bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimation of the infection fatality rate and the total number of SARS-CoV-2 infections
Authors: Hernandez-Suarez Carlos , Verme Paolo , Murillo-Zamora Efren 
Published: April 2020

AbstractWe introduce a simple methodology to estimate the infection fatality rate (IFR) and from here the total number of infected with SARS-CoV-2. The virus has shown to be highly infectious and thus we based our method under the assumption that all members of a household with at least one confirmed case of COVID-19 should be infected, therefore we estimate the IFR using the number of secondary fatalities in households. The simplicity of the methodology allows for large sample sizes, since it requires minimal laboratory testing capabilities. We applied this methodology to a database of 3,232 confirmed cases in Mexico and arrived to an IFR estimate within the range reported in other studies.
No Detectable Surge in SARS-CoV-2 Transmission due to the April 7, 2020 Wisconsin Election
Authors: Leung Kathy , Wu Joseph T. , Xu Kuang , Wein Lawrence M. 
Published: April 2020

AbstractWe analyze confirmed cases and new hospitalizations in Wisconsin in the weeks surrounding the April 7, 2020 election, and find no evidence of a surge in SARS-CoV-2 transmission.
Clinical Laboratory Parameters Associated with Severe or Critical Novel Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-analysis
Authors: Moutchia Jude , Pokharel Pratik , Kerri Aldiona , McGaw Kaodi , Uchai Shreeshti , Nji Miriam , Goodman Michael 
Published: April 2020

AbstractBackgroundTo date, several clinical laboratory parameters associated with COVID-19 severity have been reported. However, these parameters have not been observed consistently across studies. The aim of this review was to assess clinical laboratory parameters which may serve as markers or predictors of severe or critical COVID-19 diseaseMethodsWe conducted a systematic search of MEDLINE, Embase, Web of Science, CINAHL and Google Scholar databases from 2019 through April 18, 2020, and reviewed bibliographies of eligible studies, relevant systematic reviews, and the medRxiv pre-print server. We included hospital-based observational studies reporting clinical laboratory parameters of confirmed cases of COVID-19 and excluded studies having large proportions (>10%) of children and pregnant women. Two authors independently carried out screening of articles, data extraction and quality assessment. Meta-analyses were done using random effects model. Meta-median difference (MMD) and 95% confidence interval (CI) was calculated for each laboratory parameter.ResultsForty-five studies in 6 countries were included. Compared to non-severe COVID-19 cases, severe or critical COVID-19 disease was characterised by higher neutrophil count (MMD: 1.23 [95% CI: 0.58 to 1.88] ×109 cells/L), and lower lymphocyte and CD4 counts with MMD (95% CI) of -0.39 (-0.47, -0.31) ×109 cells/L and -204.9 (-302.6, -107.1) cells/μl, respectively. Other notable results were observed for C-reactive protein (MMD: 36.97 [95% CI: 27.58, 46.35] mg/L), interleukin-6 (MMD: 17.37 [95% CI: 4.74, 30.00] pg/ml,), Troponin I (MMD: 0.01 [0.00, 0.02] ng/ml), and D-dimer (MMD: 0.65 [0.45, 0.85] mg/ml).Conclusions and RelevanceRelative to non-severe COVID-19, severe or critical COVID-19 is characterised by increased markers of innate immune response, decreased markers of adaptive immune response, and increased markers of tissue damage and major organ failure. These markers could be used to recognise severe or critical disease and to monitor clinical course of COVID-19.
Estimating COVID-19 Antibody Seroprevalence in Santa Clara County, California. A re-analysis of Bendavid et al.
Authors: Bennett Stephen T. , Steyvers Mark 
Published: April 2020

AbstractA recent study by Bendavid et al. claimed that the rate of infection of COVID-19 in Santa Clara county was between 2.49% and 4.16%, 50-85 times higher than the number of officially confirmed cases. The statistical methodology used in that study overestimates of rate of infection given the available data. We jointly estimate the sensitivity and specificity of the test kit along with rate of infection with a simple Bayesian model, arriving at lower estimates of the rate of COVID-19 in Santa Clara county. Re-analyzing their data, we find that the rate of infection was likely between 0.27% and 3.21%.
Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment
Authors: Rashidi Taha Hossein , Shahriari Siroos , Azad AKM , Vafaee Fatemeh 
Published: April 2020

AbstractSubstantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with advanced modelling techniques to provide real-time insights. This study introduces a unified platform which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform is backed up by advanced time series models to capture any possible non-linearity in the data which is enhanced by the capability of measuring the expected impact of preventive interventions such as social distancing and lockdowns. The platform enables lay users, and experts, to examine the data and develop several customized models with different restriction such as models developed for specific time window of the data. Our policy assessment of the case of Australia, shows that social distancing and travel ban restriction significantly affect the reduction of number of cases, as an effective policy.
Automated Diagnosis of COVID-19 Using Deep Learning and Data Augmentation on Chest CT
Authors: Hu Runwen , Ruan Guanqi , Xiang Shijun , Huang Minghui , Liang Qiaoyi , Li Jingxuan 
Published: April 2020

AbstractBackgroundCoronavirus disease 2019 (COVID-19) has surprised the world since the beginning of 2020, and the rapid growth of COVID-19 is beyond the capability of doctors and hospitals that could deal in many areas. The chest computed tomography (CT) could be served as an effective tool in detection of COVID-19. It is valuable to develop automatic detection of COVID-19.Materials and MethodsThe collected dataset consisted of 1042 chest CT images (including 521 COVID-19, 397 healthy, 76 bacterial pneumonia and 48 SARS) obtained by exhaustively searching available data on the Internet. Then, these data are divided into three sets, referred to training set, validation set and testing set. Sixteen data augmentation operations are designed to enrich the training set in deep learning training phase. Multiple experiments were conducted to analyze the performance of the model in the detection of COVID-19 both in case of no noisy labels and noisy labels. The performance was assessed by the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy.ResultsThe data augmentation operations on the training set are effective for improvement of the model performance. The area under the receiver operating characteristic curve is 0.9689 with (95% CI: 0.9308, 1) in case of no noisy labels for the classification of COVID-19 from heathy subject, while the per-exam sensitivity, specificity and accuracy for detecting COVID-19 in the independent testing set are 90.52%, 91.58% and 91.21%, respectively. In the classification of COVID-19 from other hybrid cases, the average AUC of the proposed model is 0.9222 with (95%CI: 0.8418, 1) if there are no noisy labels. The model is also robust when part of the training samples is marked incorrectly. The average AUC is 92.23% in the case of noisy labels of 10% in the training set.ConclusionA deep learning model with insufficient samples can be developed by using data augmentation in assisting medical workers in making quick and correct diagnosis of COVID-19.
Genomics of Indian SARS-CoV-2: Implications in genetic diversity, possible origin and spread of virus
Authors: Mondal Mainak , Lawarde Ankita , Somasundaram Kumaravel 
Published: April 2020

AbstractWorld Health Organization (WHO) declared COVID-19 as a pandemic disease on March 11, 2020. Comparison of genome sequences from diverse locations allows us to identify the genetic diversity among viruses which would help in ascertaining viral virulence, disease pathogenicity, origin and spread of the SARS-CoV-2 between countries. The aim of this study is to ascertain the genetic diversity among Indian SARS-CoV-2 isolates. Initial examination of the phylogenetic data of SARS-CoV-2 genomes (n=3123) from different continents deposited at GISAID (Global Initiative on Sharing All Influenza Data) revealed multiple origin for Indian isolates. An in-depth analysis of 449 viral genomes derived from samples representing countries from USA, Europe, China, East Asia, South Asia, Oceania, Middle East regions and India revealed that most Indian samples are divided into two clusters (A and B) with cluster A showing more similarity to samples from Oceania and Kuwait and the cluster B grouping with countries from Europe, Middle East and South Asia. Diversity analysis of viral clades, which are characterized by specific non-synonymous mutations in viral proteins, discovered that the cluster A Indian samples belong to I clade (V378I in ORF1ab), which is an Oceania clade with samples having Iran connections and the cluster B Indian samples belong to G clade (D614G in Spike protein), which is an European clade. Thus our study identifies that the Indian SARS-CoV-2 viruses belong to I and G clades with potential origin to be countries mainly from Oceania, Europe, Middle East and South Asia regions, which strongly implying the spread of virus through most travelled countries. The study also emphasizes the importance of pathogen genomics through phylogenetic analysis to discover viral genetic diversity and understand the viral transmission dynamics with eventual grasp on viral virulence and disease pathogenesis.
Males that silence their father’s genes: genomic imprinting of a complete haploid genome
Authors: de la Filia Andrés G. , Mongue Andrew J. , Dorrens Jennifer , Lemon Hannah , Laetsch Dominik R. , Ross Laura 
Published: April 2020

