bluuebunny/crossref_metadata_2025_split huggingface dataset:
IgG Antibodies against SARS-CoV-2 Correlate with Days from Symptom Onset, Viral Load and IL-10
Authors: Young Mary K. , Kornmeier Christine , Carpenter Rebecca M. , Natale Nick R. , Sasson Jennifer M. , Solga Michael D. , Mathers Amy J. , Poulter Melinda D. , Qiang Xiao , Petri William A. 
Published: December 2020

AbstractThe emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a pandemic of the respiratory disease coronavirus disease 2019 (COVID-19). Antibody testing is essential to identify persons exposed to the virus and potentially in predicting disease immunity. 183 COVID-19 patients (68 of whom required mechanical ventilation) and 41 controls were tested for plasma IgG, IgA and IgM against the SARS-CoV-2 S1, S2, receptor binding domain (RBD) and N proteins using the MILLIPLEX® SARS-CoV-2 Antigen Panel. Plasma cytokines were concurrently measured using the MILLIPLEX® MAP Human Cytokine/Chemokine/Growth Factor Panel A. As expected the 183 COVID-19 positive patients had high levels of IgG, IgA and IgM anti-SARS-CoV-2 antibodies against each of the viral proteins. Sensitivity of anti-S1 IgG increased from 60% to 93% one week after symptom onset. S1-IgG and S1-IgA had specificities of 98% compared to the 41 COVID-19 negative patients. The 68 ventilated COVID-19 positive patients had higher antibody levels than the 115 COVID-19 positive patients who were not ventilated. IgG antibody levels against S1 protein had the strongest positive correlation to days from symptom onset. There were no statistically significant differences in IgG, IgA and IgM antibodies against S1 based on age. We found that patients with the highest levels of anti-SARS-CoV-2 antibodies had the lowest viral load in the nasopharynx. Finally there was a correlation of high plasma IL-10 with low anti-SARS-CoV-2 antibodies. Anti-SARS-CoV-2 antibody levels, as measured by a novel antigen panel, increased within days after symptom onset, achieving > 90% sensitivity and specificity within one week, and were highest in patients who required mechanical ventilation. Antibody levels were inversely associated with viral load but did not differ as a function of age. The correlation of high IL-10 with low antibody response suggests a potentially suppressive role of this cytokine in the humoral immune response in COVID-19.
YieldNet: A Convolutional Neural Network for Simultaneous Corn and Soybean Yield Prediction Based on Remote Sensing Data
Authors: Khaki Saeed , Pham Hieu , Wang Lizhi 
Published: December 2020

AbstractLarge scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout its growth state. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts yield of multiple crops and concurrently considers the interaction between multiple crop’s yield. We propose a new model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. Numerical results demonstrate that our proposed method accurately predicts yield from one to four months before the harvest, and is competitive to other state-of-the-art approaches.
Epidemic Progression and Vaccination in a Heterogeneous Population. Application to the Covid-19 epidemic
Authors: Volpert Vitaly , Banerjee Malay , Sharma Swarnali 
Published: December 2020

AbstractThe paper is devoted to a compartmental epidemiological model of infection progression in a heterogeneous population which consists of two groups with high disease transmission (HT) and low disease transmission (LT) potentials. Final size and duration of epidemic, the total and current maximal number of infected individuals are estimated depending on the structure of the population. It is shown that with the same basic reproduction numberR0in the beginning of epidemic, its further progression depends on the ratio between the two groups. Therefore, fitting the data in the beginning of epidemic and the determination ofR0are not sufficient to predict its long time behaviour. Available data on the Covid-19 epidemic allows the estimation of the proportion of the HT and LT groups. Estimated structure of the population is used for the investigation of the influence of vaccination on further epidemic development. The result of vaccination strongly depends on the proportion of vaccinated individuals between the two groups. Vaccination of the HT group acts to stop the epidemic and essentially decreases the total number of infected individuals at the end of epidemic and the current maximal number of infected individuals while vaccination of the LT group only acts to protect vaccinated individuals from further infection.
Comparing Decision Tree-Based Ensemble Machine Learning Models for COVID-19 Death Probability Profiling
Authors: Gonçalves Carlos Pedro , Rouco José 
Published: December 2020

