bluuebunny/crossref_metadata_2025_split huggingface dataset:
A cell phone data driven time use analysis of the COVID-19 epidemic
Authors: Fenichel Eli P. , Berry Kevin , Bayham Jude , Gonsalves Gregg 
Published: April 2020

AbstractTransmission of the SAR-COV-2 virus that causes COVID-19 is largely driven by human behavior and person-to-person contact. By staying home, people reduce the probability of contacting an infectious individual, becoming infected, and passing on the virus. One of the most promising sources of data on time use is smartphone location data. We develop a time use driven proportional mixing SEIR model that naturally incorporates time spent at home measured using smartphone location data and allows people of different health statuses to behave differently. We simulate epidemics in almost every county in the United States. The model suggests that Americans’ behavioral shifts have reduced cases in 55%-86% of counties and for 71%-91% of the population, depending on modeling assumptions. Resuming pre-epidemic behavior would lead to a rapid rise in cases in most counties. Spatial patterns of bending and flattening the curve are robust to modeling assumptions. Depending on epidemic history, county demographics, and behavior within a county, returning those with acquired immunity (assuming it exists) to regular schedules generally helps reduce cumulative COVID-19 cases. The model robustly identifies which counties would experience the greatest share of case reduction relative to continued distancing behavior. The model occasionally mischaracterizes epidemic patterns in counties tightly connected to larger counties that are experiencing large epidemics. Understanding these patterns is critical for prioritizing testing resources and back-to-work planning for the United States.
An improved mathematical prediction of the time evolution of the Covid-19 Pandemic in Italy, with Monte Carlo simulations and error analyses
Authors: Ciufolini Ignazio , Paolozzi Antonio 
Published: April 2020

AbstractWe present an improved mathematical analysis of the time evolution of the Covid-19 pandemic in Italy and a statistical error analyses of its evolution, including Monte Carlo simulations with a very large number of runs to evaluate the uncertainties in its evolution. A previous analysis was based on the assumption that the number of nasopharyngeal swabs would be constant. However the number of daily swabs is now more than five times what it was when we did our previous analysis. Therefore, here we consider the time evolution of the ratio of the new daily cases to number of swabs, which is more representative of the evolution of the pandemic when the number of swabs is increasing or changing in time. We consider a number of possible distributions representing the evolution of the pandemic in Italy and we test their prediction capability over a period of up to four weeks. The results show that a distribution of the type of Planck black body radiation law provides very good forecasting. The use of different distributions provides an independent possible estimate of the uncertainty. We then consider five possible trajectories for the number of daily swabs and we estimate the potential dates of a substantial reduction in the number of new daily cases. We then estimate the spread in a substantial reduction, below a certain threshold, of the daily cases per swab among the Italian regions. We finally perform Monte Carlo simulations with 25000 runs to evaluate a random uncertainty in the prediction of the date of a substantial reduction in the number of diagnosed daily cases per swab.
Recombinant SARS-CoV-2 spike S1-Fc fusion protein induced high levels of neutralizing responses in nonhuman primates
Authors: Ren Wenlin , Sun Hunter , Gao George F. , Chen Jianxin , Sun Sean , Zhao Rongqing , Gao Guang , Hu Yalin , Zhao Gan , Chen Yuxin , Jin Xia , Fang Feng , Chen Jinggong , Wang Qi , Gong Sitao , Gao Wen , Sun Yufei , Su Junchi , He Ailiang , Cheng Xin , Li Min , Xia Chenxi , Li Maohua , Sun Le 
Published: April 2020

AbstractThe COVID-19 outbreak has become a global pandemic responsible for over 2,000,000 confirmed cases and over 126,000 deaths worldwide. In this study, we examined the immunogenicity of CHO-expressed recombinant SARS-CoV-2 S1-Fc fusion protein in mice, rabbits, and monkeys as a potential candidate for a COVID-19 vaccine. We demonstrate that the S1-Fc fusion protein is extremely immunogenic, as evidenced by strong antibody titers observed by day 7. Strong virus neutralizing activity was observed on day 14 in rabbits immunized with the S1-Fc fusion protein using a pseudovirus neutralization assay. Most importantly, in less than 20 days and three injections of the S1-Fc fusion protein, two monkeys developed higher virus neutralizing titers than a recovered COVID-19 patient in a live SARS-CoV-2 infection assay. Our data strongly suggests that the CHO-expressed SARS-CoV-2 S1-Fc recombinant protein could be a strong candidate for vaccine development against COVID-19.HighlightsCHO-expressed S1-Fc protein is very immunogenic in various animals and can rapidly induce strong antibody productionS1-Fc protein solicits strong neutralizing activities against live virusStable CHO cell line expressing 50 mg/L of S1-Fc and a 3,000 L Bioreactor can produce 3 million doses of human COVID-19 vaccine every 10 days, making it an accessible and affordable option for worldwide vaccination
Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2
Authors: Heiser Katie , McLean Peter F. , Davis Chadwick T. , Fogelson Ben , Gordon Hannah B. , Jacobson Pamela , Hurst Brett , Miller Ben , Alfa Ronald W. , Earnshaw Berton A. , Victors Mason L. , Chong Yolanda T. , Haque Imran S. , Low Adeline S. , Gibson Christopher C. 
Published: April 2020

