bluuebunny/crossref_metadata_2025_split huggingface dataset:
Japanese citizens’ behavioral changes and preparedness against COVID-19: How effective is Japan’s approach of self-restraint ?
Authors: Muto Kaori , Yamamoto Isamu , Nagasu Miwako , Tanaka Mikihito , Wada Koji 
Published: April 2020

AbstractThe Japanese government instituted countermeasures against COVID-19, a pneumonia caused by the new coronavirus, in January 2020. Seeking “people’s behavioral changes,” in which the government called on the public to take precautionary measures or exercise self-restraint, was one of the important strategies. The purpose of this study is to investigate how and from when Japanese citizens have changed their precautionary behavior under these circumstances, where the government has only requested their cooperation. This study uses micro data from a cross-sectional survey conducted on an online platform of an online research company, based on quota sampling that is representative of the Japanese population. By the end of March 2020, we had recruited a total of 11,342 respondents, aged from 20 to 64 years. About 85% reported practising the social distancing recommended by the government. More females than males and more older than younger participants are supportive of practicing social distancing. Frequent handwashing is conducted by 86 percent of all, 92 percent of female and 87.9 percent of over-40 participants. The most important event influencing these precautionary actions was the infection aboard the Diamond Princess cruise ship, which occurred in early February 2020 (23%). Information from the central and local governments, received by 60% of the participants, was deemed trustworthy by 50%. However, the results also showed that about 20% of the participants were reluctant to implement proper prevention measures. The statistical analysis indicated that the typical characteristics of those people were male, younger (under 30 years old), unmarried, from lower-income households, with a drinking or smoking habit and a higher extraversion score. To prevent the spread of infection in Japan, it is imperative to address these individuals and encourage their behavioral changes using various means to reach and influence them.
Harmonizing heterogeneous endpoints in COVID-19 trials without loss of information –an essential step to facilitate decision making
Authors: von Cube Maja , Grodd Marlon , Wolkewitz Martin , Hazard Derek , Lambert Jerome 
Published: April 2020

AbstractBackgroundMany trials are now underway to inform decision-makers on potential effects of treatments for COVID-19. To provide sufficient information for all involved decision-makers (clinicians, public health authorities, drug regulatory agencies) a multiplicity of endpoints must be considered. It is a challenge to generate detailed high quality evidence from data while ensuring fast availability and evaluation of the results.MethodsWe reviewed all interventional COVID-19 trials on Remdesivir, Lopinavir/ritonavir and Hydroxychloroquine registered in the National Library of Medicine (NLM) at the National Institutes of Health (NIH) and summarized the endpoints used to assess treatment effects. We propose a multistate model that harmonizes heterogeneous endpoints and differing lengths of follow-up within and between trials.ResultsThere are currently, March 27, 2020, 23 registered interventional trials investigating the potential benefits of Remdesivir, Lopinavir/ritonavir and Hydroxychloroquine. The endpoints are highly heterogeneous. Follow-up for the primary endpoints ranges from four to 168 days.A detailed precisely defined endpoint has been proposed by the global network REMAP-CAP, which is specialized on community-acquired pneumonia. Their seven-category endpoint accounts for major clinical events informative for all decision-makers. Moreover, the Core Outcome Measures in Effectiveness Trials (COMET) Initiative is currently working on a core outcome set. We propose a multistate model that accommodates analysis of these recommended endpoints. The model allows for a detailed investigation of treatment effects for various endpoints over the course of time thereby harmonizing differing endpoints and lengths of follow-up.ConclusionMultistate model analysis is a powerful tool to study clinically heterogeneous endpoints (mortality, discharge) as well as endpoints influencing hospital capacities (duration of hospitalization and ventilation) simultaneously over time. Our proposed model extracts all information available in the data and is - by harmonizing endpoints within and between trials - a step towards faster decision making. All ongoing clinical trials, especially those with severe cases, should accommodate primary analysis with a stacked probability plot of the major events mechanical ventilation, discharge alive and death.
Globalized low-income countries may experience higher COVID-19 mortality rates
Authors: Jaffé Rodolfo , Ortiz Mabel , Jaffé Klaus 
Published: April 2020

AbstractUnderstanding the factors underpinning COVID-19 infection and mortality rates is essential in order to implement actions that help mitigate the current pandemic. Here we evaluate how a suit of 15 climatic and socio-economic variables influence COVID-19 exponential growth-phase infection and mortality rates across 36 countries. We found that imports of goods and services, international tourism and the number of published scientific papers are good predictors of COVID-19 infection rates, indicating that more globalized countries may have experienced multiple and recurrent introductions of the virus. However, high-income countries showed lower mortality rates, suggesting that the consequences of the current pandemic will be worse for globalized low-income countries. International aid agencies could use this information to help mitigate the consequences of the current pandemic in the most vulnerable countries.
Modelling the COVID-19 epidemics in Brasil: Parametric identification and public health measures influence
Authors: Cotta R.M. , Naveira-Cotta C.P. , Magal P. 
Published: April 2020

