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Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2

Science

A minority of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmit most infections. How does this happen? Sun et al. reconstructed transmission in Hunan, China, up to April 2020. Such detailed data can be used to separate out the relative contribution of transmission control measures aimed at isolating individuals relative to population-level distancing measures. The authors found that most of the secondary transmissions could be traced back to a minority of infected individuals, and well over half of transmission occurred in the presymptomatic phase. Furthermore, the duration of exposure to an infected person combined with closeness and number of household contacts constituted the greatest risks for transmission, particularly when lockdown conditions prevailed. These findings could help in the design of infection control policies that have the potential to minimize both virus transmission and economic strain. Science , this issue p. [eabe2424][1] ### INTRODUCTION The role of transmission heterogeneities in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) dynamics remains unclear, particularly those heterogeneities driven by demography, behavior, and interventions. To understand individual heterogeneities and their effect on disease control, we analyze detailed contact-tracing data from Hunan, a province in China adjacent to Hubei and one of the first regions to experience a SARS-CoV-2 outbreak in January to March 2020. The Hunan outbreak was swiftly brought under control by March 2020 through a combination of nonpharmaceutical interventions including population-level mobility restriction (i.e., lockdown), traveler screening, case isolation, contact tracing, and quarantine. In parallel, highly detailed epidemiological information on SARS-CoV-2–infected individuals and their close contacts was collected by the Hunan Provincial Center for Disease Control and Prevention. ### RATIONALE Contact-tracing data provide information to reconstruct transmission chains and understand outbreak dynamics. These data can in turn generate valuable intelligence on key epidemiological parameters and risk factors for transmission, which paves the way for more-targeted and cost-effective interventions. ### RESULTS On the basis of epidemiological information and exposure diaries on 1178 SARS-CoV-2–infected individuals and their 15,648 close contacts, we developed a series of statistical and computational models to stochastically reconstruct transmission chains, identify risk factors for transmission, and infer the infectiousness profile over the course of a typical infection. We observe overdispersion in the distribution of secondary infections, with 80% of secondary cases traced back to 15% of infections, which indicates substantial transmission heterogeneities. We find that SARS-CoV-2 transmission risk scales positively with the duration of exposure and the closeness of social interactions, with the highest per-contact risk estimated in the household. Lockdown interventions increase transmission risk in families and households, whereas the timely isolation of infected individuals reduces risk across all types of contacts. There is a gradient of increasing susceptibility with age but no significant difference in infectivity by age or clinical severity. Early isolation of SARS-CoV-2–infected individuals drastically alters transmission kinetics, leading to shorter generation and serial intervals and a higher fraction of presymptomatic transmission. After adjusting for the censoring effects of isolation, we find that the infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom onset, with 53% of transmission occurring in the presymptomatic phase in an uncontrolled setting. We then use these results to evaluate the effectiveness of individual-based strategies (case isolation and contact quarantine) both alone and in combination with population-level contact reductions. We find that a plausible parameter space for SARS-CoV-2 control is restricted to scenarios where interventions are synergistically combined, owing to the particular transmission kinetics of this virus. ### CONCLUSION There is considerable heterogeneity in SARS-CoV-2 transmission owing to individual differences in biology and contacts that is modulated by the effects of interventions. We estimate that about half of secondary transmission events occur in the presymptomatic phase of a primary case in uncontrolled outbreaks. Achieving epidemic control requires that isolation and contact-tracing interventions are layered with population-level approaches, such as mask wearing, increased teleworking, and restrictions on large gatherings. Our study also demonstrates the value of conducting high-quality contact-tracing investigations to advance our understanding of the transmission dynamics of an emerging pathogen. ![Figure][2] Transmission chains, contact patterns, and transmission kinetics of SARS-CoV-2 in Hunan, China, based on case and contact-tracing data from Hunan, China. (Top left) One realization of the reconstructed transmission chains, with a histogram representing overdispersion in the distribution of secondary infections. (Top right) Contact matrices of community, social, extended family, and household contacts reveal distinct age profiles. (Bottom) Earlier isolation of primary infections shortens the generation and serial intervals while increasing the relative contribution of transmission in the presymptomatic phase. A long-standing question in infectious disease dynamics concerns the role of transmission heterogeneities, which are driven by demography, behavior, and interventions. On the basis of detailed patient and contact-tracing data in Hunan, China, we find that 80% of secondary infections traced back to 15% of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primary infections, which indicates substantial transmission heterogeneities. Transmission risk scales positively with the duration of exposure and the closeness of social interactions and is modulated by demographic and clinical factors. The lockdown period increases transmission risk in the family and households, whereas isolation and quarantine reduce risks across all types of contacts. The reconstructed infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom presentation. Modeling indicates that SARS-CoV-2 control requires the synergistic efforts of case isolation, contact quarantine, and population-level interventions because of the specific transmission kinetics of this virus. [1]: /lookup/doi/10.1126/science.abe2424 [2]: pending:yes


