Ex situ collections offer the potential to reduce extinction risks, affording option to society in maintaining future breeding opportunities for productivity and heritage traits. However, how much should we be seeking to collect and conserve in gene banks, and where? We developed a mathematical model to optimize logistical decisions of breed conservation choices and to evaluate alternative scenarios for efficiently re‐allocating genetic materials currently stored in different European gene banks, allowing for cross‐country collections, cost and cryogenic capacity differentials. We show how alternative allocations for the breeds that are currently stored in 11 European gene banks could reduce overall conservation costs by around 20% by selecting cryogenic banks that have relatively lower combination of fixed and collection costs, and are geographically closer to collection regions. Our results show that centralizing collections in one gene bank would double the costs, relative to collective European collections approaches. We also calculate marginal costs of collections and show that increasing diversity within the gene banks implies in higher costs per conserved breed. Other national and global plant and animal collections are held as public good resources in networks under The International Treaty on Plant Genetic Resources for Food and Agriculture . But other national and regional collections are more proprietary, offering restricted access usually through subscription. More generally, the academic literature on ex situ conservation is skewed towards storage of plant materials. There have been limited efforts to detail or audit animal collections. The European Gene bank Network for Animal Genetic Resources (EUGENA), coordinated by the European Regional Focal Point on Animal Genetic Resources (http://www.rfp-europe.org), is an emerging networking activity specifically targeting national farm animal genetic resource collections  collaborative project  breeds so that this genetic diversity might be available for use in the future. But the efficacy of collections is also largely anecdotal, with some concern that materials stored in gene banks may be compromised, or become redundant or mismatched with independently evolved in situ conditions , a type of optimization model consisting of both integer (stored breeds) and continuous variables  as opposed to Linear Programing (LP) which contains only continuous variables . Logistical considerations are also inevitable and include conservation budgets or collection and maintenance costs of gene banks (GBs). The management decision may be further complicated by rationalization options; that is, keeping fewer collections at one or several geographical locations. This objective might be reasonable in the context of a collaborative research network where the free exchange and use of materials are a common goal. This study the has two objectives: (a) to identify least‐cost logistical strategies for material collection and storage; and (b) to estimate diversity costs for different livestock species, measured as the cost per conserved breed. Mathematical modelling can be used to rationalize multiple genetic collections covering overlapping material at several locations.2.2facility location problem , budget limitations, limited cryogenic tank capacity, maintenance and freezing costs of genetic material, and distances between gene banks and farm zones. The model currently only considers semen collection, accounting for over 90% of materials stored in the GBs covered in this study  and constraints. Table gbC.Equation  represenb,gbSB represents the number of semen doses of breed b stored in gene bank gb.Equation  represenb,gbin terms of semen b collected by gene bank gb at a collection point in region j , or semen b collected by gene bank gb at farm zone j .Equation  defines b,gb by imposing the model collects zero, or a value that is in a pre‐defined interval between a minimum number of semen doses of breed b (μb) and no more than bM doses.Equation  adds a fgb as the sum of maintenance costs (MCgb) and collection costs (VCgb).Equation  defines gb as the sum of fixed annual costs (gbF) and variable maintenance costs given by the product of storing and freezing costs of gene bank gb (mcgb) and the total number of semen doses in gene bank gb .Equation  defines gb (CCgb) and travel costs from gene bank gb (TCgb).Equation  defines b collected by gene bank gb at all farm zones j; sum over j , multiplied by the required number of animal donors per semen dose (br) and semen collection costs per dose (bcf) of breed b.Equation  represengb (gbTC) given by the costs of collections at farm zones and at collection points, respectively the first and second terms in the right hand side (RHS) of Equation . The multiplying parameters inside the sum, jd and gb,jD represent the average distance from region of gene bank j to farm zones and the distance from gene bank gb to gene bank j, respectively. The parameter gbtc represents the average costs per unit of distance; gbk represents the average number of semen doses collected per journey by gene bank gb. The second term in the RHS is analogous to the first term but with jd =0.Equation  describeEquation . The firb,je is binary vector indicating if breed b is available for collection in the region of gene bank gb ; b,gbA corresponds to the number of doses of breed b currently stored in gene bank gb.Equation  is used gb  cannot be greater than the capacity of gene bank gb,gb cp.Equation  represenTB represents the total European collective budget.Equation  is the c2.4b,gb, which increases the model solving complexity and breaks its linearity. The value of SBb,gb must be either zero or between a particular bound. This is a necessary assumption as the number of stored doses of semen can be zero; but if greater than zero, needs to be between an interval, for example greater than 400 doses but less than 600 doses for cattle breeds. We use a linear programming trick ∑gbSThe model was written in AIMMS algebraic language , measured by the number of selected breeds, as a function of n available budgets (iB) as follows:While the previous scenarios are explored by minimizing collection costs, a sensitivity analysis employs a diversity function Equation  to estim0B represents the initial budget; i represents the budget scenario and varies from 1 to n = 100 (an arbitrary number of scenarios); and BMax is the maximum available budget. For all iB's, the value of diversity is calculated as iD = f(iB). Where f(iB) represents the optimal solution of the MIP model when maximizing the diversity function. iD and iB are plotted as y‐and x‐axis to define efficient cost curves for each of the livestock species.where 30 scenario assumes no cross‐regional collection strategies. Of the 489 breeds, 55% of semen doses are cattle, 25% are sheep, 9% pig, 4% poultry, followed by goat and horse, together representing 3% of the total collected doses. Figure 0S shows overlapping collection of cattle, goat, sheep and pigs, and most significantly for cattle; for example, the cattle breed Blonde D'Aquitaine is currently stored in five different banks, varying from 50 semen doses (B11) to 9,670 (B1) . If the breeds in Figure UCS reduces collection costs by transferring cattle breeds from GBs 6, 7, 9 and 11 to GBs 1, 2 and 10, relative to 0S, sheep breeds from GB 3 to GB 11, horse breeds from B7 to B1, while poultry and pigs are kept approximately the same as S0. In fact, Table Figure C50S) reduces the required budget by 19% relative to 0S, or by 4.3 M EUR. Figure UC, is transferring cattle breed doses from B2 and B10 to B6 and B11 due to the first two being above the gene banks capacity. This is because under UCS only BANK 2 exceeds the assumed tank capacity, thus moving to the closest GB, BANK 11, which is only 39.7 km away .As well as being risky, collecting all materials in one of the existing cryogenic banks considered in this study is a relatively expensive option compared with multiple banks. This is because there is no GB with storage and collection costs cheaper for all species, and storing in a single GB increases cross‐country transportation costs because some breeds are unavailable for collection in a single region.Our results could be refined by improved cost estimates for collections and the addition of more breeds and country collections not taken into account in the analysis, for example, from FAO DAD‐IS database (FAO‐DADIS, A further apparent limitation is the representativeness of the cost survey, or more specifically varying banking technologies. A bank with lower storage costs may imply poorer conservation quality and higher failure risks, which we would ideally include as a specific variable in the optimization problem.The analysis further suggests that the diversity‐cost curve varies for breeds of each species. Since the surveys did not seek to understand breed‐specific weighting criteria, the diversity‐cost curves do not discriminate in terms of important breed‐specific attributes, for example, related to greater or lower expected economic returns, breed endangerment, susceptibility to climate change or cultural attributes.Like relative failure risks, these attributes could be included in the MIP as stochastic parameters, for example by forcing the model to select