AbstractGenetic conflict is considered a key driver in the evolution of new reproductive and sex determining systems. In particular, reproductive strategies with non-Mendelian inheritance, where parents do not contribute equally to the genetic makeup of their offspring. One of the most extraordinary examples of non-Mendelian inheritance is paternal genome elimination (PGE), a form of haplodiploidy which has evolved repeatedly across arthropods. Under PGE, males are diploid but only transmit maternally-inherited chromosomes to their offspring, while the paternal homologues are excluded from sperm. This asymmetric inheritance is thought to have evolved through an evolutionary arms race between paternal and maternal genomes over transmission to future generations. In several clades with PGE, such as the mealybugs (Hemiptera: Pseudococcidae), paternal chromosomes are not just eliminated from sperm, but also heterochromatinised early in development and thought to remain inactive. Such paternal genome silencing could alleviate genetic conflict between paternal alleles over transmission. However, it is unclear if paternal chromosomes are indeed genetically inert in both soma and germline. Here, we present a parent-of-origin allele-specific transcriptome analysis in male mealybugs. We show that expression is globally biased towards the maternal genome, but detect activity of paternal chromosomes in both somatic and reproductive tissues. Up to 70% of somatically-expressed genes are to some degree paternally-expressed. However, paternal genome expression is much more restricted in the testis, with only 20% of genes showing paternal contribution. Finally, we show that the patterns of parent-of-origin-specific gene expression are remarkably similar across genotypes and that those genes with biparental expression show elevated rates of molecular evolution. Our results provide the clearest example yet of genome-wide genomic imprinting (parent-of-origin specific gene expression) in insects. Furthermore, it enhances our understanding of PGE, which will aid future empirical tests of evolutionary theory regarding the origin of this unusual reproductive strategy.
A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness
Authors: Sego T.J. , Aponte-Serrano Josua O. , Gianlupi Juliano Ferrari , Heaps Samuel R. , Breithaupt Kira , Brusch Lutz , Crawshaw Jessica , Osborne James M. , Quardokus Ellen M. , Plemper Richard K. , Glazier James A. 
Published: April 2020

AbstractSimulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.Author summaryThis study presents an open-source, extensible, multiscale platform for simulating viral immune interactions in epithelial tissues, which enables the rapid development and deployment of sophisticated models of viruses, infection mechanisms and tissue types. The model is used to investigate how potential treatments influence disease progression. Simulation results suggest that drugs which interfere with virus replication (e.g., remdesivir) yield substantially better infection outcomes when administered prophylactically even at very low doses than when used at high doses as treatment for an infection that has already begun.
Deep feature extraction of single-cell transcriptomes by generative adversarial network
Authors: Bahrami Mojtaba , Maitra Malosree , Nagy Corina , Turecki Gustavo , Rabiee Hamid R. , Li Yue 
Published: April 2020

AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) has opened the opportunities to dissect the heterogeneous cellular composition and interrogate the cell-type-specific gene expression patterns across diverse conditions. However, batch effects such as laboratory conditions and individual-variability hinder their usage in cross-condition design.ResultsWe present single-cell Generative Adversarial Network (scGAN). Our main contribution is to introduce an adversarial network to predict batch effects using the embeddings from the variational autoencoder network, which does not only need to maximize the Negative Binomial data likelihood of the raw scRNA-seq counts but also minimize the correlation between the latent embeddings and the batch effects. We demonstrate scGAN on three public scRNA-seq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder.AvailabilityThe code is available athttps://github.com/li-lab-mcgill/singlecell-deepfeatureContactyueli@cs.mcgill.ca
House price and household consumption in China: evidence from micro-level data
Authors: Yuan Xiaojun , Shen Yinjie , Zhou Haigang 
Published: April 2020

Purpose
This paper aims to identify how house price affects household consumption.

Design/methodology/approach
The authors use a micro-level data set that tracks the house price and consumption of a vast number of households over a period of four years. OLS regression is the main econometric method.

Findings
The authors document robust evidence that an increase in house prices stimulates household consumption, regardless of whether a household owns or rents. Moreover, the authors find that both acquiring and losing homeownership negatively affects household consumption. Further investigation suggests significant regional heterogeneity in the relationship between house prices and household consumption.

