bluuebunny/crossref_metadata_2025_split huggingface dataset:
Topological data analysis model for the spread of the coronavirus
Authors: Chen Yiran , Volić Ismar 
Published: August 2020

AbstractWe apply topological data analysis, specifically the Mapper algorithm, to the U.S. COVID-19 data. The resulting Mapper graphs provide visualizations of the pandemic that are more complete than those supplied by other, more standard methods. They encode a variety of geometric features of the data cloud created from geographic information, time progression, and the number of COVID-19 cases. They reflect the development of the pandemic across all of the U.S. and capture the growth rates as well as the regional prominence of hot-spots. The Mapper graphs allow for easy comparisons across time and space and have the potential of becoming a useful predictive tool for the spread of the coronavirus.
Causal associations between COVID-19 and Atrial Fibrillation: A bidirectional Mendelian randomization study
Authors: Zhang Xiaoyu , Wang Biyan , Liu Di , Zhang Jinxia , Tian Qiuyue , Meng Xiaoni , Zhang Jie , Hou Haifeng , Song Manshu , Wang Wei , Wang Youxin 
Published: August 2020

AbstractBackgroundObservational studies showed that coronavirus disease 2019 (COVID-19) attacks universally and its most menacing progression uniquely endangers the elderly with cardiovascular disease (CVD). Whether COVID-19 is causally related to increasing susceptibility and severity of atrial fibrillation (AF), the main form of CVD, remains still unknown.MethodsThe study aims to investigate the bidirectional causal relations of COVID-19 with AF using two-sample Mendelian randomization (MR) analysis.ResultsMR evidence suggested genetically predicted severe COVID-19 was significantly associated with higher risk of AF (odds ratio [OR], 1.041; 95% confidence interval (CI), 1.007-1.076; P = 0.017), while genetically predicted AF was not causally associated with severe COVID-19 (OR, 0.831; 95% CI, 1.619-1.115; P=0.217). There was limited evidence to support association of genetically proxied COVID-19 with risk of AF (OR, 1.051; 95% CI, 0.991-1.114; P=0.097), and vice versa (OR, 0.163; 95% CI, 0.004-6.790; P=0.341). MR-Egger indicated no evidence of pleiotropic bias.ConclusionOverall, severe COVID-19 may causally affect AF through independent biological pathway. Survivors from severe COVID-19 might be of high risk of AF in the future.
Data Mining Approach to Analyze Covid19 Dataset of Brazilian Patients
Authors: Chire Josimar 
Published: August 2020

AbstractThe pandemic originated by coronavirus(covid19), name coined by World Health Organization during the first month in 2020. Actually, almost all the countries presented covid19 positive cases and governments are choosing different health policies to stop the infection and many research groups are working on patients data to understand the virus, at the same time scientists are looking for a vacuum to enhance imnulogy system to tack covid19 virus. One of top countries with more infections is Brazil, until August 11 had a total of 3,112,393 cases. Research Foundation of Sao Paulo State(Fapesp) released a dataset, it was an innovative in collaboration with hospitals(Einstein, Sirio-Libanes), laboratory(Fleury) and Sao Paulo University to foster reseach on this trend topic. The present paper presents an exploratory analysis of the datasets, using a Data Mining Approach, and some inconsistencies are found, i.e. NaN values, null references values for analytes, outliers on results of analytes, encoding issues. The results were cleaned datasets for future studies, but at least a 20% of data were discarded because of non numerical, null values and numbers out of reference range.
Mortality Associated With Intubation and Mechanical Ventilation in Patients with COVID-19
Authors: Zimmerman Patrick W. , Stroever Stephanie J. , Burton Timothy , Hester Karri E. , Kim Minha , Fahy Ryan T. , Corbitt Kimberly A. , Petrini Joann R. , Nicastro Jeffrey 
Published: August 2020