breeds with probability of extinction greater than a threshold value, variance of expected return, or probability of successful restoration. Assignment of breed‐specific and technology attribute weights to the diversity function will change the shape of the curves.Many breed attributes are likely to be prominent in any survey of public preferences for conservation spending, which we suspect would mirror priorities for public good provision related to in situ conservation decisions. However, this raises a further question about the demand for ex situ collections, and whether they should serve public or private good objectives; the latter focussed largely on animal productivity traits. This largely depends on ownership and how they are financed. The survey by Passmard et al  suggeste5Rapid progress in the development of next generation gene sequencing and bioinformatic tools have revolutionised animal breeding, but potentially distracted from a basic problem of what genetic and reproductive materials to collect and store, and how stored information is consistently recorded. Breed and gene bank selection clearly involves numerous biotechnological, institutional and economic challenges that can be informed by mathematical modelling of cost‐effective breed conservation.For given objectives and constraints our model provides some indication of potential rationalization options and demonstrates the increasing marginal costs of conservation effort. The exercise begs important questions about the specific optimization objectives, which in turn require more institutional coordination to define the mix of private and public good objectives and hence potential cost and benefit sharing. This implies clearer articulation of in situ risks including endangerment due to climate change and other pressures, expected economic returns and other attributes that determine stakeholders’ conservation preferences. There is also a need to improve understanding of the efficacy of technologies developed for ex situ curation and associated risks of successful use in future agricultural scenarios.The authors declare no conflict of interest.


We challenged chickens, turkeys, ducks, quail, and geese with severe acute respiratory syndrome coronavirus 2 or Middle East respiratory syndrome coronavirus. We observed no disease and detected no virus replication and no serum antibodies. We concluded that poultry are unlikely to serve a role in maintenance of either virus. MERS-CoV causes lower respiratory disease, similar to the SARS-CoVs  have been used for virus isolation culture, including use in vaccine production, for diverse avian and mammalian viruses; therefore, we tested ECE for their ability to support the replication of both viruses.Gallus gallus domesticus), turkeys , Pekin ducks (Anas platyrhinchos domesticus), Japanese quail (Coturnix japonica), and white Chinese geese (Anser cygnoides). The US National Poultry Research Center Institutional Animal Care and Use Committee reviewed and approved all procedures involving animals; the Institutional Biosafety Committee approved the use of the viruses.We examined 5 poultry species: chickens . We used either the USA-WA1/2020 isolate of SARS-CoV-2 (BEI NR-58221) or the Florida/USA-2_SaudiArabia_2014 isolate of MERS-CoV (BEI NR-50415) . We collected oropharyngeal and cloacal swabs from all birds at 2, 4, and 7 days postchallenge (dpc) and tested them for virus by real-time reverse transcription PCR. At 14 dpc we collected serum specimens from the birds and tested for antibody to the challenge virus by microneutralization. No clinical signs were observed at any time in any species, and virus was not detected in any swab material . AntibodWe tested ECE for their ability to support SARS-CoV-2 or MERS-CoV replication after inoculation with any of the 3 most common routes: yolk sac, chorioallantoic sac, or chorioallantoic membrane . We collIdentifying potential reservoir hosts of the novel coronaviruses is critical to controlling exposure and subsequent infection, as well as to preserving a safe and consistent food supply. None of the avian species nor the ECE appeared to support replication of either virus. Our findings demonstrate that poultry are unlikely to serve a role in the maintenance or transmission of either SARS-CoV-2 or MERS-CoV, and furthermore that ECE are not a viable laboratory host system.Additional information about susceptibility of poultry to SARS-CoV-2 and MERS-CoV.


Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common inherited kidney diseases characterized by progressive development of renal cysts and numerous extra-renal manifestations, eventually leading to kidney failure. Given its chronic and progressive nature, ADPKD is expected to carry a substantial economic burden over the course of the disease. However, there is a paucity of evidence on the impact of ADPKD from a societal perspective. This study aimed to estimate the direct and indirect costs associated with ADPKD in the United States (US).A prevalence-based approach using data from scientific literature, and governmental and non-governmental organizations was employed to estimate direct healthcare costs , direct non-healthcare costs , and indirect costs . The incremental costs associated with ADPKD were calculated as the difference between costs incurred over a one-year period by individuals with ADPKD and the US population. Sensitivity analyses using different sources and assumptions were performed to assess robustness of estimates and account for variability in published estimates.The estimated total annual costs attributed to ADPKD in 2018 ranged from $7.3 to $9.6 billion in sensitivity analyses, equivalent to $51,970 to $68,091 per individual with ADPKD. In the base scenario, direct healthcare costs accounted for $5.7 billion (78.6%) of the total $7.3 billion costs, mostly driven by patients requiring renal replacement therapy . Indirect costs accounted for $1.4 billion (19.7%), mostly driven by productivity loss due to unemployment  and reduced productivity at work . Total excess direct non-healthcare costs were estimated at $125 million (1.7%).ADPKD carries a considerable economic burden, predominantly attributed to direct healthcare costs, the majority of which are incurred by public and private healthcare payers. Effective and timely interventions to slow down the progression of ADPKD could substantially reduce the economic burden of ADPKD. Autosomal dominant polycystic kidney disease (ADPKD) is the most common of the inherited renal cystic diseases, a group of related but pathologically distinct disorders characterized by the progressive development of renal cysts . The couGiven that ADPKD is a life-long and progressive disorder, a substantial economic burden over the course of the disease is expected, including a variety of treatment-related and other costs –13. Few The economic burden of ADPKD from a US societal perspective remains unknown. With rising healthcare costs, there is a growing need for comprehensive estimates of the economic burden of ADPKD to inform clinical practice and decision-making.Therefore, the objective of this study was to estimate the economic burden of ADPKD from a US societal perspective using the published literature and government and non-profit data sources.The economic burden associated with ADPKD was estimated for 2018 and comprised direct healthcare costs, direct non-healthcare costs, and indirect costs. Cost components were identified from scientific publications, governmental agencies, and non-governmental organizations. Costs were stratified by disease stage when information was available.Costs associated with ADPKD were calculated using a prevalence-based approach with a prevalence of 0.043% and the estimated 2018 US adult population from the US Census Bureau , 15. TheExcess direct healthcare costs associated with ADPKD were calculated based on the average annual excess direct healthcare costs incurred by an individual with ADPKD compared to those incurred by an individual from the US population, stratified by disease stage, multiplied by the estimated number of individuals with ADPKD by disease stage in the US , 17. ExcDirect non-healthcare cost components included research, training, advocacy, matching of donors and recipients for kidney transplant, and transportation to and from dialysis centers. Costs of research, training, and advocacy were drawn directly from the estimates of funding for research on PKD reported by the National Institute of Health (NIH) and from the annual report of the PKD Foundation , 32. CosThe studied components of indirect costs were reduced productivity at work, loss of productivity due to unemployment, loss of productivity from premature mortality, and caregiver burden, as previously identified by Cloutier et al. . The latCosts associated with productivity loss from unemployment were estimated based on the excess number of unemployed individuals with ADPKD multiplied by the age-adjusted average annual wage in the US employed population , 16, 36.Reduced productivity at work was assessed based on the productivity weight in the ADPKD employed population by disease stage compared to the US population. Costs associated with this reduced productivity were further calculated based on the number of employed individuals with ADPKD by disease