AbstractWe compare the performance of major decision tree-based ensemble machine learning models on the task of COVID-19 death probability prediction, conditional on three risk factors: age group, sex and underlying comorbidity or disease, using the US Centers for Disease Control and Prevention (CDC)’s COVID-19 case surveillance dataset. To evaluate the impact of the three risk factors on COVID-19 death probability, we extract and analyze the conditional probability profile produced by the best performer. The results show the presence of an exponential rise in death probability from COVID-19 with the age group, with males exhibiting a higher exponential growth rate than females, an effect that is stronger when an underlying comorbidity or disease is present, which also acts as an accelerator of COVID-19 death probability rise for both male and female subjects. The results are discussed in connection to healthcare and epidemiological concerns and in the degree to which they reinforce findings coming from other studies on COVID-19.
Credible learning of hydroxychloroquine and dexamethasone effects on COVID-19 mortality outside of randomized trials
Authors: Hazlett Chad , Wulf David Ami , Pasaniuc Bogdan , Arah Onyebuchi A. , Erlandson Kristine M. , Montague Brian T. 
Published: December 2020

AbstractObjectivesTo investigate the effectiveness of hydroxychloroquine and dexamethasone on coronavirus disease (COVID-19) mortality using patient data outside of randomized trials.DesignPhenotypes derived from electronic health records were analyzed using the stability-controlled quasi-experiment (SCQE) to provide a range of possible causal effects of hydroxy-chloroquine and dexamethasone on COVID-19 mortality.Setting and participantsData from 2,007 COVID-19 positive patients hospitalized at a large university hospital system over the course of 200 days and not enrolled in randomized trials were analyzed using SCQE. For hyrdoxychloroquine, we examine a high-use cohort (n=766, days 1 to 43) and a later, low-use cohort (n=548, days 44 to 82). For dexamethasone, we examine a low-use cohort (n=614, days 44 to 101) and high-use cohort (n=622, days 102 to 200).Outcome measure14-day mortality, with a secondary outcome of 28-day mortality.ResultsHydroxycholoroquine could only have been significantly (p<0.05) beneficial if baseline mortality was at least 6.4 percentage points (55%) lower among patients in the later (low-use) than the earlier (high-use) cohort. Hydroxychloroquine instead proves significantly harmful if baseline mortality rose from one cohort to the next by just 0.3 percentage points. Dexamethasone significantly reduced mortality risk if baseline mortality in the later (high-use) cohort (days 102-200) was higher than, the same as, or up to 1.5 percentage points lower than that in the earlier (low-use) cohort (days 44-101). It could only prove significantly harmful if mortality improved from one cohort to the next by 6.8 percentage points due to other causes—an assumption implying an unlikely 84% reduction in mortality due to other causes, leaving an in-hospital mortality rate of just 1.3%.ConclusionsThe assumptions required for a beneficial effect of hydroxychloroquine on 14 day mortality are difficult to sustain, while the assumptions required for hydroxychloroquine to be harmful are difficult to reject with confidence. Dexamethasone, by contrast, was beneficial under a wide range of plausible assumptions, and was only harmful if a nearly impossible assumption is met. More broadly, the SCQE reveals what inferences can be credibly supported by evidence from non-randomized uses of experimental therapies, making it a useful tool when randomized trials have not yet produced clear evidence or to provide corroborative evidence from different populations.
Persistent Homology of Tumor CT Scans is Associated with Survival In Lung Cancer
Authors: Somasundaram Eashwar , Litzler Adam , Wadhwa Raoul R. , Barker-Clarke Rowan , Scott Jacob G. 
Published: December 2020

