mteb/CovidQA huggingface dataset:
Another mRNA under post-transcriptional regulation by Regnase-1 and Roquin is Furin, which encodes a conserved proprotein convertase crucial in human health and disease. Furin, along with other PCSK family members, is widely implicated in immune regulation, cancer and the entry, maturation or release of a broad array of evolutionarily diverse viruses including human papillomavirus (HPV), influenza (IAV), Ebola (EboV), dengue (DenV) and human immunodeficiency virus (HIV). Here, Braun and Sauter review the roles of furin in these processes, as well as the history and future of furin-targeting therapeutics. 7 They also discuss their recent work revealing how two IFN-cinducible factors exhibit broad-spectrum inhibition of IAV, measles (MV), zika (ZikV) and HIV by suppressing furin activity. 8 Over the coming decade, I expect to see an ever-finer spatiotemporal resolution of host-oriented therapies to achieve safe, effective and broad-spectrum yet costeffective therapies for clinical use.
The increasing abundance of affordable, sensitive, high-throughput genome sequencing technologies has led to a recent boom in metagenomics and the cataloguing of the microbiome of our world. The MinION nanopore sequencer is one of the latest innovations in this space, enabling direct sequencing in a miniature form factor with only minimal sample preparation and a consumer-grade laptop computer. Nakagawa and colleagues here report on their latest experiments using this system, further improving its performance for use in resource-poor contexts for meningitis diagnoses. 9 While direct sequencing of viral genomic RNA is challenging, this system was recently used to directly sequence an RNA virus genome (IAV) for the first time. 10 I anticipate further improvements in the performance of such devices over the coming decade will transform virus surveillance efforts, the importance of which was underscored by the recent EboV and novel coronavirus (nCoV / COVID-19) outbreaks, enabling rapid
deployment of antiviral treatments that take resistance-conferring mutations into account.
Decades of basic immunology research have provided a near-complete picture of the main armaments in the human antiviral arsenal. Nevertheless, this focus on mammalian defences and pathologies has sidelined examination of the types and roles of viruses and antiviral defences that exist throughout our biosphere. One case in point is the CRISPR/Cas antiviral immune system of prokaryotes, which is now repurposed as a revolutionary gene-editing biotechnology in plants and animals. 11 Another is the ancient lineage of nucleocytosolic large DNA viruses (NCLDVs), which are emerging human pathogens that possess enormous genomes of up to several megabases in size encoding hundreds of proteins with unique and unknown functions. 12 Moreover, hundreds of human-and avian-infective viruses such as IAV strain H5N1 are known, but recent efforts indicate the true number may be in the millions and many harbour zoonotic potential. 13 It is increasingly clear that host-virus interactions have generated
truly vast yet poorly understood and untapped biodiversity. Closing this Special Feature, Watanabe and Kawaoka elaborate on neo-virology, an emerging field engaged in cataloguing and characterising this biodiversity through a global consortium. 14 I predict these efforts will unlock a vast wealth of currently unexplored biodiversity, leading to biotechnologies and treatments that leverage the host-virus interactions developed throughout evolution.
When biomedical innovations fall into the 'Valley of Death', patients who are therefore not reached all too often fall with them. Being entrusted with the resources and expectation to conceive, deliver and communicate dividends to society is both cherished and eagerly pursued at every stage of our careers. Nevertheless, the road to research translation is winding and is built on a foundation of basic research. Supporting industry-academia collaboration and nurturing talent and skills in the Indo-Pacific region are two of the four pillars of the National Innovation and Science Agenda. 2 These frame Australia's Medical Research and Innovation Priorities, which include antimicrobial resistance, global health and health security, drug repurposing and translational research infrastructure, 15 capturing many of the key elements of this CTI Special Feature. Establishing durable international relationships that integrate diverse expertise is essential to delivering these outcomes. To this
end, NHMRC has recently taken steps under the International Engagement Strategy 16 to increase cooperation with its counterparts overseas. These include the Japan Agency for Medical Research and Development (AMED), tasked with translating the biomedical research output of that country. Given the reciprocal efforts at accelerating bilateral engagement currently underway, 17 the prospects for new areas of international cooperation and mobility have never been more exciting nor urgent. With the above in mind, all contributions to this CTI Special Feature I have selected from research presented by fellow invitees to the 2018 Awaji International Forum on Infection and Immunity (AIFII) and 2017 Consortium of Biological Sciences (ConBio) conferences in Japan. Both Australia and Japan have strong traditions in immunology and related disciplines, and I predict that the quantity, quality and importance of our bilateral cooperation will accelerate rapidly over the short to medium term. By
expanding and cooperatively leveraging our respective research strengths, our efforts may yet solve the many pressing disease, cost and other sustainability issues of our time.
Haunted with and hunting for viruses