stage and the average annual wage in the US employed population given the average age of working-age individuals with ADPKD in each disease stage. The number of employed individuals with ADPKD was based on the number of individuals with ADPKD by disease stage and the employment-to-population ratio in the ADPKD population by disease stage , 36–38.Premature all-cause mortality is associated with a productivity loss for the society. These costs were estimated using the following components by age group: (1) all-cause excess mortality rate in the ADPKD population compared to the US general population , 39, (2)The productivity loss from caregiving was defined as the number of unpaid hours of care received by individuals with ADPKD . The assMoreover, to obtain excess healthcare costs incurred by caregivers of individuals with ADPKD, the estimated excess direct healthcare costs incurred by a family caregiver compared to those of a non-caregiver were multiplied by the number of individuals with ADPKD receiving assistance from family caregivers by disease stage , 43, 44.Four sensitivity analyses were conducted to assess the impact of using different sources and assumptions on the estimates. First, an adjustment was made to the estimated excess direct healthcare costs among publicly insured individuals to reflect potentially higher excess direct healthcare costs compared to those of commercially insured individuals . Second,The total ADPKD population in the US in 2018 was estimated at 140,598 individuals based on a prevalence of 0.043%. The economic burden associated with ADPKD for these individuals was estimated at $7.3 billion in 2018, that is $51,970 per individual with ADPKD . Costs of transportation to and from dialysis centers among individuals with ADPKD in the US were estimated at $81.4 million, that is $9112 per individual with ADPKD using in-center dialysis (n = 8936). Collectively, excess direct non-healthcare costs were estimated at $124.7 million.Costs of research, training, and advocacy for ADPKD in the US were estimated at $35.7 million, that is, $29.2 million for NIH-funded research and $6.6 million for the PKD Foundation. Costs of matching donors and recipients for kidney transplant among individuals with ADPKD were estimated at $7.6 million, that is, $979 per individual with ADPKD waiting for a kidney transplant . In sensitivity analyses, the economic burden associated with ADPKD was estimated at up to $9.6 billion, that is, $68,091 per individual with ADPKD. By assuming that Medicare-insured individuals incur 14.4% higher direct healthcare costs than commercially insured individuals and that uninsured individuals with ADPKD incur 3 to 18 times higher direct healthcare costs than the uninsured US population (depending on disease stage), estimated total excess direct healthcare costs increased from $5.7 billion to $6.1 billion , 18, 46.This study estimated the societal costs of ADPKD in the US in 2018 and found that ADPKD was associated with a significant burden. Indeed, incremental costs associated with ADPKD were estimated at $7.3 billion, equivalent to $51,970 per individual with ADPKD. In sensitivity analyses based on different assumptions, estimates ranged up to $9.6 billion, equivalent to $68,091 per individual with ADPKD. The largest contributor to excess costs of ADPKD was direct healthcare costs , over half of which were incurred by individuals with ESRD-RRT. Excess indirect costs were estimated at $1.4 billion , driven primarily by costs of unemployment  and reduced productivity at work . Total excess direct non-healthcare costs were estimated at $125 million.Prior studies reporting on the burden of ADPKD are scarce, with studies reporting both direct and indirect costs associated with ADPKD being based on non-US populations and studies based on the US population focusing on direct costs only. One study that examined both direct and indirect costs of ADPKD among a population from four Nordic countries  showed that the economic burden of ADPKD was substantial at all levels of disease. It also found that progression was associated with increased resource use and costs including direct healthcare costs and indirect costs related to productivity loss and informal care . DependiIn a US context, there is scant information on the burden of ADPKD from a societal perspective as prior studies estimating the costs of ADPKD in the US focused on direct healthcare costs only and did not include indirect costs , 10, 11.Studies focusing only on direct healthcare costs do not fully account for the societal burden associated with ADPKD, as indirect costs related to work productivity loss and caregiver support account for a significant proportion of overall costs in this population. Indeed, as this study found, indirect costs accounted for 19.7% of the burden associated with ADPKD in the US. Importantly, although rare diseases, by definition, have a low prevalence, they can nonetheless carry a significant overall economic burden, comparable to more common diseases, but one that translates to a greater cost per patient. For example, the total incremental cost of ADPKD estimated at $7.3 billion in the present study amounts to $51,970 per patient per year–nearly four times higher than that estimated for diabetes per patient , another disease that can lead to CKD, with a higher prevalence compared to ADPKD . In the Rare diseases in general are subject to high unmet needs in part due to limited knowledge of the disease and the challenges in conducting intensive research to develop therapeutic options for a rare patient population. Given the considerable burden per patient, any treatment strategy targeted at improving or delaying disease manifestations in ADPKD carries a high potential for reducing disease burden. Our findings showed that most of the burden associated with ADPKD was incurred by individuals with ESRD-RRT, suggesting that effective treatments and practices targeted at alleviating the manifestations of the disease and preventing decline of renal function have the potential to reduce healthcare costs in these patients, as well as indirect productivity loss and caregiver burden. In particular, as hospital readmissions have been shown to contribute to higher costs in patients with ESRD-RRT , effortsThis study is subject to a number of limitations. Direct healthcare costs were calculated among individuals with a recorded diagnosis for ADPKD who had symptomatic disease , and thus may not fully reflect the costs incurred from a societal perspective as undiagnosed and/or asymptomatic individuals may have incurred costs which were not captured. Other costs may not have been captured in the current analysis. For example, there is scarce information on costs incurred by live kidney donors; therefore, these costs were not included. Similarly, costs associated with disability leaves may not have been fully captured under the productivity loss component as there was limited information on the extent to which individuals on disability leave were included in the studies used for the calculations. Another limitation is that there was scant information on direct excess healthcare costs incurred by individuals with ADPKD insured with Medicare or Medicaid, such that estimates of excess direct healthcare costs heavily relied on estimates among commercially insured individuals. While sensitivity analyses were conducted to account for potential differences between insurance types, further studies are warranted to confirm estimates for these populations. Moreover, there was little information on the number of hours devoted to caregiving by patient’s disease stage , and there were important variations in published estimates from different sources. While sensitivity analyses were conducted to account for various estimates, further studies are warranted to confirm the unpaid time devoted to caring for individuals with ADPKD in the US. Finally, in the absence of a single data source for costs related to ADPKD, several estimates from the literature and governmental publications were combined, including ex-US sources and/or sources pertaining to renal diseases  which may not be entirely representative of the US context. Similarly, while the costs incurred by patients with ADPKD should have ideally been compared to those of individuals with similar characteristics, identification of comparison groups of individuals without ADPKD was limited by the scope of the published literature and data from government and non-profit organizations. For example, the excess direct healthcare costs  were derived from a cohort of individuals with ADPKD and a sex- and age-matched cohort of individuals without ADPKD . Yet, otADPKD imposes substantial societal costs. Total annual excess costs associated with ADPKD were estimated at $7.3 billion, equivalent to $51,970 per individual with ADPKD in the US in 2018. While direct healthcare costs are the main drivers of the burden of ADPKD, indirect costs are substantial from a societal perspective and correspond to about 20% of the annual burden of ADPKD, with most of these costs incurred by individuals with ESRD-RRT. The findings of this study suggest that timely and effective treatment to alleviate the manifestations of the disease may significantly reduce the burden of ADPKD on individuals and society.