ABSTRACTPurposeRadiomics, the objective study of non-visual features in clinical imaging, has been useful in informing decisions in clinical oncology. However, radiomics currently lacks the ability to characterize the overall topological structure of the data. This niche can be filled by persistent homology, a form of topological data analysis that analyzes high-level structure. We hypothesized that persistent homology could be applied to lung tumor scans and analyze clinical outcomes.MethodsWe obtained segmented computed tomography lung scans (n = 565) from the NSCLC-Radiomics and NSCLC-Radiogenomics datasets in The Cancer Imaging Archive. For each scan, a cubical complex filtration based on Hounsfield units was generated. We calculated a feature curve that plotted the number of 0 dimensional topological features against each Hounsfield unit. This curve’s first moment of distribution was utilized as a summary statistic to predict survival in a Cox proportional hazards model.ResultsAfter controlling for tumor image size, age, and stage, the first moment of the 0D topological feature curve was associated with poorer survival (HR = 1.118; 95% CI = 1.026-1.218; p = 0.01). The patients in our study with the lowest first moment scores had significantly better survival (1238 days; 95% CI = 936-1599) compared to the patients with the highest first moment scores (429 days; 95% CI = 326-601; p = .0015).ConclusionsWe have shown that persistent homology can generate useful clinical correlates from tumor CT scans. Our 0-dimensional topological feature curve statistic predicts survival in lung cancer patients. This novel statistic may be used in tandem with standard radiomics variables to better inform clinical oncology decisions.
Assessing the feasibility of Nipah vaccine efficacy trials based on previous outbreaks in Bangladesh
Authors: Nikolay Birgit , Lipsitch Marc , Rahman Mahmudur , Luby Stephen P. , Salje Henrik , Gurley Emily S. , Cauchemez Simon 
Published: December 2020

AbstractBackgroundNipah virus (NiV) is an emerging, bat-borne pathogen that can be transmitted from person-to-person. Vaccines are currently being developed for NiV, and studies funded to evaluate their safety and immunogenicity, so that they could possibly be used to contain outbreaks. An important unanswered question is whether it will be possible to evaluate the efficacy of vaccine candidates in phase III clinical trials in a context where spillovers from the zoonotic reservoir are infrequent and associated with small outbreaks. The objective of this study was to investigate the feasibility of conducting a phase III vaccine trial in Bangladesh, the only country reporting regularly NiV cases.MethodsWe used simulations based on previously observed NiV cases from Bangladesh, an assumed vaccine efficacy of 90% and other NiV vaccine target characteristics, to compare three vaccination study designs: (i) cluster randomized ring vaccination, (ii) cluster randomized mass vaccination, and (iii) an observational case-control study design.ResultsThe simulations showed that, assuming a ramp-up period of 10 days and a mean hospitalization delay of 4 days, it would take 516 years and over 163,000 vaccine doses to run a ring vaccination trial under current epidemic conditions. A cluster-randomized trial in the two most affected districts would take 81 years and 2.3 million vaccine doses. An observational case-control design in these two districts would require seven years and 2.5 million vaccine doses.DiscussionWithout a change in the epidemiology of NiV, ring vaccination or cluster-randomized trials are unlikely to be completed within a reasonable time window. In this light, the remaining options are: (i) not conducting a phase III trial until the epidemiology of NiV changes, (ii) identifying alternative ways to licensure such as observational studies or controlled studies in animals such as in the US Food and Drug Administration’s (FDA) Animal Rule.
Using Relion software within Scipion framework
Authors: Sharov Grigory , Morado Dustin R. , Carroni Marta , de la Rosa-Trevín José Miguel 
Published: December 2020

Scipion is a modular image processing framework integrating several software packages under a unified interface while taking care of file formats and conversions. Here new developments and capabilities of the Scipion plugin for the Relion software are presented and illustrated with the image processing pipeline of published data. The user interfaces of Scipion and Relion are compared and the key differences highlighted, allowing this manuscript to be used as a guide for both new and experienced users of these software. Different streaming image processing options are also discussed demonstrating the flexibility of the Scipion framework.SynopsisAn overview of the Scipion plugin for the Relion software is presented and various capabilities of image processing within Scipion framework are discussed.
Efficient inhibition of SARS-CoV-2 strains by a novel ACE2-IgG4-Fc fusion protein with a stabilized hinge region
Authors: Svilenov Hristo L. , Sacherl Julia , Reiter Alwin , Wolff Lisa , Chen Cho-Chin , Stern Marcel , Wachs Frank-Peter , Simonavicius Nicole , Pippig Susanne , Wolschin Florian , Buchner Johannes , Brockmeyer Carsten , Protzer Ulrike 
Published: December 2020