ABSTRACTA SIRU-type epidemic model is proposed for the prediction of COVID-19 spreading within Brasil, and analyse the influence of public health measures on simulating the control of this infectious disease. Since the reported cases are typically only a fraction of the total number of the symptomatic infectious individuals, the model accounts for both reported and unreported cases. Also, the model allows for the time variation of both the transmission rate and the fraction of asymptomatic infectious that become reported symptomatic individuals, so as to reflect public health interventions, towards its control, along the course of the epidemic evolution. An analytical exponential behaviour for the accumulated reported cases evolution is assumed at the onset of the epidemy, for explicitly estimating initial conditions, while a Bayesian inference approach is adopted for parametric estimations employing the present direct problem model with the data from the known portion of the epidemics evolution, represented by the time series for the reported cases of infected individuals. The direct-inverse problem analysis is then employed with the actual data from China, with the first half been employed for the parametric estimation and the second half for validation of the predictive capability of the proposed approach. The full dataset for China is then employed in another parameter identification, aimed at refining the values for the average times that asymptomatic infectious individuals and that symptomatic individuals remain infectious. Following this validation, the available data on reported cases in Brasil from February 15thtill March 29th, 2020, is used for estimating parameters and then predict the epidemy evolution under these conditions. Finally, public health interventions are simulated, aimed at diminishing the effects of the disease spreading, by acting on both the transmission rate and the fraction of the total number of the symptomatic infectious individuals, considering time variable exponential behaviours for these two parameters, usually assumed constant in epidemic evolutions without intervention. It is demonstrated that a combination of actions to affect both parameters can have a much faster and effective result in the control of the epidemy dynamics.
A flexible method for optimising sharing of healthcare resources and demand in the context of the COVID-19 pandemic
Authors: Lacasa Lucas , Challen Robert , Brooks-Pollock Ellen , Danon Leon 
Published: April 2020

AbstractAs the number of cases of COVID-19 continues to grow, local health services are at risk of being overwhelmed with patients requiring intensive care. We develop and implement an algorithm to provide optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. We validate our approach with realistic data from the United Kingdom and Spain. In the UK, we consider the National Health Service at the level of trusts and define a 4-regular geometric graph which indicates the four nearest neighbours of any given trust. In Spain we coarse-grain the healthcare system at the level of autonomous communities, and extract similar contact networks. Through random search optimisation we identify the best load sharing strategy, where the cost function to minimise is based on the total number of ICU units above capacity. Our framework is general and flexible allowing for additional criteria, alternative cost functions, and can be extended to other resources beyond ICU units or ventilators. Assuming a uniform ICU demand, we show that it is possible to enable access to ICU for up to 1000 additional cases in the UK in a single step of the algorithm. Under a more realistic and heterogeneous demand, our method is able to balance about 600 beds per step in the Spanish system only using local sharing, and over 1300 using countrywide sharing, potentially saving a large percentage of these lives that would otherwise not have access to ICU.
COVID-19 infection during pregnancy: a systematic review to summarize possible symptoms, treatments, and pregnancy outcomes
Authors: Ali Khan Md. Mostaured , Khan Md. Nuruzzaman , Mustagir Md. Golam , Rana Juwel , Haque Md. Rajwanul , Rahman Md. Mosfequr 
Published: April 2020

AbstractBackgroundWith the exponential increase in coronavirus disease 2019 (COVID-19) worldwide, an increasing proportion of pregnant women are now infected during their pregnancy. The aims of this systematic review were to summarize the possible symptoms, treatments, and pregnancy outcomes of women infected with COVID-19 during their pregnancy.MethodsFour databases (Medline, Web of Science, Scopus, and CINAHL) were searched on March 25, 2020, using the following keywords: “COVID-19”, “nCoV-2019”, and “coronavirus.” Articles included if they reported either the symptoms, treatments for the women who had been infected with the COVID-19 during their pregnancy or pregnancy outcomes. The selected articles’ results were summarized employing a narrative synthesis approach.ResultsA total of nine studies were selected for this study, comprising 101 infected pregnant women. Other than the infected general population, infected pregnant women reported different symptoms; however, fever (66.7%), cough (39.4%), fatigue (15.2%), and breathing difficulties (14.1%) were common. Infected pregnant women were given different treatments than the general infected population. The C-section was a common (83.9%) mode of delivery among infected pregnant women, and a higher proportion of births were preterm births (30.4%) and low birth weight (17.9%).ConclusionsPregnant infected women had different symptoms, and they were given dissimilar treatments than the general infected population. Healthcare providers may have appropriately informed about these symptoms and treatments. They, therefore, would be able to handle infection during pregnancy effectively, which would reduce common adverse consequences among infected pregnant women.
Projected ICU and Mortuary load due to COVID-19 in Sydney
Authors: Francis Andrew , Guo Yi , Hurley Paul , Obst Oliver , Park Laurence , Tanaka Mark , Thomson Russell , Wang X. Rosalind 
Published: April 2020