AbstractObjectiveThe need for critical care, hemodynamic support, renal replacement therapy, and mechanical ventilation have emerged as key features of the SARS-nCoV-2 (COVID-19) pandemic. The primary aim of this study was to determine the in-hospital mortality rate of mechanically ventilated patients. We also sought to determine the risk of in-hospital mortality by age, gender, race, ethnicity, and body mass index.MethodsWe performed a retrospective cohort study to determine the mortality rate among inpatient adults with COVID-19 on mechanical ventilation in the Nuvance Health system between March 1, 2020 and July 17, 2020. Patients were included if they were 18 years or older, had a laboratory confirmed COVID-19 diagnosis, were admitted to hospitals within the Nuvance Health network (7 hospitals), and were on mechanical ventilation at any time during their inpatient stay.ResultsOverall mortality in our cohort of 304 patients was 53.3%. Multivariable logistic regression including age, gender, race, ethnicity, and BMI demonstrated patients over 71 years old had greater risk of mortality compared to patients ages 61-70, and females had half the risk compared to males. There was no significant difference in risk of mortality given race, ethnicity, or BMI.ConclusionsIn adult patients with confirmed COVID-19 infection requiring mechanical ventilation and intensive care, advanced age (>71 years old) and male gender are associated with increased risk of mortality. This information contributes to a collective body of evidence to support ongoing planning and decision-making among clinicians and for directed infection prevention programming.
The landscape of childhood vaccine exemptions in the United States
Authors: Zipfel Casey M , Garnier Romain , Kuney Madeline , Bansal Shweta 
Published: August 2020

AbstractOnce-eliminated vaccine-preventable childhood diseases, such as measles, are resurging across the United States. Understanding the spatio-temporal trends in vaccine exemptions is crucial to targeting public health intervention to increase vaccine uptake and anticipating vulnerable populations as cases surge. However, prior available data on childhood disease vaccination is either on too rough a spatial scale for this spatially-heterogeneous issue, or is only available for small geographic regions, making general conclusions infeasible. Here, we have collated school vaccine exemption data across the United States and provide it at the county-level for all years available. We demonstrate the fine-scale spatial heterogeneity in vaccine exemption levels, and show that many counties may fall below the herd immunity threshold. We also show that vaccine exemptions increase over time in most states, and non-medical exemptions are highly prevalent where allowed. Our dataset also highlights the need for greater data sharing and standardized reporting across the United States.
Designed Variants of ACE2-Fc that Decouple Anti-SARS-CoV-2 Activities from Unwanted Cardiovascular Effects
Authors: Liu Pan , Xie Xinfang , Gao Li , Jin Jing 
Published: August 2020

AbstractAngiotensin-converting enzyme 2 (ACE2) is the entry receptor for SARS-CoV-2, and recombinant ACE2 decoys are being evaluated as new antiviral therapies. We designed and tested an antibody-like ACE2-Fc fusion protein, which has the benefit of long pharmacological half-life and the potential to facilitate immune clearance of the virus. Out of a concern that the intrinsic catalytic activity of ACE2 may unintentionally alter the balance of its hormonal substrates and cause adverse cardiovascular effects in treatment, we performed a mutagenesis screening for inactivating the enzyme. Three mutants, R273A, H378A and E402A, completely lost their enzymatic activity for either surrogate or physiological substrates. All of them remained capable of binding SARS-CoV-2 and could suppress the transduction of a pseudotyped virus in cell culture. This study established new ACE2-Fc candidates as antiviral treatment for SARS-CoV-2 without potentially harmful side effects from ACE2’s catalytic actions toward its vasoactive substrates.
A comparative analysis of chromatin accessibility in cattle, pig, and mouse tissues
Authors: Halstead Michelle M , Kern Colin , Saelao Perot , Wang Ying , Chanthavixay Ganrea , Medrano Juan F , Van Eenennaam Alison L , Korf Ian , Tuggle Christopher K , Ernst Catherine W , Zhou Huaijun , Ross Pablo J 
Published: August 2020