Virus taxonomy emerged as a discipline in the middle of the twentieth century. Traditionally, classification by virus taxonomists has been focussed on the grouping of relatively closely related viruses. However, during the past few years, the International Committee on Taxonomy of Viruses (ICTV) has recognized that the taxonomy it develops can be usefully extended to include the basal evolutionary relationships among distantly related viruses. Consequently, the ICTV has changed its Code to allow a 15-rank classification hierarchy that closely aligns with the Linnaean taxonomic system and may accommodate the entire spectrum of genetic divergence in the virosphere. The current taxonomies of three human pathogens, Ebola virus, severe acute respiratory syndrome coronavirus and herpes simplex virus 1 are used to illustrate the impact of the expanded rank structure. This new rank hierarchy of virus taxonomy will stimulate further research on virus origins and evolution, and vice versa, and could promote crosstalk with the taxonomies of cellular organisms. Here, the International Committee on Taxonomy of Viruses describe a new, expanded virus classification scheme with 15 ranks that closely aligns with the Linnaean taxonomic system and better encompasses viral diversity. Subsequently, their pathogenicity and ability to undergo rapid evolutionary change6 has sparked a large body of research, often connected to the so-called ‘microevolution’ of relatively closely related viruses8. However, over the last decade, our appreciation of the importance and distribution of viruses has expanded beyond the original parasitic–pathogen model, and now virologists recognize the role of viruses in host regulation and the maintenance of natural ecosystems9. Shotgun metagenomic sequencing has also revealed the presence of a vast variety of viruses in diverse environmental samples and in apparently healthy organisms from all divisions of life13.Viruses were discovered at the end of the nineteenth century as filterable agents causing infectious diseases of plants and animals14.To understand the true extent of virus genomic diversity—which may be significantly broader than that of their hosts—and the origins and forces that shape this diversity, virologists will have to systematically rationalize the more distant relationships between viruses, ideally reflecting their ‘macroevolution’, and virus taxonomy should provide an inclusive yet dynamic classification framework to reflect these relationships. In contrast to the taxonomies of cellular organisms, this new virus taxonomic framework will have to accommodate the current view that viruses have multiple origins (polyphyly) and that their diversity cannot be represented by a single virosphere-wide tree15. In its earliest versions, the ICTV classification of viruses into taxa formally recognized only genera and families but, over time, this classification scheme developed into a five-rank hierarchy of species, genus, subfamily (used rarely), family and order17. This five-rank structure matched a section of the Linnaean hierarchical structure used in the taxonomies of cellular organisms and remained in place until 2017  oversees the official classification of viruses and nomenclature of taxa, that is, taxonomy Box 15. In itBox 1 The ICTV15. The ICTV is a committee of the Virology Division of the International Union of Microbiological Societies (IUMS) and is governed by an Executive Committee (EC) that supervises approximately 100 specialized Study Groups. The Study Group members are chosen by each Study Group Chair, who is an ICTV member. The ICTV is responsible for developing the taxonomy, including the official classification of all viruses, viroids and satellites, regardless of their hosts or perceived importance, as well as the nomenclature of approved taxa. This taxonomy is in accordance with the Statutes, which form the normative basis of the organization, and the Code, which formalizes the rules on implementing the taxonomy. The ICTV also maintains several web-based resources that serve the virology community:The ICTV, which was originally named the International Committee on Nomenclature of Viruses, is a voluntary, largely self-regulating and non-profit global organization. Currently, membership includes about 150 virologists representing many nationalities, with the majority of members elected or appointed for a fixed termhttps://ictv.global/taxonomy/). The ICTV database can be accessed using an online taxonomy browser. The database is also used to generate a downloadable spreadsheet of current virus taxonomy, the Master Species List, which is published annually.• ICTV taxonomy database . The online ICTV Report is a developing compendium of all current virus taxa with information describing the properties and characteristics of virus members of each taxon.• Online ICTV Report . The downloadable Virus Metadata Resource includes virus names, virus name abbreviations, isolate designations, GenBank accession numbers and host groups for exemplar viruses of each approved virus species.• Virus Metadata Resource  to the ICTV EC. Any virologist can submit a TaxoProp addressing the creation, revision or dissolution of taxa, or nomenclature changes. Each TaxoProp is reviewed by the EC, considering the recommendations of relevant Study Groups. These Study Groups (often the authors or co-authors of TaxoProps) are tasked with the taxonomic development of specific families or other high-rank taxa as well as the development of demarcation criteria used to define taxa at different ranks. An EC-approved TaxoProp is then considered for ratification by the entire ICTV membership see Box . Irrespe23 and DNA viruses28. These groupings relate to otherwise seemingly disparate viruses belonging to different families and have a variety of different hosts, genome types and organizations, and replication mechanisms. Importantly, these groupings have relied on distant relationships often associated with structure–function hypotheses , which were then validated in subsequent experimental studies31; these provided independent support for the inferred classifications.Until recently, the evolutionary relationships between viruses of different families or orders were considered by the ICTV, and by many in the virology community, as being too distant to be resolved in a credible classification. Thus, there was little impetus to extend the taxonomy rank structure. The result was a taxonomy that, in profound contrast to its cellular counterparts, included many disjointed taxa, the number of which increases with the accelerating discovery of novel viruses (exceeding 100 families in 2018). However, classification efforts continued outside of the official taxonomic framework and, over the last few decades, several informal groupings such as ‘supergroups’ or ‘superfamilies’ were proposed for subsets of RNA viruses33. Because of its conceptual clarity and functional foundation, this classification system is still widely used. It complements virus taxonomy by grouping viruses into meaningful classes at a different scale of virus divergence, albeit without attempting to evaluate their evolutionary relationships.Also, before these developments took place, Baltimore had introduced a non-hierarchical classification of viruses which groups viruses into just seven  classes according to their genome type  and its relation to the synthesis of mRNA34. Figuratively speaking, a taxonomic hierarchy was sought that could accommodate a virosphere-wide tree (or trees) from the roots to the tips of the branches. Because of its potential impact on the practice of virus taxonomy, the EC created a Working Group to consider the matter in more detail. An account of the process undertaken by the Working Group to propose a new taxonomy is outlined in Box In 2016, the urgency, timeliness and logistics of introducing additional ranks to the virus taxonomy hierarchy were