Learning the language of viral evolution and escape

Science

Viral mutations that evade neutralizing antibodies, an occurrence known as viral escape, can occur and may impede the development of vaccines. To predict which mutations may lead to viral escape, Hie et al. used a machine learning technique for natural language processing with two components: grammar (or syntax) and meaning (or semantics) (see the Perspective by Kim and Przytycka). Three different unsupervised language models were constructed for influenza A hemagglutinin, HIV-1 envelope glycoprotein, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. Semantic landscapes for these viruses predicted viral escape mutations that produce sequences that are syntactically and/or grammatically correct but effectively different in semantics and thus able to evade the immune system. Science , this issue p. [284][1]; see also p. [233][2] The ability for viruses to mutate and evade the human immune system and cause infection, called viral escape, remains an obstacle to antiviral and vaccine development. Understanding the complex rules that govern escape could inform therapeutic design. We modeled viral escape with machine learning algorithms originally developed for human natural language. We identified escape mutations as those that preserve viral infectivity but cause a virus to look different to the immune system, akin to word changes that preserve a sentence’s grammaticality but change its meaning. With this approach, language models of influenza hemagglutinin, HIV-1 envelope glycoprotein (HIV Env), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike viral proteins can accurately predict structural escape patterns using sequence data alone. Our study represents a promising conceptual bridge between natural language and viral evolution. [1]: /lookup/doi/10.1126/science.abd7331 [2]: /lookup/doi/10.1126/science.abf6894


Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic

Science

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence peaked in Manaus, Brazil, in May 2020 with a devastating toll on the city's inhabitants, leaving its health services shattered and cemeteries overwhelmed. Buss et al. collected data from blood donors from Manaus and São Paulo, noted when transmission began to fall, and estimated the final attack rates in October 2020 (see the Perspective by Sridhar and Gurdasani). Heterogeneities in immune protection, population structure, poverty, modes of public transport, and uneven adoption of nonpharmaceutical interventions mean that despite a high attack rate, herd immunity may not have been achieved. This unfortunate city has become a sentinel for how natural population immunity could influence future transmission. Events in Manaus reveal what tragedy and harm to society can unfold if this virus is left to run its course. Science , this issue p. [288][1]; see also p. [230][2] Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly in Manaus, the capital of Amazonas state in northern Brazil. The attack rate there is an estimate of the final size of the largely unmitigated epidemic that occurred in Manaus. We use a convenience sample of blood donors to show that by June 2020, 1 month after the epidemic peak in Manaus, 44% of the population had detectable immunoglobulin G (IgG) antibodies. Correcting for cases without a detectable antibody response and for antibody waning, we estimate a 66% attack rate in June, rising to 76% in October. This is higher than in São Paulo, in southeastern Brazil, where the estimated attack rate in October was 29%. These results confirm that when poorly controlled, COVID-19 can infect a large proportion of the population, causing high mortality. [1]: /lookup/doi/10.1126/science.abe9728 [2]: /lookup/doi/10.1126/science.abf7921