AbstractThe spread of COVID-19 is expected to put a large strain on many hospital resources, including ICU bed space, and mortuary capacity. In this report we study the possible demands on ICU and mortuary capacity in Sydney, Australia, using an adapted SEIR epidemiological model.
Simple model for Covid-19 epidemics – back-casting in China and forecasting in the US
Authors: Hermanowicz Slav W. 
Published: April 2020

Abstract and FindingsIn our previous work, we analyze, in near-real time, evolution of Covid-19 epidemic in China for the first 22 days of reliable data (up to February 6, 2020). In this work, we used the data for the whole 87 days (up to March 13, 2020) in China and the US data available till March 31 (day 70) for systematic evaluation of the logistic model to predict epidemic growth. We sequentially estimated sets of model parameters (maximum number of casesK, growth rater, and half-timet0) and the epidemic “end time”t95(defined as the time when the number of cases, predicted or actual, reached 95% of the maximum). The estimates of these parameters were done for sequences of reported cases growing daily (back-casting for China and forecasting for the US). In both countries, the estimates ofKgrew very much in time during the exponential and nearly exponential phases making longer term forecasting not reliable. For the US, the current estimate of the maximum number of casesKis about 265,000 but it is very likely that it will grow in the future. However, running estimates of the “end time”t95were in a much smaller interval for China (60 – 70 daysvs. the actual value of 67). For the US, the values estimated from the data sequences going back two weeks from now range from 70 to 80 days. If the behavior of the US epidemic is similar to the previous Chinese development, thenumber of reported casescould reach amaximum around April 10 to 14.
Versatile workflow for cell type resolved transcriptional and epigenetic profiles from cryopreserved human lung
Authors: Llamazares Prada M , Espinet E , Mijosek V , Schwartz U , Waszak SM , Lutsik P , Tamas R , Richter M , Behrendt A , Pohl S , Benz N , Muley T , Warth A , Heußel CP , Winter H , Herth F , Mertens T , Karmouty-Quintana H , Koch I , Benes V , Korbel JO , Trumpp A , Wyatt D , Stahl H , Plass C , Jurkowska RZ 
Published: April 2020

AbstractThe complexity of the lung microenvironment together with changes in cellular composition during disease progression make it exceptionally hard to understand the molecular mechanisms leading to the development of chronic lung diseases. Although recent advances in cell type resolved and single-cell sequencing approaches hold great promise for studying complex diseases, their implementation greatly relies on local access to fresh tissue, as traditional methods to process and store tissue do not allow viable cell isolation. To overcome these hurdles, we developed a novel, versatile workflow that allows long-term storage of human lung tissue with high cell viability, permits thorough sample quality check before cell isolation, and is compatible with next generation sequencing-based profiling, including single-cell approaches. We demonstrate that cryopreservation is suitable for isolation of multiple cell types from different lung locations and is applicable to both healthy and diseased tissue, including COPD and tumor samples. Basal cells isolated from cryopreserved airways retain the ability to differentiate, indicating that cellular identity is not altered by cryopreservation. Importantly, using RNA sequencing (RNA-seq) and Illumina EPIC Array, we show that genome-wide gene expression and DNA methylation signatures are preserved upon cryopreservation, emphasizing the suitability of our workflow for -omics profiling of human lung cells. In addition, we obtained high-quality single-cell RNA sequencing data of cells isolated from cryopreserved human lung, demonstrating that cryopreservation empowers single-cell approaches. Overall, thanks to its simplicity, our cryopreservation workflow is well-suited for prospective tissue collection by academic collaborators and biobanks, opening worldwide access to human tissue.
Computational target fishing by mining transcriptional data using a novel Siamese spectral-based graph convolutional network
Authors: Zhong Feisheng , Wu Xiaolong , Li Xutong , Wang Dingyan , Fu Zunyun , Liu Xiaohong , Wan XiaoZhe , Yang Tianbiao , Luo Xiaomin , Chen Kaixian , Jiang Hualiang , Zheng Mingyue 
Published: April 2020