discussed at length by the ICTV Executive Committee (EC). The discussion addressed how best to mirror the complete Linnaean taxonomy system , how to allow for the hierarchical clustering of virus taxa in higher ranks such as orders, and whether the Baltimore classes might be adopted as taxa, perhaps at the basal ranks of the taxonomy35. These new principal ranks cover the entire scale of virus divergence to include the deepest virus relationships at the basal rank of realm. The large scale of virus divergence encompassed by the 15 ranks is exemplified by the newly created Riboviria taxon  that currently includes all RNA viruses encoding an RNA-directed RNA polymerase, including viruses of three Baltimore classes 36.The Working Group concluded that an extended, formal virus classification hierarchy should provide 15 ranks, including eight principal (or primary) ranks and seven derivative (or secondary) ranks . These changes provide the virology community with the opportunity to submit taxonomic proposals that fill the new principal and secondary ranks with defined taxa.The new rank hierarchy and its normative basis, in the form of changes to the ICTV Code, were approved by the ICTV EC and subsequently ratified by the ICTV in two votes in 2018 and 2019  under three main premises: (1) virus discovery will continue at an accelerating pace, which will help to establish links between distantly related viruses; (2) virus taxonomy will continue to develop to reflect the evolutionary relationships of viruses; and (3) advances in comparative genomics will provide the necessary tools for defining natural groups of viruses over the full scale of virus divergence. The deliberations also included a ‘thought exercise’ that used the informal hierarchical groupings of diverse RNA virus subsets  to define the number of ranks that may be needed in a fully inclusive virus classification. The group recommended an extended rank structure of virus taxonomy that was subsequently approved by the ICTV. Virologists active in comparative genomics now have the opportunity to test their discoveries and theories on all aspects of virus classification through the ICTV-mediated process (see refs. 50) and its engagement with the wider community of virologists.In 2016, the ICTV EC formed a Working Group to drive the modernization of the rank structure of virus taxonomy. This group produced a Consultation Report and then an official TaxoProp, which became available at the ICTV website for public discussionZaire ebolavirus and Severe acute respiratory syndrome-related coronavirus, respectively. Although both of these viruses infect humans, they differ taxonomically in the ranks that are populated and the demarcation criteria that define the taxa. Only at the basal, realm rank are the two viruses included in the same taxon, Riboviria. Figure When developing taxonomy, virologists are only obliged to assign a (new) virus to taxa at genus and species ranks. Other ranks may be used optionally when the scientific justification is sufficient. The new 15-rank taxonomic structure applies to all viruses, although none have been assigned to all ranks thus far. To illustrate the application of some of the newly established ranks, Fig. A surge of activity to populate the existing and additional ranks of virus taxonomy can now be expected and is to be encouraged. This will include the consideration of numerous supergroups and superfamilies that have remained outside of virus taxonomy to date. At the time of writing (January 2020), the ranks included highly different numbers of taxa Fig. . As expe28. For example, increasing evidence supports an ancestral relationship of some viruses of the order Caudovirales  and viruses of the order Herpesvirales  through a shared virion morphogenesis module. This module includes the HK97-type major capsid protein, portal protein, capsid maturation protease and the genome-packaging terminase complex41, and appears to reflect monophyletic relationships that may warrant taxonomic recognition.These variations could persist, although the currently observed differences in taxon density may be partially alleviated when researchers define the more distant taxonomic relationships among viruses and improve their resolution by involving traditional and new evolutionary methodologies, such as network analysesAs a result of the change to the number and scope of ranks, virus taxonomy is now, for the first time, able to accommodate taxa at any level of virus divergence between the very narrow (species) and the extremely wide . How these ranks are used will depend on the research community, including the ICTV. We stress that the validity of any established taxa, or those created in the future, depends on the strength of scientific evidence to support the demarcation and ranking of taxa, which is considered on a case-to-case basis.The codified availability of a greater number of ranks in a formal virus classification that emulates a Linnaean framework may also facilitate the comparison, and possibly improve the compatibility of virus taxonomy with the taxonomies of cellular organisms. Although the switching of hosts by viruses may be a complicating factor, the availability of fossils and a defined evolutionary timescale for some virus hosts should benefit virus taxonomy. Such information will be essential for taxa demarcation and rank definition in the future, notwithstanding that all taxonomies depend on the accuracy of evolutionary reconstructions, which are most challenging for distant relationships that reflect numerous changes, including those resulting from horizontal  gene transfer.We expect that the described changes to the hierarchical rank structure will create a new impetus for the exploration of virus macroevolution and a framework for its application to taxonomy. The changes will also stimulate research on the defining characteristics of monophyletic virus lineages and the recognition of historical events that played a decisive role in their origins and evolution. These events may be comparable to major transitions in the evolution of cellular life forms, such as the origins of eukaryotes or plants. This information could be used to define taxa and ranks, and the improved interaction with evolutionary research will facilitate the main mission of virus taxonomy, which is to serve the virology community and the public at large in a comprehensive, scientific manner.


Shellfish constitute an important protein source but may be contaminated by viruses from various origins. A study performed on clams collected in Cameroon showed a high prevalence of norovirus and hepatitis A virus. After sequencing, the hepatitis A virus showed similarities with the genotype V simian strains. The need for a one-health approach to protect humans from emerging diseases is evident by the high number of microbial pathogens that become zoonotic following insect bites or consumption of contaminated meat. This is particularly well documented for infectious disease transmission from non-human primates to human  were collected on the Sanaga river , between February 2018 and March 2019 from two sampling points (eight from site B and six from site M). Clams were immediately frozen and shipped to the Ifremer laboratory where they were dissected and the digestive tissues (DT) recovered. Viruses were eluted from clam DT and nucleic acids were extracted using the reference method . The sequence of the VP3-VP1 cleavage site of KZN_Irr-20130530 suggested a simian origin for this strain (Rachida et al. Finding this virus in a food raises several concerns. If the strain originates from simian hosts, the risk of host jump to humans is enhanced by the high frequency of positive samples, the close relation between human and non-human primates, and the high stability of HAV. Indeed, some HAV strains appear highly resistant to heat and cooked coquina clams have been implicated in outbreaks (Pinto et al.