SARS-CoV-2 spillover events

Science

Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19 all broke out in recent decades and are caused by different strains of coronavirus (CoV). These viruses are considered to originate from bats and to have been transmitted to humans through intermediate hosts. SARS-CoV was identified in palm civets in wildlife markets and MERS-CoV in dromedary camels ([ 1 ][1]), but the direct source of the COVID-19 causative agent, SARS-CoV-2, is still undetermined. On page 172 of this issue, Oude Munnink et al. ([ 2 ][2]) report an in-depth investigation of SARS-CoV-2 infections in animals and humans working or living in 16 mink farms in the Netherlands. SARS-CoV-2 infections were detected in 66 out of 97 (68%) of the owners, workers, and their close contacts. Some people were infected with viral strains with an animal sequence signature, providing evidence of SARS-CoV-2 spillover back and forth between animals and humans within mink farms. Besides mink, multiple species of wild or domestic animals may also carry SARS-CoV-2 or its related viruses. Experimental infections and binding-affinity assays between the SARS-CoV-2 spike (a surface protein that mediates cell entry) and its receptor, angiotensin-converting enzyme II (ACE2), demonstrate that SARS-CoV-2 has a wide host range ([ 3 ][3]). After the SARS-CoV-2 outbreak, several groups reported SARS-related CoVs in horseshoe bats in China and in pangolins smuggled from South Asian countries, but according to genome sequence comparison, none are directly the progenitor virus of SARS-CoV-2 ([ 4 ][4]). Domestic cats and dogs, as well as tigers in zoos, have also been found to be naturally infected by SARS-CoV-2 from humans, but there is no evidence that they can infect humans, and so they are unlikely to be the source hosts of SARS-CoV-2 ([ 4 ][4], [ 5 ][5]). To date, SARS-CoV-2 infections in mink farms have been reported in eight countries (the Netherlands, Denmark, Spain, France, Sweden, Italy, the United States, and Greece), according to the World Organisation for Animal Health ([ 6 ][6]). In addition to animal-to-human transmission in farms, cold food supplier chains are raising substantial concern. In various cities in China, several small-scale COVID-19 outbreaks caused by virus-contaminated uncooked seafood or pork from overseas countries have been documented. It was found that viral genome signatures in these outbreaks were different from the viral strains present in China ([ 7 ][7], [ 8 ][8]). There is evidence that SARS-CoV-2 can survive up to 3 weeks in meat and on the surface of cold food packages without losing infectivity ([ 7 ][7], [ 8 ][8]). Thus, meat from SARS-CoV-2–infected animals or food packaging contaminated by SARS-CoV-2 could be a source of human infection (see the figure). This raises concerns about public health and agriculture in the prevention and control of SARS-CoV-2. Most SARS-CoV-2–infected animals do not display an obvious clinical syndrome, and infections would be unrecognized without routine diagnosis. The massive mink culling of infected farms is an efficient way to prevent further transmission of the virus. However, it cannot be applied to all domestic animals (if other species are found to be SARS-CoV-2 hosts). Thus, out of caution, extensive and strict quarantine measures should be implemented in all domestic farms with high-density animal populations. Because the virus is able to jump between some animals (such as mink) and humans, similar strategies should be applied to people in key occupations involving animal-human interfaces, such as animal farmers, zookeepers, or people who work in slaughterhouses. Notably, there is limited evidence of animal-to-human transmission of SARS-CoV-2 except for mink. Research on whether other domestic animals carry SARS-CoV-2, whether they can transmit it to humans, and factors related to spillover should be conducted. The RNA genome of SARS-CoV-2 seems relatively stable during transmission within human populations, although accumulated mutations have been detected. It is generally accepted that coronaviruses tend to exhibit rapid evolution when jumping to a different species. To keep the replication error rate low, coronaviruses encode several RNA-processing and proofreading enzymes that are thought to increase the fidelity of viral replication. However, viruses tend to have reduced fidelity in favor of adaptation to a new host species ([ 9 ][9]), although the mechanisms underlying this phenomenon are unclear. The coronaviral spike protein is prone to have more mutations because it is the first virus-host interaction protein and thus faces the strongest selection pressure. This molecular evolution can be observed in SARS-CoV genomes, which were under more adaptive pressure in the early stage of the epidemic (palm civet to human) than in later