AbstractThe novel severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2) enters its host cells after binding to the angiotensin-converting enzyme 2 (ACE2)viaits spike glycoprotein. This interaction is critical for virus entry and virus-host membrane fusion. Soluble ACE2 ectodomains bind and neutralize the virus but the shortin vivohalf-lives of soluble ACE2 limits its therapeutic use. Fusion of the fragment crystallizable (Fc) part of human immunoglobulin G (IgG) to the ACE2 ectodomain can prolong thein vivohalf-life but bears the risk of unwanted Fc-receptor activation and antibody-dependent disease enhancement. Here, we describe optimized ACE2-Fc fusion constructs that avoid Fc-receptor binding by using IgG4-Fc as a fusion partner. The engineered ACE2-IgG4-Fc fusion proteins described herein exhibit promising pharmaceutical properties and a broad antiviral activity at single-digit nanomolar concentration. In addition, they allow to maintain the beneficial enzymatic activity of ACE2 and thus are very promising candidate antivirals broadly acting against coronaviruses.
Deciphering spatio-temporal fluorescence changes using multi-threshold event detection (MTED)
Authors: Müller Franziska E. , Cherkas Volodymyr , Stopper Gebhard , Caudal Laura C. , Stopper Laura , Kirchhoff Frank , Henneberger Christian , Ponimaskin Evgeni G. , Zeug Andre 
Published: December 2020

AbstractRecent achievements in indicator optimization and imaging techniques promote the exploration of Ca2+ activity patterns as a main second messenger in many organs. Astrocytes are important regulators of brain activity and well known for their Ca2+-dependent modulation of neurons. However, standardized methods to analyze and interpret Ca2+ activity recordings are missing and hindering global comparisons. Here, we present a biophysics-based concept to analyze Ca2+signals, which includes multiple thresholds and provides the experimenter with a comprehensive toolbox for a differentiated and in-depth characterization of Ca2+ signals. We analyzed various ex vivo and in vivo imaging datasets and verify the validity of our multi-threshold event detection (MTED) algorithm across Ca2+ indicators, imaging setups, and model systems from primary cell culture to awake, head-fixed mice. Applying our MTED concept enables standardized analysis and advances research using optical readouts of cellular activity to decrypt brain function. It allowed us to obtain new insights into the complex dependence of Ca2+activity patterns on temperature and neuronal activity.Highlights→We present a robust pixel-based algorithm to analyze multidimensional fluorescence data.→Automated multiple-threshold analysis accurately quantifies changes in fluorescence across magnitudes.→It characterizes the complexity of dynamic and overlapping activity patterns of Ca2+ activity of astrocytes in vitro, in situ, and in vivo.→Its application provides quantitative parameters how temperature and neuronal activity determine astrocytic Ca2+ activity.
Machine learning for analysis of active stand responses in older adults with vasovagal syncope
Authors: Kwok Michelle , Nolan Hugh , Fan Chie Wei , O’Dwyer Clodagh , Kenny Rose A , Finucane Ciarán 
Published: December 2020