Dengue fever is a mosquito-borne infectious disease threatening more than a hundred tropical countries of the world. The heterogeneity of mosquito bites of human during the spread of dengue virus is an important factor that should be considered while modeling the dynamics of the disease. However, traditional models assumed homogeneous transmission between host and vectors which is inconsistent with reality. Mathematically, we can describe the heterogeneity and uncertainty of the transmission of the disease by introducing fuzzy theory. In the present work, we study transmission dynamics of dengue with the fuzzy SEIR-SEI compartmental model. The transmission rate and recovery rate of the disease are considered as fuzzy numbers. The dynamical behavior of the system is discussed with different amounts of dengue viruses. Also, the fuzzy basic reproduction number for a group of infected individuals with different virus loads is calculated using Sugeno integral. Simulations are made to illustrate the mathematical results graphically. Dengue is one of the major public health concerns. It is a mosquito-borne fastest growing tropical disease in the world. Dengue cases have been increasing from 2.2 million in 2010) to over 3.2 million (in 2015) across the America, Southeast Asia, and Western Pacific. More than one-third of the world's population, approximately 3.9 billion people, are living in the dengue risk area of 128 countries  is a fuzzy measure  be an uncertain variable; i.e., u is a fuzzy subset and μ a fuzzy measure on Ω. Then, fuzzy expected value (FEV) of u is the real number, defined by the Sugeno integral  = supΓ(v) with v′ ≤ v ≤ vmax, where k(0) = 1 and k(1) = Γ(vmax).Thus, v in the population was assumed as a linguistic meaning which is classified into three states: weak virus load, medium virus load, and strong virus load. Each state has a fuzzy behavior based on values vmin, vM, and vmax described in The amount of virus Weak virus load when γ0R0(v)) = 0 < γ0⇔R0f < 1.So, FEV is continuous and a decreasing function with k(0) = 1 and k(1) = 0. Hence, the FEV(γ0R0(v)) is the fixed point of k andThus, if R0f and R0f is higher than the number of secondary infection As a function Strong virus load when As in R0f > 1; the disease will be endemic.Thus, 7, and 1.04 × 1010 RNA per ml [We explore the influence of dengue virus load with the fuzzy behavior on the transmission dynamics of the dengue disease. We have simulated the fuzzy SEIR-SEI model with different values of dengue virus loads, 357, 5 × 10A per ml , 27.v increases, the infection rate increases. It causes the increase in an infectious human population. So, the susceptible human population decreases. Figures 510 RNA per ml), the susceptible human population decreases significantly to its least value , the basic reproduction number decreases slightly, since at that situation, infection of the disease will be very high. After that, it decreases very sharply (Basic reproduction number indicates whether the dengue disease will vanish or persists in the population over time. When the fuzzy transmission rate of the disease increases, the infectious population increases, so the basic reproduction number also increases . The bas sharply ; because sharply .Different phenomena of transmission dynamics of the dengue disease are observed due to fuzziness of the model parameters which are considered functions of virus loads. These phenomena cannot be observed in deterministic models. Thus, the fuzzy model can describe transmission dynamics of dengue in a more realistic than deterministic model of the disease.In this paper, we have studied the SEIR-SEI epidemic model of dengue disease in a crisp and fuzzy system. We have studied the dynamical behavior of the system. We considered the two parameters, transmission rate of disease and recovery rate, from infection as a function of virus loads and defined their fuzzy membership functions. Also, we analyzed the stability of the model at disease-free equilibrium point with different virus loads. We computed the fuzzy basic reproduction number.Dengue disease cannot be spread among the population if the amount of dengue virus is very low due to the natural immunity. It will be endemic if the amount of the virus is high. In the fuzzy system, these phenomena could be considered, but it cannot be considered in the crisp system. In the classical system, the basic reproduction number is the function of system parameters only, whereas in the fuzzy system, the basic reproduction number is a function of disease-spreading virus. Thus, the fuzzy model is more realistic, flexible, and balanced than the crisp model of the dengue disease.The uncertain model parameters, transmission rate and recovery rate, play a crucial role in the disease transmission dynamics. So, we have considered only these parameters as a function of dengue virus loads. We may consider the other uncertain parameters of the model as the function of virus.


Viruses are the most frequent cause of severe acute respiratory infection (SARI) in children. It is currently unknown whether presence of a virus, the number of viruses, or type of virus, are associated with clinical outcomes of pediatric SARI in developing countries.post hoc analyses we evaluated if the PCR results, and demographic and clinical characteristics, were associated with course of disease, duration of respiratory support, and length of stay (LOS).Between 2012 and 2014 nasopharyngeal swabs and demographic and clinical variables were prospectively collected for surveillance of viral causes of SARI in Surinamese children within 48 hours after hospitalization. These swabs were tested for 18 respiratory viruses using a multiplex polymerase chain reaction (PCR) panel to identify the specific viral causes of SARI, unknown to the treating physicians. In Of a total of 316 analyzed children, 290 (92%) had one or more viruses. Rhinovirus/enterovirus (43%) and respiratory syncytial virus (34%) were most prevalent. Course of disease was mild in 234 (74%), moderate in 68 (22%), and severe in 14 (4%) children. Neither presence of a single virus, multiple viruses, or the type of virus, were different between groups. Prematurity and lower weight-for-age-z-score were independent predictors of a severe course of disease, longer duration of respiratory support, and longer LOS.Viruses are common causes of pediatric SARI in Suriname, yet not necessarily associated with clinical outcomes. In developing countries, demographic and clinical variables can help to identify children at-risk for worse outcome, while PCR testing may be reserved to identify specific viruses, such as influenza, in specific patient groups or during outbreaks. Severe acute respiratory infections (SARI) are responsible for an estimated annual 1.4 million deaths among children younger than five years of age worldwide .Polymerase chain reaction (PCR) testing has identified many different viral causes of pediatric SARI –6. HowevWe performed a prospective surveillance study for identification of viral causes of SARI in Surinamese hospitalized children. Then, we were interested whether presence of a virus, the number of viruses, or specific type of virus, were associated with course of disease, duration of respiratory support, and length of stay (LOS). In addition, we evaluated the potential association of demographic and clinical characteristics of Surinamese children with these outcomes.Between April 2012 and March 2014, surveillance was performed to identify viral causes of pediatric SARI at the Department of Pediatrics of the Academic Hospital Paramaribo (AHP), Suriname. All children (under 18 years of age) admitted to the pediatric ward of the AHP with signs of respiratory infection were eligible to be included into the surveillance