stages (human to human) ([ 10 ][10]). ![Figure][11] Possible SARS-CoV-2 transmission chains Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spillover likely occurred from bat and/or pangolin (ancestral virus) through unidentified intermediate host animals (direct progenitor virus). Human SARS-CoV-2 strains infect susceptible domestic animals (such as mink) and likely adapt to these species through mutation. The virus can be transmitted from mink back to farm workers and close contacts. SARS-CoV-2 can also be transmitted to humans through contact with contaminated uncooked meat or food packaging. GRAPHIC: MELISSA THOMAS BAUM/ SCIENCE Mutations that occur in SARS-CoV-2 in animals may increase its pathogenesis or transmissibility in humans. Five clusters of SARS-CoV-2 strains were found in mink, each characterized by a specific mink-related variant. In Denmark, the cluster 5 strain of mink SARS-CoV-2 was less immunogenic to COVID-19 patient serum than was human SARS-CoV-2 because of mutations of the spike proteins in the mink strains ([ 11 ][12]). This cluster 5 strain has infected at least 12 people, and the clinical presentation, severity, and transmission among those infected are similar to those of other circulating human SARS-CoV-2 strains ([ 12 ][13]). Currently, there is no evidence that any mutation from mink strains of SARS-CoV-2 escapes neutralization by antibodies designed to target the prevalent human strains. However, considering the possible risk of spillover of SARS-CoV-2 between humans and some animals, it is imperative to closely monitor mutations in the viral genome from infected animals and humans, particularly the genome regions affecting diagnostic tests, antiviral drugs, and vaccine development. It is anticipated that vaccines will allow control of COVID-19. Vaccines have been developed against the current prevalent viral strains and could face challenges if there is continued spillover from animals. The viral genome mutations likely produced during interspecies transmission between animals and humans raise concerns about whether the current vaccines can protect against emerging strains in the future. The extensive sequencing of viral genomes from animals and humans and worldwide data sharing will be central to efforts to monitor the key mutations that could affect vaccine efficacy. Laboratory-based studies should test whether the observed mutations affect key features of the virus, including pathogenesis, immunogenicity, and cross-neutralization. Moreover, preparedness of vaccines based on newly detected variants should be considered in advance. In the long term, vaccination of animals should also be considered to avoid economic losses in agriculture. There has been debate about whether bats or pangolins, which carry coronaviruses with genomes that are ∼90 to 96% similar to human SARS-CoV-2, were the animal source of the first human outbreak ([ 4 ][4]). Evolutionary analyses of viral genomes from bats and pangolins indicate that further adaptions, either in animal hosts or in humans, occurred before the virus caused the COVID-19 pandemic ([ 13 ][14]). Therefore, an animal species that has a high population density to allow natural selection and a competent ACE2 protein for SARS-CoV-2—mink, for example—would be a possible host of the direct progenitor of SARS-CoV-2. Another debate concerns the source of SARS-CoV-2 that caused the COVID-19 outbreak at the end of 2019. The current data question the animal origin of SARS-CoV-2 in the seafood market where the early cases were identified in Wuhan, China. Given the finding of SARS-CoV-2 on the surface of imported food packages, contact with contaminated uncooked food could be an important source of SARS-CoV-2 transmission ([ 8 ][8]). Recently, SARS-CoV-2 antibodies were found in human serum samples taken outside of China before the COVID-19 outbreak was detected ([ 14 ][15], [ 15 ][16]), which suggests that SARS-CoV-2 existed for some time before the first cases were described in Wuhan. Retrospective investigations of preoutbreak samples from mink or other susceptible animals, as well as humans, should be conducted to identify the hosts of the direct progenitor virus and to determine when the virus spilled over into humans. 1. [↵][17]1. J. Cui, 2. F. Li, 3. Z. L. Shi , Nat. Rev. Microbiol. 17, 181 (2019). [OpenUrl][18][CrossRef][19][PubMed][20] 2. [↵][21]1. B. B. Oude Munnink et al ., Science 371, 172 (2020). [OpenUrl][22] 3. [↵][23]1. L. Wu et al ., Cell Discov. 6, 68 (2020). [OpenUrl][24] 4. [↵][25]1. B. Hu, 2. H. Guo, 3. P. Zhou, 4. Z. L. Shi , Nat. Rev. Microbiol. 10.1038/s41579-020-00459-7 (2020). 5. [↵][26]1. D. McAloose et al ., mBio 11, e02220-20 (2020). [OpenUrl][27][Abstract/FREE Full Text][28] 6. [↵][29]World Organisation for Animal Health, COVID-19 Portal: Events in Animals (2020); [www.oie.int/en/scientific-expertise/specific-information-and-recommendations/questions-and-answers-on-2019novel-coronavirus/events-in-animals/][30]. 