AbstractBackgroundAlthough considerable progress has been made towards annotating the noncoding portion of the human and mouse genomes, regulatory elements in other species, such as livestock, remain poorly characterized. This lack of functional annotation poses a substantial roadblock to agricultural research and diminishes the value of these species as model organisms. As active regulatory elements are typically characterized by chromatin accessibility, we implemented the Assay for Transposase Accessible Chromatin (ATAC-seq) to annotate and characterize regulatory elements in pigs and cattle, given a set of eight adult tissues.ResultsOverall, 306,304 and 273,594 active regulatory elements were identified in pig and cattle, respectively. 71,478 porcine and 47,454 bovine regulatory elements were highly tissue-specific and were correspondingly enriched for binding motifs of known tissue-specific transcription factors. However, in every tissue the most prevalent accessible motif corresponded to the insulator CTCF, suggesting pervasive involvement in 3-D chromatin organization. Taking advantage of a similar dataset in mouse, open chromatin in pig, cattle, and mice were compared, revealing that the conservation of regulatory elements, in terms of sequence identity and accessibility, was consistent with evolutionary distance; whereas pig and cattle shared about 20% of accessible sites, mice and ungulates only had about 10% of accessible sites in common. Furthermore, conservation of accessibility was more prevalent at promoters than at intergenic regions.ConclusionsThe lack of conserved accessibility at distal elements is consistent with rapid evolution of enhancers, and further emphasizes the need to annotate regulatory elements in individual species, rather than inferring elements based on homology. This atlas of chromatin accessibility in cattle and pig constitutes a substantial step towards annotating livestock genomes and dissecting the regulatory link between genome and phenome.
Pandemic danger to the deep: the risk of marine mammals contracting SARS-CoV-2 from wastewater
Authors: Mathavarajah Sabateeshan , Stoddart Amina K. , Gagnon Graham A. , Dellaire Graham 
Published: August 2020

AbstractWe are in unprecedented times with the ongoing COVID-19 pandemic. The pandemic has impacted public health, the economy and our society on a global scale. In addition, the impacts of COVID-19 permeate into our environment and wildlife as well. Here, we discuss the essential role of wastewater treatment and management during these times. A consequence of poor wastewater management is the discharge of untreated wastewater carrying infectious SARS-CoV-2 into natural water systems that are home to marine mammals. Here, we predict the susceptibility of marine mammal species using a modelling approach. We identified that many species of whale, dolphin and seal, as well as otters, are predicted to be highly susceptible to infection by the SARS-CoV-2 virus. In addition, geo-mapping highlights how current wastewater management in Alaska may lead to susceptible marine mammal populations being exposed to the virus. Localities such as Cold Bay, Naknek, Dillingham and Palmer may require additional treatment of their wastewater to prevent virus spillover through sewage. Since over half of these susceptibility species are already at risk worldwide, the release of the virus via untreated wastewater could have devastating consequences for their already declining populations. For these reasons, we discuss approaches that can be taken by the public, policymakers and wastewater treatment facilities to reduce the risk of virus spillover in our natural water systems. Thus, we indicate the potential for reverse zoonotic transmission of COVID-19 and its impact on marine wildlife; impacts that can be mitigated with appropriate action to prevent further damage to these vulnerable populations.
In SilicoDesign of siRNAs Targeting Existing and Future Respiratory Viruses with VirusSi
Authors: Zhang Dingyao , Lu Jun 
Published: August 2020