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089303/

SHA: c51c4f6146d0c636bc4dc3839c16b9e3ef52849a

Authors: Gao, George Fu; Wu, Ying
Date: 2013-08-07
DOI: 10.1007/s11427-013-4525-x
License: cc-by

Abstract: nan

Text: pecially with next-generation sequencing (NGS) for new virus genome discovery, e.g., Ruben Donis et al. [10] sequenced a bat-derived influenza virus genome by using NGS in 2012, raising a serious question as to whether or not our seasonal or pandemic flu might have another reservoir host. Chen and colleagues [11] confirmed the SFTSV independently by using NGS. Indeed, metagenomics analysis has yielded a great deal of new viruses, especially from the environment. Our actively hunting for new viruses has made some significant contributions for our understanding of virus ecology, pathogenesis and interspecies transmission.
Science China Life Sciences has focused on this hot topic in the event of the H7N9 outbreak after a comprehensive overview of the topic addressing HPAIV H5N1 in 2009 in the journal [12] [13] [14] . In this issue, six groups have been invited to present their recent findings on the emerging viruses, in addition to a previous report on H7N9 [3] .
Shi [15] reviewed recent discoveries of new viruses or virus genomes from bat. Bat is believed to harbor many more viruses than we ever thought as a reservoir host or even a susceptible host [16] . After the SARS-CoV virus, we have been actively seeking for new coronaviruses from bat and have yielded many of them, including potential human infecting HKU-1, 4, 5 and 9 [17, 18] . Recent MERS-CoV infection is another example for severe disease caused by used-to-be-less pathogenic coronaviruses. Shi and colleagues [19] by using NGS have discovered many unknown animal viruses from bat, especially some important paramyxoviruses and reoviruses. Filovirus has also been identified in bat with potential severe outcomes. Lyssaviruses (with many genotypes, including rabies virus) in the Rhabdoviridae family have been linked with severe fatal human cases, even in the developed countries, including Australia, with the bites of bats in the city [20, 21] . The potential roles of these viruses in bats
for interspecies transmission are yet to be elucidated.
Tan and colleagues [22] specifically focused on the newly-emerged MERS-CoV. The virus was identified in 2012 in the Middle East with some exported cases to Europe. In 2013 the virus has been re-emerging and expanding its borders to more European countries. In the initial diagnosis, the pan-coronavirus real-time reverse transcription polymerase chain reaction (RT-PCR) assay played a very important role for the identification of the causative agents. By using this method, scientists detected an expected-size PCR fragment for the corresponding conserved region of ORF1b of the replicase gene of a coronavirus. This is another example that molecular biology methods played for the discovery of new pathogens. Soon the receptor used by MERS-CoV to enter the host cells was identified [23] and the molecular basis of the receptor binding to the virus was also elucidated recently [8] .
Enterovirus has been known as serious human pathogens for a long time but their significance to the public health has been emphasized by the emergence of enterovirus 71 in 1998 as a serious pathogenic agents for children in Taiwan [24] and re-emerged in mainland China in 2008 [25] . In this issue, Duan and colleagues [26] summarized the findings of new enteroviruses by using NGS. Because of the application of new NGS technology they also challenged the Koch's postulates. A new model of Koch's postulates, named the metagenomic Koch's postulates, has provided guidance for the study of the pathogenicity of novel viruses. The review also provided a detailed description of the NGS and related molecular methods for the virus discovery followed by a list of new enteroviruses found in human feces. These include viruses in the family of Piconaviridae, Parvoviridae, Circoviridae, Astroviridae and Polyomaviridae.