7. [↵][31]1. P. Liu et al ., Biosaf. Health 10.1016/j.bsheal.2020.11.003 (2020). 8. [↵][32]1. J. Han, 2. X. Zhang, 3. S. He, 4. P. Jia , Environ. Chem. Lett. 10.1007/s10311-020-01101-x (2020). 9. [↵][33]1. R. L. Graham, 2. R. S. Baric , J. Virol. 84, 3134 (2010). [OpenUrl][34][Abstract/FREE Full Text][35] 10. [↵][36]Chinese SARS Molecular Epidemiology Consortium, Science 303, 1666 (2004). [OpenUrl][37][Abstract/FREE Full Text][38] 11. [↵][39]European Centre for Disease Prevention and Control, “Rapid Risk Assessment: Detection of New SARS-CoV-2 Variants Related to Mink” (2020); [www.ecdc.europa.eu/sites/default/files/documents/RRA-SARS-CoV-2-in-mink-12-nov-2020.pdf][40]. 12. [↵][41]1. R. Lassauniere et al ., “SARS-CoV-2 spike mutations arising in Danish mink and their spread to humans” (2020); . 13. [↵][42]1. K. G. Andersen, 2. A. Rambaut, 3. W. I. Lipkin, 4. E. C. Holmes, 5. R. F. Garry , Nat. Med. 26, 450 (2020). [OpenUrl][43][CrossRef][44][PubMed][45] 14. [↵][46]1. G. Apolone et al ., Tumori J. 10.1177/0300891620974755 (2020). 15. [↵][47]1. S. V. Basavaraju et al ., Clin. Infect. Dis. ciaa1785 (2020). Acknowledgments: Supported by China National Science Foundation for Excellent Scholars award 81822028 (P.Z.) and Strategic Priority Research Program of the Chinese Academy of Sciences awards XDB29010101 (Z.-L.S.) and XDB29010204 (P.Z.). [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: pending:yes [12]: #ref-11 [13]: #ref-12 [14]: #ref-13 [15]: #ref-14 [16]: #ref-15 [17]: #xref-ref-1-1 "View reference 1 in text" [18]: {openurl}?query=rft.jtitle%253DNat.%2BRev.%2BMicrobiol.%26rft.volume%253D17%26rft.spage%253D181%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41579-018-0118-9%26rft_id%253Dinfo%253Apmid%252F30531947%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [19]: /lookup/external-ref?access_num=10.1038/s41579-018-0118-9&link_type=DOI [20]: /lookup/external-ref?access_num=30531947&link_type=MED&atom=%2Fsci%2F371%2F6525%2F120.atom [21]: #xref-ref-2-1 "View reference 2 in text" [22]: {openurl}?query=rft.jtitle%253DScience%26rft.volume%253D371%26rft.spage%253D172%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [23]: #xref-ref-3-1 "View reference 3 in text" [24]: {openurl}?query=rft.jtitle%253DCell%2BDiscov.%26rft.volume%253D6%26rft.spage%253D68%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [25]: #xref-ref-4-1 "View reference 4 in text" [26]: #xref-ref-5-1 "View reference 5 in text" [27]: {openurl}?query=rft.jtitle%253DmBio%26rft_id%253Dinfo%253Adoi%252F10.1128%252FmBio.02220-20%26rft_id%253Dinfo%253Apmid%252F33051368%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [28]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoibWJpbyI7czo1OiJyZXNpZCI7czoxNDoiMTEvNS9lMDIyMjAtMjAiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzEvNjUyNS8xMjAuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [29]: #xref-ref-6-1 "View reference 6 in text" [30]: http://www.oie.int/en/scientific-expertise/specific-information-and-recommendations/questions-and-answers-on-2019novel-coronavirus/events-in-animals/ [31]: #xref-ref-7-1 "View reference 7 in text" [32]: #xref-ref-8-1 "View reference 8 in text" [33]: #xref-ref-9-1 "View reference 9 in text" [34]: {openurl}?query=rft.jtitle%253DJ.%2BVirol.%26rft_id%253Dinfo%253Adoi%252F10.1128%252FJVI.01394-09%26rft_id%253Dinfo%253Apmid%252F19906932%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [35]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MzoianZpIjtzOjU6InJlc2lkIjtzOjk6Ijg0LzcvMzEzNCI7czo0OiJhdG9tIjtzOjIyOiIvc2NpLzM3MS82NTI1LzEyMC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30= [36]: #xref-ref-10-1 "View reference 10 in text" [37]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.1092002%26rft_id%253Dinfo%253Apmid%252F14752165%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [38]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIzMDMvNTY2NC8xNjY2IjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzcxLzY1MjUvMTIwLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [39]: #xref-ref-11-1 "View reference 11 in text" [40]: http://www.ecdc.europa.eu/sites/default/files/documents/RRA-SARS-CoV-2-in-mink-12-nov-2020.pdf [41]: #xref-ref-12-1 "View reference 12 in text" [42]: #xref-ref-13-1 "View reference 13 in text" [43]: {openurl}?query=rft.jtitle%253DNat.%2BMed.%26rft.volume%253D26%26rft.spage%253D450%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41591-020-0820-9%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [44]: /lookup/external-ref?access_num=10.1038/s41591-020-0820-9&link_type=DOI [45]: /lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fsci%2F371%2F6525%2F120.atom [46]: #xref-ref-14-1 "View reference 14 in text" [47]: #xref-ref-15-1 "View reference 15 in text"


