Physical Sciences and Mathematics Commons^™

Exploring Binding Pockets In The Conformational States Of The Sars-Cov-2 Spike Trimers For The Screening Of Allosteric Inhibitors Using Molecular Simulations And Ensemble-Based Ligand Docking, Grace Gupta, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Understanding mechanisms of allosteric regulation remains elusive for the SARS-CoV-2 spike protein, despite the increasing interest and effort in discovering allosteric inhibitors of the viral activity and interactions with the host receptor ACE2. The challenges of discovering allosteric modulators of the SARS-CoV-2 spike proteins are associated with the diversity of cryptic allosteric sites and complex molecular mechanisms that can be employed by allosteric ligands, including the alteration of the conformational equilibrium of spike protein and preferential stabilization of specific functional states. In the current study, we combine conformational dynamics analysis of distinct forms of the full-length spike protein trimers and …

Cardiogpt: An Ecg Interpretation Generation Model, Guohua Fu, Jianwei Zheng, Islam Abudayyeh, Chizobam Ani, Cyril Rakovski, Louis Ehwerhemuepha, Hongxia Lu, Yongjuan Guo, Shenglin Liu, Huimin Chu, Bing Yang

Mathematics, Physics, and Computer Science Faculty Articles and Research

Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai …

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 …

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 …

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 …

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 …

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. …

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 …

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 …

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 …

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 + …

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 …

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 …

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 …

A Multicenter Mixed-Effects Model For Inference And Prediction Of 72-H Return Visits To The Emergency Department For Adult Patients With Trauma-Related Diagnoses, Ehsan Yaghmaei, Louis Ehwerhemuepha, William Feaster, David Gibbs, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Objective

Emergency department (ED) return visits within 72 h may be a sign of poor quality of care and entail unnecessary use of healthcare resources. In this study, we compare the performance of two leading statistical and machine learning classification algorithms, and we use the best performing approach to identify novel risk factors of ED return visits.

Methods

We analyzed 3.2 million ED encounters with at least one diagnosis under “injury, poisoning and certain other consequences of external causes” and “external causes of morbidity.” These encounters included patients 18 years or older from across 128 emergency room facilities in the …

A 12-Lead Ecg Database To Identify Origins Of Idiopathic Ventricular Arrhythmia Containing 334 Patients, Jianwei Zhang, Guohua Fu, Kyle Anderson, Huimin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Cardiac catheter ablation has shown the effectiveness of treating the idiopathic premature ventricular complex and ventricular tachycardia. As the most important prerequisite for successful therapy, criteria based on analysis of 12-lead ECGs are employed to reliably speculate the locations of idiopathic ventricular arrhythmia before a subsequent catheter ablation procedure. Among these possible locations, right ventricular outflow tract and left outflow tract are the major ones. 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 …

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 …

Establishing Computational Approaches Towards Identifying Malarial Allosteric Modulators: A Case Study Of Plasmodium Falciparum Hsp70s, Arnold Amusengeri, Lindy Astl, Kevin Lobb, Gennady M. Verkhivker, Özlem Tastan Bishop

Mathematics, Physics, and Computer Science Faculty Articles and Research

Combating malaria is almost a never-ending battle, as Plasmodium parasites develop resistance to the drugs used against them, as observed recently in artemisinin-based combination therapies. The main concern now is if the resistant parasite strains spread from Southeast Asia to Africa, the continent hosting most malaria cases. To prevent catastrophic results, we need to find non-conventional approaches. Allosteric drug targeting sites and modulators might be a new hope for malarial treatments. Heat shock proteins (HSPs) are potential malarial drug targets and have complex allosteric control mechanisms. Yet, studies on designing allosteric modulators against them are limited. Here, we identified allosteric …

Coccidioidomycosis: Medical And Spatio-Temporal Perspectives, Nikias Sarafoglou, Rafael Laniado-Laborin, Menas Kafatos

Mathematics, Physics, and Computer Science Faculty Articles and Research

Coccidioidomycosis (CM) is a disease of major public health importance due to the challenges in its diagnosis and treatment. To understand CM requires the attributes of a multidisciplinary network analysis to appreciate the complexity of the medical, the environmental and the social issues involved: public health, public policy, geology, atmospheric science, agronomy, social sciences and finally humanities, all which provide insight into this population transformation.

In section 1 of this paper, we describe the CM-epidemiology, the clinical features, the diagnosis and finally the treatment.

In section 2, we highlight the most important contributions and controversies in the history of the …