Yu Xue-Jie and colleagues [27] reviewed the new bunyavirus, SFTSV, identified in China. As the virus discoverers, they have overviewed the whole process of the discovery, which is helpful and meaningful for the new virus discoveries in the future. The disease caused by SFTSV, with a CFR of 12%, had been in China for a couple of years before the causative agent was finally identified. There are still a lot of questions remained unknown for this new virus and vigorous studies are in great need. The transmission route of the virus has not been clarified but tick as vector is suspected. Domestic and wild animals, e.g., goats, boars, cattle and dogs, are believed to be the virus-amplifying hosts. Therefore the effective control measures are still under evaluation. Vaccines protecting the SFTSV infection are under its way in Chinese Center for Disease Control and Prevention. Recently a similar virus has been identified in both Japan and USA (a new name of Heartland virus was proposed for
the US virus) [9] .
In addition to new viruses infecting human beings, some new viruses infecting animals but their public health significance needing to be further evaluated, have also been discovered. The new flavivirus, duck egg-drop syndrome virus (DEDSV), is a good example. Su and colleagues [28] reviewed the characterization of the DEDSV and its disease form in this issue. The virus was found closely-related to a long-time-known virus, Tembusu virus [29, 30] . Initially, the disease was only found in egg-raising ducks but soon it was found in pigeons, chickens and geese [31, 32] . Yet the transmission vector, though mosquitoes are suspected, has not been identified. Due to the public health concerns of its related viruses, potential human infection of DEDSV should be evaluated.
Research on insect viruses is reviving in recent years. In this issue, Zhou and colleagues [33] reviewed the newly-identified insect viruses in China. Insects are the largest group of animals on the Earth therefore they also carry many more viruses. Studies on these viruses can provide useful knowledge for our understanding about animal or human infecting viruses. More importantly, modification and application of insect-infecting viruses can be used as effective biologicals for the control of insect pest. The new viruses identified include Wuhan nodavirus (WhNV), a member of family Nodaviridae; Dendrolimus punctatus tetravirus (DpTV), a new member of the genus Omegatetravirus of the family Alphatetravirida; Ectropis obliqua picorna-like virus (EoV), a positive-strand RNA virus causing a lethal granulosis infection in the larvae of the tea looper (Ectropis obliqua), the virus a member of the Flaviridae family.
While we are enjoying ourselves with the civilization of modern societies, the ecology has ever been changing. Human beings encounter more ecology-climate-changing problems, including the zoonotic pathogens. We have to face some unknown pathogenic agents passively. To get ourselves well prepared we also ought to actively hunt for unknown pathogens. Prediction and pre-warning can only be realized by knowing more about the unknown. This is especially true for infectious agents.
Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak