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Science

![Figure][1] Eppendorf now offers a further building block in supporting scientists with tailored solutions for the daily lab routine. Controlled, reliable cell thawing is mandatory for further downstream experiments in every cell-handling lab. Although water-bath based or even manual thawing of cells is still commonly practiced, these methods are not as desirable due to their limited reproducibility. The Eppendorf ThermoMixer C now features an exchangeable thermoblock, the cryo-thaw SmartBlock, which provides a dedicated thawing program for reproducible, reliable thawing of cells from frozen storage conditions up to 37°C. In response to the community's need for highly specific and reproducible antibodies for SARS-CoV-1/2 research, MilliporeSigma has designed ZooMAb recombinant monoclonal antibodies against various SARS CoV 1/2 targets. ZooMAb antibodies are all recombinantly produced, lyophilized, and free of animal components. Explore our offering of ZooMAb recombinant monoclonal antibodies that are suitable for COVID-19 research: Anti-SARS-CoV-1/2 NP, clone 1C7C7 ZooMAb Mouse Monoclonal Nucleoprotein; Anti-SARS-CoV-1/2 S Protein, clone 2B3E5 ZooMAb Mouse Monoclonal SARS-CoV-1/2 Spike Glycoprotein; and Anti-SARS-CoV-1/2 S Protein clone hu2B3E5 ZooMAb Chimeric Monoclonal. The BioScaffolder Prime from Analytik is an affordable, high-performance 3D bioprinter that delivers precision engineering in an advanced, customizable platform. The rapidly expanding field of 3D bioprinting for tissue engineering and regenerative medicine combines biocompatible/biodegradable polymers with living cells. This bioprinter package offers researchers the ability to create bioscaffolds for cell growth and to deposit layers of bioinks on implants or microfluidic objects. The unit can be equipped with multiple dispensing tools, including unique core/shell tools for simultaneous dispensing of different materials. Decentralized units for printing, media control, and computing save precious space in your biosafety cabinet and ensure superb heat dissipation. Silent but smart XYZ-drives deliver micrometer precision. In addition, the system comes with a Peltier heater/cooler cartridge for temperature-controlled bioprinting and a built-in UV-source UV-LED pen. Designed to fit and operate in a standard biosafety cabinet, BioScaffolder Prime enables you to undertake your 3D printing applications quickly, safely, and in a sterile environment. Ziath reports strong uptake of its Mohawk semiautomated tube picker in smaller biobanks and biorepositories, which need to select tubes from cold racks straight from the freezer but cannot afford the huge investment in robotics required to automatically pick and place tubes. The small, compact Mohawk can pick up 16 tubes simultaneously from a 96-position tube rack. By elevating sample tubes in racks using solenoids, the Mohawk enables biobank operators to quickly retrieve the correct tubes and put them in the destination racks. Additionally, because the Mohawk can seamlessly connect with Ziath rack scanners, biobank users can read a picking list, select tubes, and verify that the correct tubes are picked—making the process of finding and selecting the right tubes in your biobank more efficient and economical. The Cold Coil II Flow Reactor Module from Uniqsis is a flexible, entry-level solution for low temperature flow chemistry applications. Used in conjunction with an external thermoregulation circulator, the unit can maintain stable temperatures between −78°C and 150°C for extended periods of time. It is compatible with all Uniqsis coil reactors, from 2.0 mL up to 60 mL capacity. A proprietary clamping mechanism holds the coil reactor firmly in place and ensures optimal thermal contact while allowing easy interchange of coil reactors. The Cold Coil II can be easily converted into a photoreactor by coupling it with a Uniqsis PhotoSyn high-power LED light module. It is also compatible with the Uniqsis HotColumn multiple-column reactor adaptor for packed-bed applications. To ensure accurate remote measurement of the Cold Coil II reactor temperature, an optional internal temperature probe can be connected directly via RS232C. The RAPID EPS (Easy Piercing Seal) from BioChromato is designed for scientists looking to prevent contamination issues and autosampler-needle clogging when accessing samples stored in 96-well microplates ready for LC/MS analysis. For LC/MS users, a key criterion for an effective microplate seal is its resistance to solvents such as acetonitrile, methanol, and DMSO, which are commonly used in experiments and analysis. The RAPID EPS uses a synthetic rubber adhesive to create a high-integrity, airtight seal with microplates, and shows no contamination in the eluents. In addition, the unique construction of BioChromato's RAPID EPS does not leave particulate material when pierced, further safeguarding your samples from contamination and eliminating potentially harmful effects to your LC/MS autosampler. The RAPID EPS is proven to offer dependable microplate sealing over a working temperature range of −80°C to 80°C.  [1]: pending:yes