database. Inclusion into the surveillance database was based on the WHO case definition for SARI, namely: an acute respiratory illness with onset during the previous ten days requiring overnight hospitalization, along with history of fever or measured fever of ≥ 38°C, and with cough, shortness of breath or difficulty breathing . Before Mycoplasma pneumonia, Chlamydophila pneumonia, Legionella pneumophilia, and Bordetella pertussis were also detected with this multiplex PCR kit, but not included into this study on viral causes.Nasopharyngeal swab specimens were collected with Universal Transport Swabs  from admitted pediatric SARI patients. Both swabs were retained in the same vial and stored at 4°C before daily transport to the laboratory. In the laboratory, swabs were stored in a refrigerator (4°C) for a maximum of 72 hours, before PCR analysis was performed. If longer storage was necessary, the swabs were stored at -20°C. Extraction of genetic material from the samples was performed with a QIAamp MinElute Virus Spin Kit , according to the manufacturer’s protocol. DNA or RNA of influenza A virus (subtype H1N1 and remaining subtypes), influenza B virus, RSV (subtype A and B), parainfluenza virus (subtypes 1–4), adenovirus, rhinovirus/enterovirus (RV/ENT), coronavirus , human metapneumovirus, bocavirus, were detected with the Respifinder SMART 22 multiplex PCR kit , performed according to the manufacturer’s instructions. For the pre-amplification reaction, a thermocycler  was used. The hybridization, ligation/amplification and detection were done with a Lightcycler 480 . During the surveillance period a standardized case report form was used to collect demographic and clinical data. Patients were admitted through the emergency department or pediatric outpatient clinic. On admission, demographic variables, clinical diagnosis, history of illness, medical history, and findings of physical examination such as respiratory rate, presence of nasal flaring and chest retractions, pulse rate, body temperature , presence of fever (i.e. defined as temperature ≥38.0°C), transcutaneous hemoglobin oxygen saturation measured with a pulse oximeter with a pediatric sensor, and bodyweight measured with a mechanical scale to the nearest 10 grams, were recorded. Pediatric Early Warning System (PEWS) scores were collected using appropriate vital signs pulse rate and respiratory rate for age . The datPrior to analyses, we excluded cases from the surveillance database that did not meet the WHO SARI criteria, cases in which SARI diagnosis was rejected during admission, and cases in which comorbidity could directly influence the course of disease during the admission, namely in case of congenital heart disease, known immunodeficiency, neuromuscular disorder compromising respiratory function, and congenital respiratory tract malformations. Our first aim was to perform surveillance to identify viral causes of SARI. Our second aim was to evaluate association of the presence of either a single virus or multiple viruses, or the specific type of viruses, with the primary outcome course of disease, and the secondary outcomes duration of respiratory support and LOS in days. Course of disease was based on the maximum respiratory support received and defined as mild , moderate , or as severe . In addition, we evaluated the association of demographic and clinical variables with these outcomes.Before statistical analysis, weight was corrected for age using weight-for-age z-scores according to the WHO Growth Standard , 15. FreThis study was reviewed and approved by the Surinamese Commission for Human Research (VG014_16). The requirement to obtain informed consent was waived because nasopharyngeal swab sampling was performed for surveillance purposes and considered a noninvasive intervention for children who were hospitalized due to respiratory infection. Collected data were encoded for the researchers.Nasopharyngeal swab were collected from 416 patients, of whom 100 were excluded . Of 316 A total of 409 viral causes were detected in n = 290 (92%) patient samples that tested positive for at least one virus . In 85 . There wAge, prematurity, weight-for-age-z-score, duration of respiratory support, and LOS were significantly different between groups . NeitherIn this study, we describe viral causes of SARI and the association of results of PCR testing for these viruses with course of disease and LOS in a large cohort of Surinamese children. Although there was a high prevalence of viral pathogens, we found no association between presence, number, and specific type of the detected viruses and the course of disease, duration of respiratory support, and LOS. However, demographic variables prematurity and lower weight-for-age-z-score were associated with a severe course of disease, and longer duration of respiratory support and LOS.The overall prevalence of pathogens (92%), and the proportion of viral co-infections 32%) in our study are comparable with those reported in the literature 2% in our, 16, 17.Though the burden of RSV disease is mostly in low- and middle-income countries, the understanding of temporal dynamics of RSV disease in these countries has lagged behind , 24. In Bacterial pneumonia, which has a high mortality, was not tested systematically (i.e. with sputum culture or chest X-rays), so that children with bacterial pneumonia could not be excluded from the study. Assuming these children were also in the database, the overall mortality in our study seems low (i.e. 0.3%) considering that 9% of all mortality of children under five years of age in Suriname is explained by SARI according to the WHO . This diBordetella pertussis, as treatment is available by antibiotics) in specific patients groups  with high risk for mortality due to influenza) and for the identification and monitoring of outbreaks of specific causes . As an example of the latter, shortly after the study period, young infants presented with clear symptoms of Bordetella pertussis. Multiplex PCR then confirmed Bordetella pertussis in eleven other infants with SARI. In contrast, less than one case of Bordetella pertussis had been reported in Suriname each year between 2003 and 2012 [The clinical utility of PCR testing for viral causes of SARI remains under debate. Our data suggest that the clinical course of SARI may not only be dependent on the presence, number, and type of viruses, but also on host and environmental factors. These data are consistent with those from earlier reports from developed countries –33. Overand 2012 . The DepThe strengths of this study include the prospective collection of data and the fact that PCR results were not available for clinicians, thereby limiting the chance of bias. However, this study is not without limitations. First, a positive PCR result does not differentiate between viable pathogenic viruses or nonviable viral DNA or RNA material after shedding. Second, we did not systematically test for bacterial infection, since this was not ethical due to the invasive nature of the collection of sputum and blood sampling in small children. Last, RSV might exhibit cyclic epidemics over the time span of multiple years. A study period of two consecutive years may not be sufficient to observe these cyclic patterns.In conclusion, this study contributes to our understanding of the prevalence of viral pathogens associated with children admitted with SARI in Suriname. Our findings underline the importance of viruses as SARI-associated pathogens in this group. We propose that in developing countries, demographic and clinical variables are used to identify children at-risk for severe course of disease. PCR testing can be useful to confirm specific viral causes of SARI in specific patients and during outbreaks.S1 Fig(TIF)Click here for additional data file.


