-level semantic decision task (Experiment 1) and a passage-level comprehension task (Experiment 2). …

Additive Effects Of Cyclic Peptide [R4w4] When Added Alongside Azithromycin And Rifampicin Against Mycobacterium Avium Infection, Melissa Kelley, Kayvan Sasaninia, Arbi Abnousian, Ali Badaoui, James Owens, Abrianna Beever, Nala Kachour, Rakesh Kumar Tiwari, Vishwanath Venketaraman

Pharmacy Faculty Articles and Research

Mycobacterium avium (M. avium), a type of nontuberculous mycobacteria (NTM), poses a risk for pulmonary infections and disseminated infections in immunocompromised individuals. Conventional treatment consists of a 12-month regimen of the first-line antibiotics rifampicin and azithromycin. However, the treatment duration and low antibiotic tolerability present challenges in the treatment of M. avium infection. Furthermore, the emergence of multidrug-resistant mycobacterium strains prompts a need for novel treatments against M. avium infection. This study aims to test the efficacy of a novel antimicrobial peptide, cyclic [R4W4], alongside the first-line antibiotics azithromycin and rifampicin in reducing M. avium survival. Colony-forming unit (CFU) …

Balancing Functional Tradeoffs Between Protein Stability And Ace2 Binding In The Sars-Cov-2 Omicron Ba.2, Ba.2.75 And Xbb Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic And Energetic Changes, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta

Mathematics, Physics, and Computer Science Faculty Articles and Research

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and …

Small Community Water Systems Have The Highest Prevalence Of Mn In Drinking Water In California, Usa, Miranda Aiken, Samantha C. Ying

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Manganese (Mn) is currently regulated as a secondary contaminant in California, USA; however, recent revisions of the World Health Organization drinking water guidelines have increased regulatory attention of Mn in drinking water due to increasing reports of neurotoxic effects in infants and children. In this study, Mn concentrations reported to California’s Safe Drinking Water Information System were used to estimate the potentially exposed population within California based on system size. We estimate that between 2011 and 2021, over 525,000 users in areas with reported Mn data are potentially exposed to Mn concentrations exceeding the WHO health-based guideline (80 μg L …

Structure-Guided Mutagenesis Reveals The Catalytic Residue That Controls The Regiospecificity Of C6-Indole Prenyltransferases, Ahmed R. Aoun, Nagaraju Mupparapu, Diem N. Nguyen, Tae Ho Kim, Christopher M. Nguyen, Zhengfeiyue Pan, Sherif I. Elshahawi

Pharmacy Faculty Articles and Research

Indole is a significant structural moiety and functionalization of the C−H bond in indole-containing molecules expands their chemical space, and modifies their properties and/or activities. Indole prenyltransferases (IPTs) catalyze the direct regiospecific installation of prenyl moieties on indole-derived compounds. IPTs have shown relaxed substrate flexibility enabling them to be used as tools for indole functionalization. However, the mechanism by which certain IPTs target a specific carbon position is not fully understood. Herein, we use structure-guided site-directed mutagenesis, in vitro enzymatic reactions, kinetics and structural-elucidation of analogs to verify the key catalytic residues that control the regiospecificity of all characterized regiospecific …

Dense & Attention Convolutional Neural Networks For Toe Walking Recognition, Junde Chen, Rahul Soangra, Marybeth Grant-Beuttler, Y. A. Nanehkaran, Yuxin Wen

Physical Therapy Faculty Articles and Research

Idiopathic toe walking (ITW) is a gait disorder where children’s initial contacts show limited or no heel touch during the gait cycle. Toe walking can lead to poor balance, increased risk of falling or tripping, leg pain, and stunted growth in children. Early detection and identification can facilitate targeted interventions for children diagnosed with ITW. This study proposes a new one-dimensional (1D) Dense & Attention convolutional network architecture, which is termed as the DANet, to detect idiopathic toe walking. The dense block is integrated into the network to maximize information transfer and avoid missed features. Further, the attention modules are …

From Deep Mutational Mapping Of Allosteric Protein Landscapes To Deep Learning Of Allostery And Hidden Allosteric Sites: Zooming In On “Allosteric Intersection” Of Biochemical And Big Data Approaches, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta, Sian Xiao, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric …

Computational Design And Molecular Modeling Of Morphine Derivatives For Preferential Binding In Inflamed Tissue, Makena Augenstein, Nayiri Alexander, Matthew Gartner

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The opioid epidemic has impacted over 10 million Americans in 2019. Opioids, like morphine, bind non-selectively in both peripheral tissue, leading to effective pain relief, and central tissue, resulting in dangerous side effects and addiction. The inflamed conditions of injured tissues have a lower pH (pH = 6–6.5) environment than healthy tissue (pH = 7.4). We aim to design a morphine derivative that binds selectively within inflamed tissue using molecular extension and dissection techniques. Morphine binds to the μ-opioid receptor (MOR) when the biochemically active amine group is protonated. Fluorination of a β-carbon from the tertiary amine group led to …

Coarse-Grained Molecular Simulations And Ensemble-Based Mutational Profiling Of Protein Stability In The Different Functional Forms Of The Sars-Cov-2 Spike Trimers: Balancing Stability And Adaptability In Ba.1, Ba.2 And Ba.2.75 Variants, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta

Mathematics, Physics, and Computer Science Faculty Articles and Research

Evolutionary and functional studies have suggested that the emergence of Omicron variants can be determined by multiple fitness tradeoffs including immune escape, binding affinity, conformational plasticity, protein stability, and allosteric modulation. In this study, we embarked on a systematic comparative analysis of the conformational dynamics, electrostatics, protein stability, and allostery in the different functional states of spike trimers for BA.1, BA.2, and BA.2.75 variants. Using efficient and accurate coarse-grained simulations and atomistic reconstruction of the ensembles, we examined the conformational dynamics of the spike trimers that agree with the recent functional studies, suggesting that BA.2.75 trimers are the most stable …

Virtual And In Vitro Screening Of Natural Products Identifies Indole And Benzene Derivatives As Inhibitors Of Sars-Cov-2 Main Protease (MPro), Dony Ang, Riley Kendall, Hagop S. Atamian

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The rapid spread of the coronavirus disease 2019 (COVID-19) resulted in serious health, social, and economic consequences. While the development of effective vaccines substantially reduced the severity of symptoms and the associated deaths, we still urgently need effective drugs to further reduce the number of casualties associated with SARS-CoV-2 infections. Machine learning methods both improved and sped up all the different stages of the drug discovery processes by performing complex analyses with enormous datasets. Natural products (NPs) have been used for treating diseases and infections for thousands of years and represent a valuable resource for drug discovery when combined with …

Predicting Suicidal And Self-Injurious Events In A Correctional Setting Using Ai Algorithms On Unstructured Medical Notes And Structured Data, Hongxia Lu, Alex Barrett, Albert Pierce, Jianwei Zheng, Yun Wang, Chun Chiang, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Suicidal and self-injurious incidents in correctional settings deplete the institutional and healthcare resources, create disorder and stress for staff and other inmates. Traditional statistical analyses provide some guidance, but they can only be applied to structured data that are often difficult to collect and their recommendations are often expensive to act upon. This study aims to extract information from medical and mental health progress notes using AI algorithms to make actionable predictions of suicidal and self-injurious events to improve the efficiency of triage for health care services and prevent suicidal and injurious events from happening at California's Orange County Jails. …

Not A Waste: Wastewater Surveillance To Enhance Public Health, Anna Gitter, Jeremiah Oghuan, Anuja Rajendra Godbole, Carlos A. Chavarria, Carlos Monserrat, Tao Hu, Yun Wang, Anthony W. Maresso, Blake M. Hanson, Kristina D. Mena, Fuqing Wu

Pharmacy Faculty Articles and Research

Domestic wastewater, when collected and evaluated appropriately, can provide valuable health-related information for a community. As a relatively unbiased and non-invasive approach, wastewater surveillance may complement current practices towards mitigating risks and protecting population health. Spurred by the COVID-19 pandemic, wastewater programs are now widely implemented to monitor viral infection trends in sewersheds and inform public health decision-making. This review summarizes recent developments in wastewater-based epidemiology for detecting and monitoring communicable infectious diseases, dissemination of antimicrobial resistance, and illicit drug consumption. Wastewater surveillance, a quickly advancing Frontier in environmental science, is becoming a new tool to enhance public health, improve …

Emulating Future Neurotechnology Using Magic, Jay A. Olson, Mariève Cyr, Despina Z. Artenie, Thomas Strandberg, Lars Hall, Matthew L. Tompkins, Amir Raz, Petter Johansson