AI-Assisted Cough Tracking Could Help Detect the Next Pandemic

#artificialintelligence

When Joe Brew worked for the Florida Department of Health as an epidemiologist for two years starting in 2013, he helped with syndromic surveillance, meaning he had the arduous job of reviewing the symptoms of patients coming into the emergency departments from all across the state. The goal of such work: to detect an abnormal spike of symptoms in an area that may indicate there's a public health concern. Public health authorities worldwide continue to use this type of surveillance. The outbreak of a novel pathogen in Wuhan, China in late 2019, for instance, was in part detected by a large uptick of patients coming to the hospital with symptoms of a respiratory infection, with unknown etiology. But Brew says this system fails to prevent the transmission of a virus like SARS-CoV-2 because by the time patients arrive at the hospital, they have likely already been infectious for a matter of days.


An Approach to Intelligent Pneumonia Detection and Integration

arXiv.org Artificial Intelligence

Each year, over 2.5 million people, most of them in developed countries, die from pneumonia [1]. Since many studies have proved pneumonia is successfully treatable when timely and correctly diagnosed, many of diagnosis aids have been developed, with AI-based methods achieving high accuracies [2]. However, currently, the usage of AI in pneumonia detection is limited, in particular, due to challenges in generalizing a locally achieved result. In this report, we propose a roadmap for creating and integrating a system that attempts to solve this challenge. We also address various technical, legal, ethical, and logistical issues, with a blueprint of possible solutions.