Originality/value
This is one of the first studies examining the relationship between house price and household consumption in China using micro-level data. Given the uniqueness of the Chinese housing market and China’s fast-growing consumption rate, the study contributes new evidence to the long-lasting debate.
Conventional REITs, Islamic REITs and macroeconomic variables in Malaysia: a review
Authors: Sukor Mohd Edil Abd , Sujak Zahida Abu , Noordin Kamaruzaman 
Published: April 2020

PurposeThe purpose of this paper is to empirically examine the return and dividend characteristics of two different types of Malaysian real estate investment trust (REIT) series, namely, conventional and Islamic, against macroeconomic variables over the period 2011-2017.Design/methodology/approachThe required data are derived from Datastream database. Multiple regression analysis is used to determine the impact of macroeconomic variables on financial performance of 13 Malaysian REIT series.FindingsResults show that the macroeconomic variables are able to predict future returns and dividends of Malaysian REITs. The analysis also suggests that Islamic REITs are seen to be less sensitive to macroeconomic variables and display better portfolio diversification benefits as compared to their conventional counterpart. The ongoing implications for large-cap and small-cap REITs are also highlighted.Research limitations/implicationsThe main limitation of the study is the small percentage of Islamic REITs sample due to limited period of observation available. However, the two Islamic REITs included are representative of Islamic REITs in Malaysia as both of them are listed in the Bursa Malaysia with asset size and market capitalization values more than RM1bn.Practical implicationsThe results of this study may serve as a useful input for financial market players on making strategic business decisions especially with regards to differences between conventional and Islamic REITs characteristics.Originality/valueThe main contribution of this paper is to explore the relationship between REITs and macroeconomic factors on a unique capital market (Malaysia) that allows comparison between conventional and its Islamic counterpart.
“We're just building!”: a study of collaborative coaching interactions
Authors: LeeKeenan Kira 
Published: April 2020

PurposeThis paper aims to clarify opportunities for collaborative interactions between cooperating teachers (CTs) and preservice teachers (PTs) in practice-based teacher preparation programs (TPPs). The study aimed to explore the discursive moves that facilitate collaboration between one CT and PT.Design/methodology/approachGrounded in the critical sociocultural theory, this study applied a qualitative microanalytic approach to the study of coaching interactions for the purpose of understanding why and how collaborative discourse developed between a CT and a PT.FindingsThis study of discourse moves within collaborative coaching interactions revealed collaborative interactions developed from strategic repositioning of social roles, which created space for authentic problem-posing by both the CT and the PT, and the co-construction of teaching events, which supported more specific planning toward future lessons; and routine and appreciative use of observational data created space for co-construction and co-inquiry.Practical implicationsThis study illuminated the complex social and discursive dance embedded within collaborative interactions. The findings also suggested that the project of co-constructing curriculum with someone is a powerful and necessary experience for a PT because it is through this co-construction that PTs learn how to design meaningful curriculum and critically reflect on practice.Originality/valueThis study offers new understandings around how collaborative talk in educational discourse transpires and why providing opportunities for PTs to take a more active role in their own learning is important.
The use of audio diaries to support reflective mentoring practice in Hungarian teacher training
Authors: Káplár-Kodácsy Kinga , Dorner Helga 
Published: April 2020

PurposeThe aim of this study is to explore how mentors' and mentees' self-concepts and related reflective practices in mentored teacher training are supported by using audio diaries within the framework of Dialogical Self Theory (Hermans, 2001), and how it could be used in the wider context of teacher training.Design/methodology/approachThis study explores a specific qualitative methodology, the use of audio diary in self-reflective activities, in the context of teacher training in Hungary. When analysing the data, we used the thematic analysis approach to employ a relatively high level of interpretation.FindingsMulti-level meta-position reflections have emerged from the data that were comparable at a given point in time. We found five different I-positions (Hermans, 2001) that suggest that mentors and mentees perceived of these as shared themes of the emerging incidents in mentoring. However, those aspects of the mentoring process on which mentors and mentees reflected only vaguely or have not reflected mutually in their audio diaries involved a certain level of mis-positioning and further tension.Practical implicationsAudio diaries are beneficial for personal and professional development. The tools and the methodology around them could be leveraged to broaden mentor–mentee dyads, which may lead to including university-based teacher educators and researchers from the field.Originality/valueThe value of this study arises from the process of recording audio diary logs as a direct representation of thoughts during the mentorship process.