Psychology Faculty Articles and Research

Recent developments in neuroscience and artificial intelligence have allowed machines to decode mental processes with growing accuracy. Neuroethicists have speculated that perfecting these technologies may result in reactions ranging from an invasion of privacy to an increase in self-understanding. Yet, evaluating these predictions is difficult given that people are poor at forecasting their reactions. To address this, we developed a paradigm using elements of performance magic to emulate future neurotechnologies. We led 59 participants to believe that a (sham) neurotechnological machine could infer their preferences, detect their errors, and reveal their deep-seated attitudes. The machine gave participants randomly assigned positive …

Arginine Methylation Of The Pgc-1Α C‑Terminus Is Temperature- Dependent, Meryl Mendoz, Mariel Mendoza, Tiffany Lubrino, Sidney Briski, Immaculeta Osuji, Janielle Cuala, Brendan Ly, Ivan Ocegueda, Harvey Peralta, Benjamin A. Garcia, Cecilia Zurita-Lopez

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

We set out to determine whether the C-terminus (amino acids 481–798) of peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α, UniProt Q9UBK2), a regulatory metabolic protein involved in mitochondrial biogenesis, and respiration, is an arginine methyltransferase substrate. Arginine methylation by protein arginine methyltransferases (PRMTs) alters protein function and thus contributes to various cellular processes. In addition to confirming methylation of the C-terminus by PRMT1 as described in the literature, we have identified methylation by another member of the PRMT family, PRMT7. We performed in vitro methylation reactions using recombinant mammalian PRMT7 and PRMT1 at 37, 30, 21, 18, and 4 °C. …

Crisisready's Novel Framework For Transdisciplinary Translation: Case-Studies In Wildfire And Hurricane Response, Andrew Schroeder, Caleb Dresser, Akash Yadav, Jennifer Chan, Shenyue Jia, Caroline Buckee, Satchit Balsari

Institute for ECHO Articles and Research

Extreme weather events including wildfires and hurricanes are becoming increasingly hazardous due to climate change, and often result in transient or permanent population displacements. Disaster-related disruptions in infrastructure, workforce, wages, and social networks can combine with population displacements to result in interruptions in health care access and prolonged impacts on morbidity and mortality. The data needed to make health systems and emergency management approaches more resilient to these hazards, and more responsive to the needs of affected populations, are sequestered in silos across private corporations and public agencies. In two case studies, we describe how our research team at CrisisReady …

Effects Of Cannabinoids On Ligand-Gated Ion Channels, Murat Oz, Keun-Hang Susan Yang, Mohamed Omer Mahgoub

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Phytocannabinoids such as Δ⁹-tetrahydrocannabinol and cannabidiol, endocannabinoids such as N-arachidonoylethanolamine (anandamide) and 2-arachidonoylglycerol, and synthetic cannabinoids such as CP47,497 and JWH-018 constitute major groups of structurally diverse cannabinoids. Along with these cannabinoids, CB1 and CB2 cannabinoid receptors and enzymes involved in synthesis and degradation of endocannabinoids comprise the major components of the cannabinoid system. Although, cannabinoid receptors are known to be involved in anti-convulsant, anti-nociceptive, anti-psychotic, anti-emetic, and anti-oxidant effects of cannabinoids, in recent years, an increasing number of studies suggest that, at pharmacologically relevant concentrations, these compounds interact with several molecular targets including G-protein coupled receptors, ion …

Probing Mechanisms Of Binding And Allostery In The Sars-Cov-2 Spike Omicron Variant Complexes With The Host Receptor: Revealing Functional Roles Of The Binding Hotspots In Mediating Epistatic Effects And Communication With Allosteric Pockets, Gennady M. Verkhivker, Steve Agajanian, Ryan Kassab, Keerthi Krishnan

Mathematics, Physics, and Computer Science Faculty Articles and Research

In this study, we performed all-atom MD simulations of RBD–ACE2 complexes for BA.1, BA.1.1, BA.2, and BA.3 Omicron subvariants, conducted a systematic mutational scanning of the RBD–ACE2 binding interfaces and analysis of electrostatic effects. The binding free energy computations of the Omicron RBD–ACE2 complexes and comprehensive examination of the electrostatic interactions quantify the driving forces of binding and provide new insights into energetic mechanisms underlying evolutionary differences between Omicron variants. A systematic mutational scanning of the RBD residues determines the protein stability centers and binding energy hotpots in the Omicron RBD–ACE2 complexes. By employing the ensemble-based global network analysis, we …