'The game has changed. AI triumphs at protein folding

Science

Artificial intelligence (AI) has solved one of biology's grand challenges: predicting how proteins fold from a chain of amino acids into 3D shapes that carry out life's tasks. This week, organizers of a protein-folding competition announced the achievement by researchers at DeepMind, a U.K.-based AI company. They say the DeepMind method will have far-reaching effects, among them dramatically speeding the creation of new medications. “What the DeepMind team has managed to achieve is fantastic and will change the future of structural biology and protein research,” says Janet Thornton, director emeritus of the European Bioinformatics Institute. “This is a 50-year-old problem,” adds John Moult, a structural biologist at the University of Maryland, Shady Grove, and co-founder of the competition, Critical Assessment of Protein Structure Prediction (CASP). “I never thought I'd see this in my lifetime.” The body uses tens of thousands of different proteins, each a string of dozens to hundreds of amino acids. The order of the amino acids dictates how the myriad pushes and pulls between them give rise to proteins' complex 3D shapes, which, in turn, determine how they function. Knowing those shapes helps researchers devise drugs that can lodge in proteins' crevices. And being able to synthesize proteins with a desired structure could speed development of enzymes to make biofuels and degrade waste plastic. ![Figure][1] CREDITS: (GRAPH) C. BICKEL/ SCIENCE ; (DATA) CASP For decades, researchers deciphered proteins' structures using experimental techniques such as x-ray crystallography or cryo–electron microscopy (cryo-EM). But such methods can take years and don't always work. Structures have been solved for only about 170,000 of the more than 200 million proteins discovered across life forms. In the 1960s, researchers realized if they could work out all interactions within a protein's sequence, they could predict its shape. But the amino acids in any given sequence could interact in so many different ways that the number of possible structures was astronomical. Computational scientists jumped on the problem, but progress was slow. In 1994, Moult and colleagues launched CASP, which takes place every 2 years. Entrants get amino acid sequences for about 100 proteins whose structures are not known. Some groups compute a structure for each sequence, while others determine it experimentally. The organizers then compare the computational predictions with the lab results and give the predictions a global distance test (GDT) score. Scores above 90 on the 100-point scale are considered on par with experimental methods, Moult says. Even in 1994, predicted structures for small, simple proteins could match experimental results. But for larger, challenging proteins, computations' GDT scores were about 20, “a complete catastrophe,” says Andrei Lupas, a CASP judge and evolutionary biologist at the Max Planck Institute for Developmental Biology. By 2016, competing groups had reached scores of about 40 for the hardest proteins, mostly by drawing insights from known structures of proteins that were closely related to the CASP targets. When DeepMind first competed, in 2018, its algorithm, called AlphaFold, relied on this comparative strategy. But AlphaFold also incorporated a computational approach called deep learning, in which the software is trained on vast data troves—in this case, the sequences and structures of known proteins—and learns to spot patterns. DeepMind won handily, beating the competition by an average of 15% on each structure, and winning GDT scores of up to about 60 for the hardest targets. But the predictions were still too coarse, says John Jumper, who heads AlphaFold's development at DeepMind. “We knew how far we were from biological relevance.” So the team combined deep learning with an “attention algorithm” that mimics the way a person might assemble a jigsaw puzzle: connecting pieces in clumps—in this case clusters of amino acids—and then searching for ways to join the clumps in a larger whole. Working with a computer network built around 128 machine learning processors, they trained the algorithm on all 170,000 or so known protein structures. And it worked. In this year's CASP, AlphaFold achieved a median GDT score of 92.4. For the most challenging proteins, AlphaFold scored a median of 87, 25 points above the next best predictions. It even excelled at solving structures of proteins that sit wedged in cell membranes, which are central to many human diseases but notoriously difficult to solve with x-ray crystallography. Venki Ramakrishnan, a structural biologist at the Medical Research Council Laboratory of Molecular Biology, calls the result “a stunning advance on the protein folding problem.” All groups in this year's competition improved, Moult says. But with AlphaFold, Lupas says, “The game has changed.” The organizers even worried DeepMind may have cheated somehow. So Lupas set a special challenge: a membrane protein from a species of archaea, an ancient group of microbes. For 10 years, his team had tried to get its x-ray crystal structure. “We couldn't solve it.” But AlphaFold had no trouble. It returned a detailed image of a three-part protein with two helical arms in the middle. The model enabled Lupas and his team to make sense of their x-ray data; within half an hour, they had fit their experimental results to AlphaFold's predicted structure. “It's almost perfect,” Lupas says. “They could not possibly have cheated on this. I don't know how they do it.” As a condition of entering CASP, DeepMind—like all groups—agreed to reveal sufficient details about its method for other groups to re-create it. That will be a boon for experimentalists, who will be able to use structure predictions to make sense of opaque x-ray and cryo-EM data. It could also enable drug designers to work out the structure of every protein in new and dangerous pathogens like SARS-CoV-2, a key step in the hunt for molecules to block them, Moult says. Still, AlphaFold doesn't do everything well. In CASP, it faltered on one protein, an amalgam of 52 small repeating segments, which distort each others' positions as they assemble. Jumper says the team now wants to train AlphaFold to solve such structures, as well as those of complexes of proteins that work together to carry out key functions in the cell. Even though one grand challenge has fallen, others will undoubtedly emerge. “This isn't the end of something,” Thornton says. “It's the beginning of many new things.” [1]: pending:yes


Artificial intelligence tool cracks code to imagine proteins in 3D

#artificialintelligence

An artificial intelligence network solved a scientific problem that has stumped researchers for half a century, successfully predicting the way proteins fold into three-dimensional shapes, a process that has typically taken expensive and painstaking lab work that could go on for decades. The way proteins, one of the building blocks of life, fold drives their functionality and behaviour. For instance, SARS-Cov-2 has a protein that folds as a spike. This shape, therefore, is relevant for biologists (including for its ability to find cures for illnesses). It isn't easy to predict the shape of a protein, though, based on the way amino acids come together to form a protein.


Artificial Intelligence

#artificialintelligence

Over the past decade, artificial intelligence (AI) has experienced a renaissance. AI enables machines to learn and make decisions without being explicitly programmed. AI has enabled a new generation of applications, opening the door to breakthroughs in many aspects of daily life. From situational awareness to threat detection, online signals to system assurance, PNNL is advancing the frontiers of scientific research and national security by applying AI to scientific problems. For machine learning models, domain-specific knowledge can enhance domain-agnostic data in terms of accuracy, interpretability, and defensibility. PNNL's AI research has been applied across a variety of domain areas from national security, to the electric grid and Earth systems.