AbstractObjectivesTo assess 1) differences in the hemodynamic response to the active stand test in older adults with a clinical diagnosis of vasovagal syncope compared to age-matched controls 2) if the active stand test combined with machine learning approaches can be used to identify the presence of vasovagal syncope in older adults.ApproachAdults aged 50 and over (Vasovagal Syncope N=46 Age=66.9±10.3; Control N=86 Age=65.3±9.5) completed an active stand test. Multiple features were extracted to characterize the hemodynamic responses to the active stand test and were compared between groups. Classification was performed using machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, support vector machine and an ensemble majority vote classifier.Main ResultsSubjects with vasovagal syncope demonstrated a higher resting (supine) heart rate (69.8±13.1 bpm vs 63.3±12.1 bpm; P=0.007), a smaller initial systolic blood pressure drop (−20.2±20.1% vs −27.3±17.5%; P=0.005), larger drops in stroke volume (−14.7±24.0% vs −2.7±23.3%; P=0.010) and cardiac output (−6.4±18.5% vs 5.8±22.3%;P<0.001) and a larger increase in total peripheral resistance (8.1±30.4% vs −6.03±22.8%; P=0.002) compared to controls. A majority vote classifier identified the presence of vasovagal syncope with 82.6% sensitivity, 76.8% specificity, and average accuracy of 78.9%.SignificanceOlder adults with vasovagal syncope display a unique hemodynamic and autonomic response to active standing characterized by relative autonomic hypersensitivity and larger drops in cardiac output compared to age-matched controls. With suitable machine learning algorithms, the active stand test holds the potential to be used to screen older adults for reflex syncopes and hypotensive susceptibility potentially reducing test time, cost, and patient discomfort. More broadly this paper presents a machine learning framework to support use of the active stand test for classification of clinical outcomes of interest.
A Particle-Based COVID-19 Simulator with Contact Tracing and Testing
Authors: Kuzdeuov Askat , Karabay Aknur , Baimukashev Daulet , Ibragimov Bauyrzhan , Varol Huseyin Atakan 
Published: December 2020

AbstractGoalThe COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of January 2021, there are more than 100 million cases and 2.1 million deaths. For informed decision making, reliable statistical data and capable simulation tools are needed. Our goal is to develop an epidemic simulator that can model the effects of random population testing and contact tracing.MethodsOur simulator models individuals as particles with the position, velocity, and epidemic status states on a 2D map and runs an SEIR epidemic model with contact tracing and testing modules. The simulator is available on GitHub under the MIT license.ResultsThe results show that the synergistic use of contact tracing and massive testing is effective in suppressing the epidemic (the number of deaths was reduced by 72%).ConclusionsThe Particle-based COVID-19 simulator enables the modeling of intervention measures, random testing, and contact tracing, for epidemic mitigation and suppression.Impact StatementOur particle-based epidemic simulator, calibrated with COVID-19 data, models each individual as a unique particle with a location, velocity, and epidemic state, enabling the consideration of contact tracing and testing measures.
Lack of antibodies against seasonal coronavirus OC43 nucleocapsid protein identifies patients at risk of critical COVID-19
Authors: Dugas Martin , Grote-Westrick Tanja , Merle Uta , Fontenay Michaela , Kremer Andreas E. , Hanses Frank , Vollenberg Richard , Lorentzen Eva , Tiwari-Heckler Shilpa , Duchemin Jérôme , Ellouze Syrine , Vetter Marcel , Fürst Julia , Schuster Philipp , Brix Tobias , Denkinger Claudia M. , Müller-Tidow Carsten , Schmidt Hartmut , Tepasse Phil-Robin , Kühn Joachim 
Published: December 2020

AbstractMost COVID-19 patients experience a mild disease; a minority suffers from critical disease.We report about a biomarker validation study regarding 296 patients with confirmed SARS-CoV-2 infections from four tertiary care referral centers in Germany and France.Patients with critical disease had significantly less anti-HCoV OC43 nucleocapsid protein antibodies compared to other COVID-19 patients (p=0.007). In multivariate analysis, OC43 negative inpatients had an increased risk of critical disease, higher than the risk by increased age or BMI, and lower than the risk by male sex. A risk stratification based on sex and OC43 serostatus was derived from this analysis.Our results indicate that prior infections with seasonal human coronaviruses can protect against a severe course of COVID-19. Anti-OC43 antibodies should be measured for COVID-19 inpatients and considered as part of the risk assessment. We expect individuals tested negative for anti-OC43 antibodies to particularly benefit from vaccination, especially with other risk factors prevailing.