Interpretable Machine Learning Models For Molecular Design Of Tyrosine Kinase Inhibitors Using Variational Autoencoders And Perturbation-Based Approach Of Chemical Space Exploration, Keerthi Krishnan, Ryan Kassab, Steve Agajanian, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

In the current study, we introduce an integrative machine learning strategy for the autonomous molecular design of protein kinase inhibitors using variational autoencoders and a novel cluster-based perturbation approach for exploration of the chemical latent space. The proposed strategy combines autoencoder-based embedding of small molecules with a cluster-based perturbation approach for efficient navigation of the latent space and a feature-based kinase inhibition likelihood classifier that guides optimization of the molecular properties and targeted molecular design. In the proposed generative approach, molecules sharing similar structures tend to cluster in the latent space, and interpolating between two molecules in the latent space …

Subtype-Selective Positive Modulation Of KCa2.3 Channels Increases Cilia Length, Young-Woo Nam, Rajasekharreddy Pala, Naglaa Salem El-Sayed, Denisse Laren-Henriquez, Farideh Amirrad, Grace Yang, Mohammad Asikur Rahman, Razan Orfali, Myles Downey, Keykavous Parang, Surya M. Nauli, Miao Zhang

Pharmacy Faculty Articles and Research

Small-conductance Ca²⁺-activated potassium (K_Ca2.x) channels are gated exclusively by intracellular Ca²⁺. The activation of K_Ca2.3 channels induces hyperpolarization, which augments Ca²⁺ signaling in endothelial cells. Cilia are specialized Ca²⁺ signaling compartments. Here, we identified compound 4 that potentiates human K_Ca2.3 channels selectively. The subtype selectivity of compound 4 for human K_Ca2.3 over rat K_Ca2.2a channels relies on an isoleucine residue in the HA/HB helices. Positive modulation of K_Ca2.3 channels by compound 4 increased flow-induced Ca²⁺ signaling and cilia length, while negative …

Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors And Machine Learning Algorithms, Rahul Soangra, Yuxin Wen, Hualin Yang, Marybeth Grant-Beuttler

Physical Therapy Faculty Articles and Research

Idiopathic toe walking (ITW) is a gait abnormality in which children’s toes touch at initial contact and demonstrate limited or no heel contact throughout the gait cycle. Toe walking results in poor balance, increased risk of falling, and developmental delays among children. Identifying toe walking steps during walking can facilitate targeted intervention among children diagnosed with ITW. With recent advances in wearable sensing, communication technologies, and machine learning, new avenues of managing toe walking behavior among children are feasible. In this study, we investigate the capabilities of Machine Learning (ML) algorithms in identifying initial foot contact (heel strike versus toe …

Integrating Conformational Dynamics And Perturbation-Based Network Modeling For Mutational Profiling Of Binding And Allostery In The Sars-Cov-2 Spike Variant Complexes With Antibodies: Balancing Local And Global Determinants Of Mutational Escape Mechanisms, Gennady M. Verkhivker, Steve Agajanian, Ryan Kassab, Keerthi Krishnan

Mathematics, Physics, and Computer Science Faculty Articles and Research

n this study, we combined all-atom MD simulations, the ensemble-based mutational scanning of protein stability and binding, and perturbation-based network profiling of allosteric interactions in the SARS-CoV-2 spike complexes with a panel of cross-reactive and ultra-potent single antibodies (B1-182.1 and A23-58.1) as well as antibody combinations (A19-61.1/B1-182.1 and A19-46.1/B1-182.1). Using this approach, we quantify the local and global effects of mutations in the complexes, identify protein stability centers, characterize binding energy hotspots, and predict the allosteric control points of long-range interactions and communications. Conformational dynamics and distance fluctuation analysis revealed the antibody-specific signatures of protein stability and flexibility of the …

A Comparative Study On Deep Learning Models For Text Classification Of Unstructured Medical Notes With Various Levels Of Class Imbalance, Hongxia Lu, Louis Ehwerhemuepha, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Background

Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios.

Methods

In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical …

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 …