https://doi.org/10.3390/jcm9020388

SHA: bf20dda99538a594eafc258553634fd9195104cb

Authors: Zhao, Shi; Musa, Salihu S.; Lin, Qianying; Ran, Jinjun; Yang, Guangpu; Wang, Weiming; Lou, Yijun; Yang, Lin; Gao, Daozhou; He, Daihai; Wang, Maggie H.
Date: 2020
DOI: 10.3390/jcm9020388
License: cc-by
Abstract: Background: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from 1 to 15 January 2020, and thus we estimated the number of unreported cases and the basic reproduction number, R0, of 2019-nCoV. Methods: We modelled the epidemic curve of 2019-nCoV cases, in mainland China from 1 December 2019 to 24 January 2020 through the exponential growth. The number of unreported cases was determined by the maximum likelihood estimation. We used the serial intervals (SI) of infection caused by two other well-known coronaviruses (CoV), Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoVs, as approximations of the unknown SI for 2019-nCoV to estimate R0. Results: We confirmed that the initial growth phase followed an exponential growth pattern.
The under-reporting was likely to have resulted in 469 (95% CI: 403&minus;540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18&minus;25) in comparison to the situation from 1 to 17 January 2020 on average. We estimated the R0 of 2019-nCoV at 2.56 (95% CI: 2.49&minus;2.63). Conclusion: The under-reporting was likely to have occurred during the first half of January 2020 and should be considered in future investigation.
Text: A novel coronavirus (2019-nCoV) infected pneumonia infection, which is deadly [1] , was first identified in Wuhan, China in December 2019 [2] . The virus causes a range of symptoms including fever, cough, and shortness of breath [3] . The cumulative number of reported cases slowly increased to cumulative 41 cases by 1 January 2020, and rapidly increased after 16 January 2020. As of 26 January 2020, the still ongoing outbreak had resulted in 2066 (618 of them are in Wuhan) confirmed cases and 56 (45 of them were in Wuhan) deaths in mainland China [4] , and sporadic cases exported from Wuhan were reported in Thailand, Japan, Republic of Korea, Hong Kong, Taiwan, Australia, and the United States, please see the World Health Organization (WHO) news release via https://www.who.int/csr/don/en/ from 14 to 21 January 2020. Using the number of cases exported from Wuhan to other countries, a research group at Imperial College London estimated that there had been 4000 (95%CI: 1000-9700)
cases in Wuhan with symptoms onset by 18 January 2020, and the basic reproduction number (R 0 ) was estimated at 2.6 (95%CI: 1.5-3.5) [5] . Leung et al. drew a similar conclusion and estimated the number of cases exported from Wuhan to other major cities in China [6] , and the potentials of travel related risks of disease spreading was also indicated by [7] .
Due to an unknown reason, the cumulative number of cases remained at 41 from 1 to 15 January 2020 according to the official report, i.e., no new case was reported during these 15 days, which appears inconsistent with the following rapid growth of the epidemic curve since 16 January 2020. We suspect that the 2019-nCoV cases were under-reported roughly from 1 to 15 January 2020. In this study, we estimated the number of unreported cases and the basic reproduction number, R 0 , of 2019-nCoV in Wuhan from 1 to 15 January 2020 based on the limited data in the early outbreak.
The time series data of 2019-nCoV cases in mainland China were initially released by the Wuhan Municipal Health Commission from 10 to 20 January 2020 [8] , and later by the National Health Commission of China after 21 January 2020 [9] . The case time series data in December 2019 were obtained from a published study [3] . All cases were laboratory confirmed following the case definition by the national health commission of China [10] . We chose the data up to 24 January 2020 instead of to the present study completion date. Given the lag between timings of case confirmation and news release of new cases, the data of the most recent few days were most likely to be tentative, and thus they were excluded from the analysis to be consistent.
We suspected that there was a number of cases, denoted by ξ, under-reported from 1 to 15 January 2020. The cumulative total number of cases, denoted by C i , of the i-th day since 1 December 2019 is the summation of the cumulative reported, c i , and cumulative unreported cases, Ξ i . We have C i = c i + Ξ i , where c i is observed from the data, and Ξ i is 0 for i before 1 January and ξ for i after 15 January 2020. Following previous studies [11, 12] , we modelled the epidemic curve, i.e., the C i series, as an exponential growing Poisson process. Since the data from 1 to 15 January 2020 appeared constant due to unclear reason(s), we removed these data from the fitting of exponential growth. The ξ and the intrinsic growth rate (γ) of the exponential growth were to be estimated based on the log-likelihood, denoted by , from the Poisson priors. The 95% confidence interval (95% CI) of ξ was estimated by the profile likelihood estimation framework with cutoff threshold determined by a
Chi-square quantile [13] , χ 2 pr = 0.95, df = 1 . With γ estimated, the basic reproduction number could be obtained by R 0 = 1/M(−γ) with 100% susceptibility for 2019-nCoV presumed at this early stage. Here, the function M(·) was the Laplace transform, i.e., the moment generating function, of the probability distribution for the serial interval (SI) of the disease [11, 14] , denoted by h(k) and k is the mean SI. Since the transmission chain of 2019-nCoV remained unclear, we adopted the SI information from Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), which share the similar pathogen as 2019-nCoV [15] [16] [17] . We modelled h(k) as Gamma distributions with mean of 8.0 days and standard deviation (SD) of 3.6 days by averaging the SI mean and SD of SARS, mean of 7.6 days and SD of 3.4 days [18] , and MERS, mean of 8.4 days and SD of 3.8 days [19] .
We were also interested in inferring the patterns of the daily number of cases, denoted by ε i for the i-th day, and thus it is obviously that C i = C i−1 + ε i . A simulation framework was developed for the iterative Poisson process such that E[