AbstractThe COVID-19 pandemic has exposed global inadequacies in therapeutic options against both the COVID-19-causing SARS-CoV-2 virus and other newly emerged respiratory viruses. In this study, we present the VirusSi computational pipeline, which facilitates the rational design of siRNAs to target existing and future respiratory viruses. Mode A of VirusSi designs siRNAs against an existing virus, incorporating considerations on siRNA properties, off-target effects, viral RNA structure and viral mutations. It designs multiple siRNAs out of which the top candidate targets >99% of SARS-CoV-2 strains, and the combination of the top four siRNAs is predicted to target all SARS-CoV-2 strains. Additionally, we develop Greedy Algorithm with Redundancy (GAR) and Similarity-weighted Greedy Algorithm with Redundancy (SGAR) to support the Mode B of VirusSi, which pre-designs siRNAs against future emerging viruses based on existing viral sequences. Time-simulations using known coronavirus genomes as early as 10 years prior to the COVID-19 outbreak show that at least three SARS-CoV-2-targeting siRNAs are among the top 30 pre-designed siRNAs. Before-the-outbreak pre-design is also possible against the MERS-CoV virus and the 2009-H1N1 swine flu virus. Our data support the feasibility of pre-designing anti-viral siRNA therapeutics prior to viral outbreaks. We propose the development of a collection of pre-designed, safety-tested, and off-the-shelf siRNAs that could accelerate responses toward future viral diseases.
msiPL: Non-linear Manifold and Peak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks
Authors: Abdelmoula Walid M. , Lopez Begona Gimenez-Cassina , Randall Elizabeth C. , Kapur Tina , Sarkaria Jann N. , White Forest M. , Agar Jeffrey N. , Wells William M. , Agar Nathalie Y.R. 
Published: August 2020

AbstractMass spectrometry imaging (MSI) is an emerging technology that holds potential for improving clinical diagnosis, biomarker discovery, metabolomics research and pharmaceutical applications. The large data size and high dimensional nature of MSI pose computational and memory complexities that hinder accurate identification of biologically-relevant molecular patterns. We propose msiPL, a robust and generic probabilistic generative model based on a fully-connected variational autoencoder for unsupervised analysis and peak learning of MSI data. The method can efficiently learn and visualize the underlying non-linear spectral manifold, reveal biologically-relevant clusters of tumor heterogeneity and identify underlying informative m/z peaks. The method provides a probabilistic parametric mapping to allow a trained model to rapidly analyze a new unseen MSI dataset in a few seconds. The computational model features a memory-efficient implementation using a minibatch processing strategy to enable the analyses of big MSI data (encompassing more than 1 million high-dimensional datapoints) with significantly less memory. We demonstrate the robustness and generic applicability of the application on MSI data of large size from different biological systems and acquired using different mass spectrometers at different centers, namely: 2D Matrix-Assisted Laser Desorption Ionization (MALDI) Fourier Transform Ion Cyclotron Resonance (FT ICR) MSI data of human prostate cancer, 3D MALDI Time-of-Flight (TOF) MSI data of human oral squamous cell carcinoma, 3D Desorption Electrospray Ionization (DESI) Orbitrap MSI data of human colorectal adenocarcinoma, 3D MALDI TOF MSI data of mouse kidney, and 3D MALDI FT ICR MSI data of a patient-derived xenograft (PDX) mouse brain model of glioblastoma.SignificanceMass spectrometry imaging (MSI) provides detailed molecular characterization of a tissue specimen while preserving spatial distributions. However, the complex nature of MSI data slows down the processing time and poses computational and memory challenges that hinder the analysis of multiple specimens required to extract biologically relevant patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Here, we present a generative probabilistic deep-learning model that can analyze and non-linearly visualize MSI data independent of the nature of the specimen and of the MSI platform. We demonstrate robustness of the method with application to different tissue types, and envision it as a new generation of rapid and robust analysis for mass spectrometry data.
The Automated Optimisation of a Coarse-Grained Force Field Using Free Energy Data
Authors: Caceres-Delpiano Javier , Wang Lee-Ping , Essex Jonathan W. 
Published: August 2020

AbstractAtomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number of degrees of freedom, at the cost of reduced accuracy. New optimisation processes to parameterise these models could improve their quality and range of applicability. We present an automated approach for the optimisation of coarse-grained force fields, by reproducing free energy data derived from atomistic molecular simulations. To illustrate the approach, we implemented hydration free energy gradients as a new target for force field optimisation in ForceBalance and applied it successfully to optimise the un-charged side-chains and the protein backbone in the SIRAH protein coarse-grain force field. The optimised parameters closely reproduced hydration free energies of atomistic models and gave improved agreement with experiment.
Standing on elevated platform changes postural responses during arm movement
Authors: Mochizuki Luis , Pennone Juliana , Bigongiari Aline , Cosme Renata Garrido , Cajueiro Monique Oliveira Baptista , Amadio Alberto Carlos 
Published: August 2020

AbstractThis study investigated the muscle activity during the preparatory (anticipatory postural adjustment, APA), execution (online postural adjustments, OPA), and compensatory (compensatory postural adjustment, CPA) phases during standing with eyes opened or closed on an elevated platform. Eight healthy young women stood in the upright position, with eyes opened or closed, and did as-fast-as-they-could shoulder flexions on the ground and on 1-m-height-portable-elevated-platform. The surface EMG of trunk (lumbar extensor, and rectus abdominis) and lower limb (rectus femoris, biceps femoris, tibialis anterior and gastrocnemius lateralis) muscles during this task were recorded (1 kHz sampling frequency) and compared during these three phases. Analysis of variance was applied to compare the effects of height (floor and elevated platform), vision (open and closed), and postural adjustment (APA, OPA and CPA) into the activity of each muscle. These muscles were more active during OPA (p<0.0001) and less active during APA. On the elevated platform, these postural muscles presented more activty during APA (p<0.001). During the most stable condition (on the ground with eyes opened), muscle activity during APA and OPA was negatively correlated, and not correlated between OPA and CPA. Our results suggest postural control adapts to sensory, motor, and cognitive conditions. Therefore, the increased demand for postural control, generated due to the height of the support base, provokes the need for greater flexibility of postural synergies and causes a change in muscle activity.Summary StatementWe discuss how postural muscle activity behaves before and after a fast upper arms movement when someone stands on a elevated platform or on the ground.
Use of Therapeutic Drug Monitoring to Characterize Cefepime-Induced Neurotoxicity
Authors: Venugopalan Veena , Nys Cara , Hurst Natalie , Chen Yiqing , Bruzzone Maria , Cherabuddi Kartikeya , Iovine Nicole , Liu Jiajun , Al-Shaer Mohammad H. , Scheetz Marc H. , Rhodes Nathaniel , Peloquin Charles A. , Klinker Kenneth 
Published: August 2020

AbstractBackgroundThe incidence of cefepime-induced neurotoxicity (CIN) in hospitalized patients is highly variable. Although greater cefepime exposures incite neurotoxicity, data evaluating trough thresholds associated with CIN remains limited. The objectives of this study were to evaluate the incidence of CIN, assess the relationship between cefepime trough concentrations and CIN, investigate clinical factors associated with CIN, and describe electroencephalogram (EEG) abnormalities in CIN.MethodsThis was a retrospective study of adult patients who had received ≥ 5 days of cefepime with ≥ 1 trough concentration > 25 mg/L. Potential CIN cases were identified utilizing neurological symptoms, neurologist assessments, EEG findings and improvement of neurotoxicity after cefepime discontinuation.ResultsOne-hundred and forty-two patients were included. The incidence of CIN was 13% (18/142). The mean cefepime trough concentration in CIN patients was significantly greater than the non-neurotoxicity group (74.2 mg/L ± 41.1 vs. 46.6 mg/L ± 23, p=0.015). Lower renal function (creatinine clearance < 30 ml/min), greater time to therapeutic drug monitoring (TDM) (≥72 hours), and each 1 mg/mL rise in cefepime trough were independently associated with increased risk of CIN. Moderate generalized slowing of the background rhythm was the most common EEG pattern associated with CIN.ConclusionCefepime should be used cautiously in hospitalized patients due to the risk of neurotoxicity. Patients with greater renal function and those who had early cefepime TDM (≤ 72 hours) had lower risk of CIN.