AbstractTo identify potential therapeutic stop-gaps for SARS-CoV-2, we evaluated a library of 1,670 approved and reference compounds in an unbiased, cellular image-based screen for their ability to suppress the broad impacts of the SARS-CoV-2 virus on phenomic profiles of human renal cortical epithelial cells using deep learning. In our assay, remdesivir is the only antiviral tested with strong efficacy, neither chloroquine nor hydroxychloroquine have any beneficial effect in this human cell model, and a small number of compounds not currently being pursued clinically for SARS-CoV-2 have efficacy. We observed weak but beneficial class effects of β-blockers, mTOR/PI3K inhibitors and Vitamin D analogues and a mild amplification of the viral phenotype with β-agonists.
Experimental and computational study on motor control and recovery after stroke: towards a constructive loop between experimental and virtual embodied neuroscience
Authors: Allegra Mascaro Anna Letizia , Falotico Egidio , Petkoski Spase , Pasquini Maria , Vannucci Lorenzo , Tort-Colet Núria , Conti Emilia , Resta Francesco , Spalletti Cristina , Ramalingasetty Shravan Tata , von Arnim Axel , Formento Emanuele , Angelidis Emmanouil , Blixhavn Camilla Hagen , Leergaard Trygve Brauns , Caleo Matteo , Destexhe Alain , Ijspeert Auke , Micera Silvestro , Laschi Cecilia , Jirsa Viktor , Gewaltig Marc-Oliver , Pavone Francesco S. 
Published: April 2020

ABSTRACTBeing able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.
UMI-Gen: a UMI-based reads simulator for variant calling evaluation in paired-end sequencing NGS libraries
Authors: Sater Vincent , Viailly Pierre-Julien , Lecroq Thierry , Ruminy Philippe , Bérard Caroline , Prieur-Gaston Élise , Jardin Fabrice 
Published: April 2020

AbstractMotivationWith Next Generation Sequencing becoming more affordable every year, NGS technologies asserted themselves as the fastest and most reliable way to detect Single Nucleotide Variants (SNV) and Copy Number Variations (CNV) in cancer patients. These technologies can be used to sequence DNA at very high depths thus allowing to detect abnormalities in tumor cells with very low frequencies. A lot of different variant callers are publicly available and usually do a good job at calling out variants. However, when frequencies begin to drop under 1%, the specificity of these tools suffers greatly as true variants at very low frequencies can be easily confused with sequencing or PCR artifacts. The recent use of Unique Molecular Identifiers (UMI) in NGS experiments offered a way to accurately separate true variants from artifacts. UMI-based variant callers are slowly replacing raw-reads based variant callers as the standard method for an accurate detection of variants at very low frequencies. However, benchmarking done in the tools publication are usually realized on real biological data in which real variants are not known, making it difficult to assess their accuracy.ResultsWe present UMI-Gen, a UMI-based reads simulator for targeted sequencing paired-end data. UMI-Gen generates reference reads covering the targeted regions at a user customizable depth. After that, using a number of control files, it estimates the background error rate at each position and then modifies the generated reads to mimic real biological data. Finally, it will insert real variants in the reads from a list provided by the user.AvailabilityThe entire pipeline is available athttps://gitlab.com/vincent-sater/umigen-masterunder MIT license.Contactvincent.sater@gmail.com
Functional and Genetic Analysis of Viral Receptor ACE2 Orthologs Reveals a Broad Potential Host Range of SARS-CoV-2
Authors: Liu Yinghui , Hu Gaowei , Wang Yuyan , Zhao Xiaomin , Ji Fansen , Ren Wenlin , Gong Mingli , Ju Xiaohui , Zhu Yuanfei , Cai Xia , Wu Jianping , Lan Xun , Xie Youhua , Wang Xinquan , Yuan Zhenghong , Zhang Rong , Ding Qiang 
Published: April 2020