AbstractComputational target fishing aims to investigate the mechanism of action or the side effects of bioactive small molecules. Unfortunately, conventional ligand-based computational methods only explore a confined chemical space, and structure-based methods are limited by the availability of crystal structures. Moreover, these methods cannot describe cellular context-dependent effects and are thus not useful for exploring the targets of drugs in specific cells. To address these challenges, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. Using a benchmark set, the model achieved impressive target inference results compared with previous methods such as Connectivity Map and ProTINA. More importantly, the powerful generalization ability of the model observed with the external LINCS phase II dataset suggests that the model is an efficient target fishing or repositioning tool for bioactive compounds.
Changes in the abundance of Danish orchids over the past 30 years
Authors: Damgaard Christian , Moeslund Jesper Erenskjold , Wind Peter 
Published: April 2020

AbstractOrchid abundance data collected over the past 30 years (1987-2016) from 440 sites within the National Orchid Monitoring Program were analysed to quantify the population trends of orchids in Denmark, and the underlying reasons for the observed population trends were analysed and discussed. Twenty of the 45 monitored Danish orchids showed a significant decrease in abundance over the past 30 years (16, if only orchids with at least 50 observations each were selected), thus corroborating the previous observations of declining orchid abundances at European scale. Generally, there was a significant negative effect of overgrowing with tall-growing herbs and shrubs on the abundance of Danish orchids mainly caused by change of farming practises, as extensive management, such as grazing or mowing of light-open grassland areas, has decreased.
Insights into The Codon Usage Bias of 13 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Isolates from Different Geo-locations
Authors: Khodary Saif M. , Anwar Ali Mostafa 
Published: April 2020

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of Coronavirus disease 2019 (COVID-19) which is an infectious disease that spread throughout the world and was declared as a pandemic by the World Health Organization (WHO). In this study, we performed a genome-wide analysis on the codon usage bias (CUB) of 13 SARS-CoV-2 isolates from different geo-locations (countries) in an attempt to characterize it, unravel the main force shaping its pattern, and understand its adaptation toHomo sapiens. Overall results revealed that, SARS-CoV-2 codon usage is slightly biased similarly to other RNA viruses. Nucleotide and dinucleotide compositions displayed a bias toward A/U content in all codon positions and CpU-ended codons preference, respectively. Eight common putative preferred codons were identified, and all of them were A/U-ended (U-ended: 7, A-ended: 1). In addition, natural selection was found to be the main force structuring the codon usage pattern of SARS-CoV-2. However, mutation pressure and other factors such as compositional constraints and hydrophobicity had an undeniable contribution. Two adaptation indices were utilized and indicated that SARS-CoV-2 is moderately adapted toHomo sapienscompared to other human viruses. The outcome of this study may help in understanding the underlying factors involved in the evolution of SARS-CoV-2 and may aid in vaccine design strategies.
The role of thickness inhomogeneities in hierarchical cortical folding
Authors: da Costa Campos Lucas , Hornung Raphael , Gompper Gerhard , Elgeti Jens , Caspers Svenja 
Published: April 2020

AbstractThe morphology of the mammalian brain cortex is highly folded. For long it has been known that specific patterns of folding are necessary for an optimally functioning brain. On the extremes, lissencephaly, a lack of folds in humans, and polymicrogyria, an overly folded brain, can lead to severe mental retardation, short life expectancy, epileptic seizures, and tetraplegia. The construction of a quantitative model on how and why these folds appear during the development of the brain is the first step in understanding the cause of these conditions. In recent years, there have been various attempts to understand and model the mechanisms of brain folding. Previous works have shown that mechanical instabilities play a crucial role in the formation of brain folds, and that the geometry of the fetal brain is one of the main factors in dictating the folding characteristics. However, modeling higher-order folding, one of the main characteristics of the highly gyrencephalic brain, has not been fully tackled. The effects of thickness inhomogeneity in the gyrogenesis of the mammalian brain are studiedin silico. Finite-element simulations of rectangular slabs are performed. The slabs are divided into two distinct regions, where the outer layer mimics the gray matter, and the inner layer the underlying white matter. Differential growth is introduced by growing the top layer tangentially, while keeping the underlying layer untouched. The brain tissue is modeled as a neo-Hookean hyperelastic material. Simulations are performed with both, homogeneous and inhomogeneous cortical thickness. The homogeneous cortex is shown to fold into a single wavelength, as is common for bilayered materials, while the inhomogeneous cortex folds into more complex conformations. In the early stages of development of the inhomogeneous cortex, structures reminiscent of the deep sulci in the brain are obtained. As the cortex continues to develop, secondary undulations, which are shallower and more variable than the structures obtained in earlier gyrification stage emerge, reproducing well-known characteristics of higher-order folding in the mammalian, and particularly the human, brain.