denoted the expectation. The simulation was implemented starting from 1 January 2020 with a cumulative number of cases seed of 40, the same as reported on 31 December 2019. We conducted 1000 samples and calculated the median and 95% CI.
The number of 2019-nCoV unreported cases was estimated at 469 (95% CI: 403-540), see Figure 1a , which was significantly larger than 0. This finding implied the occurrence of under-reporting between 1 and 15 January 2020. After accounting for the effect of under-reporting, the R 0 was estimated at 2.56 (95% CI: 2.49-2.63), see Figure 1b , which is consistent with many existing online preprints with range from 2 to 4 [5, [20] [21] [22] . With the R 0 of 2.56 and ξ of 469, the exponential growing framework fitted the cumulative total number of cases (C i ) remarkably well, see Figure 1c iterative Poisson process such that

denoted the expectation. The simulation was implemented starting from 1 January 2020 with a cumulative number of cases seed of 40, the same as reported on 31 December 2019. We conducted 1000 samples and calculated the median and 95% CI.
The number of 2019-nCoV unreported cases was estimated at 469 (95% CI: 403−540), see Figure  1a , which was significantly larger than 0. This finding implied the occurrence of under-reporting between 1 and 15 January 2020. After accounting for the effect of under-reporting, the R0 was estimated at 2.56 (95% CI: 2.49−2.63), see Figure 1b , which is consistent with many existing online preprints with range from 2 to 4 [5, [20] [21] [22] . With the R0 of 2.56 and ξ of 469, the exponential growing framework fitted the cumulative total number of cases (Ci) remarkably well, see Figure 1c , referring to McFadden's pseudo-R-squared of 0.99. show the exponential growth fitting results of the cumulative number of cases (Ci) and the daily number of cases (εi) respectively. In panels (c) and (d), the gold squares are the reported cases, the blue bold curve represents the median of the fitting results, the dashed blue curves are the 95% CI of the fitting results, and the purple shading area
represents the time window from 1 to 15 January 2020. In panel (c), the blue dots are the cumulative total, i.e., reported and unreported, number of cases. In panel (d), the grey curves are the 1000 simulation samples.
Our estimation of R0 rely on the SI of 2019-nCoV, which remains unknown as of 26 January 2020. In this work, we employed the SIs of SARS and MERS as approximations to that of 2019-nCoV. The determination of SI requires the knowledge of the chain of disease transmission that needs a sufficient number of patient samples and periods of time for follow-up [23] , and thus this is unlikely to be achieved shortly. However, using SIs of SARS and MERS as approximation could provide an panels (a,b) , the green shading area represents the 95% CI (on the horizontal axis), and the vertical green line represents the maximum likelihood estimate (MLE) of the number of unreported cases. With the MLE of R 0 at 2.56, panels (c,d) show the exponential growth fitting results of the cumulative number of cases (C i ) and the daily number of cases (ε i ) respectively. In panels (c,d), the gold squares are the reported cases, the blue bold curve represents the median of the fitting results, the dashed blue
curves are the 95% CI of the fitting results, and the purple shading area represents the time window from 1 to 15 January 2020. In panel (c), the blue dots are the cumulative total, i.e., reported and unreported, number of cases. In panel (d), the grey curves are the 1000 simulation samples.
Our estimation of R 0 rely on the SI of 2019-nCoV, which remains unknown as of 26 January 2020. In this work, we employed the SIs of SARS and MERS as approximations to that of 2019-nCoV.

The determination of SI requires the knowledge of the chain of disease transmission that needs a sufficient number of patient samples and periods of time for follow-up [23] , and thus this is unlikely to be achieved shortly. However, using SIs of SARS and MERS as approximation could provide an insight into the transmission potential of 2019-nCoV at the early outbreak. We note that slightly varying the mean and SD of SI would not affect our main conclusions. The R 0 of 2019-nCoV was estimated at 2.56 (95% CI: 2.49-2.63), and it is generally in line with those of SARS, i.e., 2-5 [19, 24, 25] , and MERS, i.e., 2.7-3.9 [26] .