AbstractThe pandemic of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a major global health threat. Epidemiological studies suggest that bats are the natural zoonotic reservoir for SARS-CoV-2. However, the host range of SARS-CoV-2 and intermediate hosts that facilitate its transmission to humans remain unknown. The interaction of coronavirus with its host receptor is a key genetic determinant of host range and cross-species transmission. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) as the receptor to enter host cells in a species-dependent manner. It has been shown that human, palm civet, pig and bat ACE2 can support virus entry, while the murine ortholog cannot. In this study, we characterized the ability of ACE2 from diverse species to support viral entry. We found that ACE2 is expressed in a wide range of species, with especially high conservation in mammals. By analyzing amino acid residues of ACE2 critical for virus entry, based on structure of SARS-CoV spike protein interaction with human, bat, palm civet, pig and ferret ACE2, we identified approximately eighty ACE2 proteins from mammals that could potentially mediate SARS-CoV-2 entry. We chose 48 representative ACE2 orthologs among eighty orthologs for functional analysis and it showed that 44 of these mammalian ACE2 orthologs, including those of domestic animals, pets, livestock, and animals commonly found in zoos and aquaria, could bind SARS-CoV-2 spike protein and support viral entry. In contrast, New World monkey ACE2 orthologs could not bind SARS-CoV-2 spike protein and support viral entry. We further identified the genetic determinant of New World monkey ACE2 that restricts viral entry using genetic and functional analyses. In summary, our study demonstrates that ACE2 from a remarkably broad range of species can facilitate SARS-CoV-2 entry. These findings highlight a potentially broad host tropism of SARS-CoV-2 and suggest that SARS-CoV-2 might be distributed much more widely than previously recognized, underscoring the necessity to monitor susceptible hosts to prevent future outbreaks.
A Substantia Innominata-midbrain Circuit Controls a General Aggressive Response
Authors: Zhu Zhenggang , Ma Qingqing , Miao Lu , Yang Hongbin , Pan Lina , Li Kaiyuan , Zeng Ling-Hui , Zhang Xiaoxing , Wu Jintao , Hao Sijia , Lin Shen , Ma Xiulin , Mai Weihao , Feng Xiang , Hao Yizhe , Sun Li , Duan Shumin , Yu Yan-qin 
Published: April 2020

SUMMARYWhile aggressive behaviors are universal and essential for survival, ‘uncontrollable’ and abnormal aggressive behaviors in animals or humans may have severe adverse consequences or social costs. Neural circuits regulating specific forms of aggression under defined conditions have been described, but how brain circuits govern a general aggressive response remains unknown. Here, we found that posterior substantia innominata (pSI) neurons responded to several aggression-provoking cues with the graded activity of differential dynamics, predicting the aggressive state and the topography of aggression in mice. Activation of pSI neurons projecting to the periaqueductal gray (PAG) increased aggressive arousal and robustly initiated/promoted all the types of aggressive behavior examined in an activity level-dependent manner. Inactivation of the pSI circuit largely blocked diverse aggressive behaviors but not mating. By encoding a general aggressive response, the pSI-PAG circuit universally drives multiple aggressive behaviors and may provide a potential target for alleviating human pathological aggression.In BriefZhu et al. find that the posterior substantia innominata neurons universally drive aggressive behaviors by representing aggressive state and topography of aggression in mice.HighlightspSI neuronal dynamics reflect aggressive state and the topography of aggressionThe pSI-PAG circuit promotes arousal and elicits thirteen aggressive behaviorsThe pSI controls various aggressive behaviors in an activity level-dependent mannerInactivation of the pSI circuit blocks diverse aggressive behaviors but not mating
Network and pathway expansion of genetic disease associations identifies successful drug targets
Authors: MacNamara Aidan , Nakic Nikolina , Amin Ali , Guo Cong , Sieber Karsten B. , Hurle Mark , Gutteridge Alex 
Published: April 2020

AbstractIt is widely accepted that genetic evidence of disease association acts as a sound basis for the selection of drug targets for complex common diseases and that propagation of genetic evidence through gene or protein interaction networks can accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these beliefs for drug discovery has been lacking.In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data.We find that protein networks formed from specific functional linkages such as protein complexes and ligand-receptor pairs are suitable for even naïve guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein-protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence should be used for drug target identification.
Diffusion-MRI-based regional cortical microstructure at birth for predicting neurodevelopmental outcomes of 2-year-olds
Authors: Ouyang Minhui , Peng Qinmu , Jeon Tina , Heyne Roy , Chalak Lina , Huang Hao 
Published: April 2020

AbstractBrain cerebral cortical architecture encoding regionally differential dendritic arborization and synaptic formation at birth underlies human behavior emergence at 2 years of age. Brain changes in 0-2 years are most dynamic across lifespan. Effective prediction of future behavior with brain microstructure at birth will reveal structural basis of behavioral emergence in typical development, and identify biomarkers for early detection and tailored intervention in atypical development. Here, we aimed to evaluate the neonate whole-brain cortical microstructure quantified by diffusion MRI for predicting future behavior. We found that individual cognitive and language functions assessed at age of 2 years were robustly predicted by neonate cortical microstructure using support vector regression. Remarkably, cortical regions contributing heavily to the prediction models exhibited distinctive functional selectivity for cognition and language. These findings highlight regional cortical microstructure at birth as potential sensitive biomarker in predicting future neurodevelopmental outcomes and identifying individual risks of brain disorders.
Concurrent neuroimaging and neurostimulation reveals a causal role for dlPFC in coding of task-relevant information
Authors: Jackson Jade B. , Feredoes Eva , Rich Anina N. , Lindner Michael , Woolgar Alexandra 
Published: April 2020

AbstractThe way in which the brain prioritises processing of information relevant for our current goals is widely contested. Many studies implicate the dorsolateral prefrontal cortex (dlPFC), and propose that it drives brain-wide focus by biasing processing in favour of relevant information. An alternative, however, is that dlPFC is involved in the inhibition of irrelevant information. Here, we address this longstanding debate using the inferentially powerful approach of applying transcranial magnetic stimulation during functional magnetic resonance imaging (concurrent TMS-fMRI) and testing for changes in information coding using multivariate pattern analysis (MVPA). We ask whether dlPFC plays a causal role in prioritising information processing, and whether this is throughselectionof relevant information orinhibitionof irrelevant information. Participants attended to one object feature whilst ignoring another feature of the same object. We reasoned that, if dlPFC is necessary forselection, active (disruptive) TMS shoulddecreasecoding of attended information compared to the low intensity (control) condition. Conversely, if right dlPFC is crucial forinhibition, active TMS shouldincreasecoding of irrelevant information relative to the control condition. The results showed that active TMS decreased coding ofrelevantinformation throughout the frontoparietal multiple demand regions, and that this impact was significantly stronger than the effect of TMS onirrelevantinformation coding, which was not statistically detectable. These data provide causal evidence for a specific role of dlPFC in supporting the representation of task-relevant information and demonstrate the crucial insights into high level cognitive-neural mechanisms possible with the combination of TMS-fMRI and MVPA.
Blocking gephyrin phosphorylation or microglia BDNF signaling prevents synapse loss and reduces infarct volume after ischemia
Authors: Cramer Teresa , Gill Raminder , Thirouin Zahra S , Vaas Markus , Sampath Suchita , Martineau Fanny , Noya Sara B. , Colameo David , Chang Philip K.-Y. , Wu Peiyou , Barker Philip A , Brown Steven A. , Paolicelli Rosa C. , Klohs Jan , McKinney R. Anne , Tyagarajan Shiva K. 
Published: April 2020

AbstractMicroglia interact with neurons to facilitate synapse plasticity; however, signal transducers between microglia and neuron remain unknown. Here, usingin vitroorganotypic hippocampal slice cultures and transient MCAO in genetically-engineered micein vivo, we report that at 24 h post-ischemia microglia release BDNF to downregulate glutamatergic and GABAergic synapses within the peri-infarct area. Analysis of the CA1 hippocampal formationin vitroshows that proBDNF and mBDNF downregulate glutamatergic dendritic spines and gephyrin scaffold stability through p75NTRand TrkB receptors respectively. Post-MCAO, we report that in the peri- infarct area and in the corresponding contralateral hemisphere similar neuroplasticity occur through microglia activation and gephyrin phosphorylation at Ser268, Ser270in vivo. Targeted deletion of theBdnfgene in microglia orGphnS268A/S270A (phospho-null) point-mutations protect against ischemic brain damage, neuroinflamation and synapse downregulation normally seen post-MCAO. Collectively, we report that gephyrin phosphorylation and microglia derived BDNF faciliate synapse plasticity after transient ischemia.
Predicting cell fate commitment of embryonic differentiation by single-cell graph entropy
Authors: Zhong Jiayuan , Han Chongyin , Zhang Xuhang , Chen Pei , Liu Rui 
Published: April 2020

AbstractCell fate commitment occurs during early embryonic development, that is, the embryonic differentiation sometimes undergoes a critical phase transition or “tipping point” of cell fate commitment, at which there is a drastic or qualitative shift of the cell populations. In this study, we presented a novel computational approach, the single-cell graph entropy (SGE), to explore the gene-gene associations among cell populations based on single-cell RNA sequencing (scRNA-seq) data. Specifically, by transforming the sparse and fluctuating gene expression data to the stable local network entropy, the SGE score quantitatively characterizes the criticality of gene regulatory networks among cell populations, and thus can be employed to predict the tipping point of cell fate or lineage commitment at the single cell level. The proposed SGE method was applied to five scRNA-seq datasets. For all these datasets of embryonic differentiation, SGE effectively captures the signal of the impending cell fate transitions, which cannot be detected by gene expressions. Some “dark” genes that are non-differential but sensitive to SGE values were revealed. The successful identification of critical transition for all five datasets demonstrates the effectiveness of our method in analyzing scRNA-seq data from a network perspective, and the potential of SGE to track the dynamics of cell differentiation.