Binderless pure silica zeolites (zeosils) spheres and hollow spheres with a diameter of 20 µm composed of silicalite-1 nanosheets particles were prepared by pseudomorphic transformation of spherical silica beads using different temperatures  and treatment times (1–5 days) in order to adapt the local dissolution rate of silica to the crystallization rate of silicalite-1 nanosheets allowing to preserve the initial morphology of the silica beads. Fully crystalline beads of 20 µm were obtained at 110 °C for 5 days, whereas hollow spheres similar in size were synthesized at higher temperatures. The crystallization process seems to begin at the outer surface of the amorphous silica beads and spreads with the time in the interior of the beads leading to a dissolution of the inner amorphous part of the beads to create zeosil hollow spheres for the highest treatment temperatures (130 and 150 °C). The dissolution rate of the inner amorphous part of the beads increases by increasing the hydrothermal treatment temperature from 130 to 150 °C. The silicalite-1 beads synthesized at 110 °C for 5 days showed to be promising for rapid molecular decontamination by adsorbing n-hexane in larger amount than the silicalite-1 conventional big crystals in powder forms. Pure silica zeolites known under the name of zeosils are microporous materials with enhanced mechanical and thermal properties compared to aluminosilicate zeolites ,2,3,4,5.However, conventional syntheses of zeolites give often rise to powders composed of micron-size crystals. Unfortunately, those powders cannot be used directly for industrial applications , especiaZeolite microspheres with hierarchical porosities are emerging as attractive materials for applications in adsorption and catalysis ,16,17,18co-divinylbenzene porous particles as template. Yin et al. Br2) named (C22-6-6) was obtained in two steps following the procedure reported by Choi et al. [22) and two quaternary ammonium groups spaced by a C6 alkyl linkage.The di-quaternary ammonium-type surfactant used for the transformation of amorphous silica spheres into MFI-type nanosheets spheres, . 0.4 g of Sodium hydroxide  is dissolved in 12 mL deionized water in a beaker. Then 1.35 g of C22-6-6 and 0.31 g of sulfuric acid  were then added under stirring. After homogenization, the solution is transferred in a 45 mL Teflon®-lined stainless steel autoclave containing 1 g of amorphous silica spheres with 20 µm size to set the molar composition of the gel to: 1 SiO2: 0.3 Na2O: 0.18 H2SO4: 0.1 C22-6-6: 40 H2O. The autoclave was placed in an oven for various temperature and heating time in static mode.Pseudomorphic transformation were performed using as silica source, amorphous porous silica spheres Silica22-6-6 surfactant was removed by calcination in a muffle furnace at 550 °C at a rate of 1 °C/min for 8 h. The resulting zeolite beads will be named (B-T(°C)-t(days)), where “T” stands for the hydrothermal treatment temperature and “t” stands for the duration of the hydrothermal treatment in days.After synthesis the product was recovered by filtration, washed with water and dried overnight at 80 °C. The C1 radiation (λ = 0.15406 nm) in the range 3 < 2θ < 40 °.The purity and the crystallinity of the calcined silicalite-1 microspheres were checked by XRD analysis. X-ray diffraction patterns of the different materials introduced in a glass capillaries were recorded using a STOE STADI-P diffractometer  operating with Cu KαThe size and the morphology of the calcined zeosil spheres were determined by scanning electron microscopy (SEM) (an average was obtained by measuring 100 particles for each sample) using a Philips XL 30 FEG (Field Emission Gun) microscope . The size of the silicalite-1 nanosheet crystals composing the spheres (an average was obtained by measuring the thickness of 100 nanosheets for each sample) was determined using a transmission electron microscopy (TEM) JEOL  model ARM-200F, under an acceleration voltage of 200 kV, with a point-to-point resolution of 80 pm. In order to detect the hollow presence or not in the inner part and the homogeneity of the beads with SEM, samples were embedded in cold mounting epoxy type resin . Embedded beads were then grinded with various SiC grinding papers and finally polished with diamond polishing suspension (particle size 1 µm) until obtaining a soft surface.Nitrogen adsorption/desorption isotherms at −196 °C were measured using a Micromeritics ASAP 2420 apparatus . Prior to the adsorption measurements, the calcined samples were outgassed for 1 h at 90 °C and then at 300 °C overnight under vacuum. The microporous volumes were determined from t-plot method by applying when needed a correction according to Galarneau et al. . Mesopor0 is the saturation vapor pressure of n-hexane at 25 °C (p° = 202 hPa) using a thermogravimetric TG92 instrument  [2 flow. The gas flow rate was stable (114 mL/min). The experiment begun with an activation phase: the zeolites were heated up to 350 °C under dried nitrogen for 2 h, at atmospheric pressure to remove all adsorbate traces. Then, the sample was cooled to 25 °C, and the organic compound was introduced to the system. The adsorbed amount was then measured every 20 s.Dynamic adsorption measurements were performed under VOC atmosphere (n-hexane) at 25 °C and controlled value of relative pressure p/p° = 1 (p is the vapor pressure and p France) . The expThis work highlights the pseudomorphic transformation of silica amorphous beads (20 µm) into zeolite beads or hollow spheres of the same size constituted of an agglomeration of particles (0.5–3 μm) composed of pure silica MFI-type nanosheets, by using di-quaternary ammonium-type surfactant. This strategy seems transposable to several other pure silica zeolite structures. It provides a simple and efficient new synthesis way for obtaining zeolitic beads and hollow spheres of controlled size. The crystallization seems to begin at the outer surface of the beads allowing at higher treatment temperature the acceleration of the dissolution kinetic of the inner amorphous part of the beads to form hollow spheres. Both plain beads and hollow spheres of silicalite-1 showed to be promising for molecular decontamination in continuous flow by adsorbing n-hexane in high amount.This strategy of pseudomorphic transformation of silica amorphous beads into zeolite beads or hollow spheres seems transposable to several other zeolite structures. It provides a simple and efficient one shot synthesis way for obtaining zeolitic beads or hollow spheres of controlled size without the use of binders for industrial applications.


We identified high-quality cps loci in 22 genera and kps in 11 genera, around 4% of which were detected in multiple species. We found SP loci to be highly dynamic genetic entities: their evolution was driven by high rates of horizontal gene transfer (HGT), both of whole loci and component genes, and relaxed purifying selection, yielding large repertoires of SP diversity. In spite of that, we found the presence of (near-)identical locus structures in distant taxonomic backgrounds that could not be explained by recent exchange, pointing to long-term selective preservation of locus structures in some populations. Our results reveal differences in evolutionary dynamics driving SP diversity within different bacterial species, with lineages of Escherichia coli, Enterobacter hormaechei and Klebsiella aerogenes most likely to share SP loci via recent exchange; and lineages of Salmonella enterica, Cronobacter sakazakii and Serratia marcescens most likely to share SP loci via other mechanisms such as long-term preservation. Overall, the evolution of SP loci in Enterobacteriales is driven by a range of evolutionary forces and their dynamics and relative importance varies between different species.Bacterial capsules and lipopolysaccharides are diverse surface polysaccharides (SPs) that serve as the frontline for interactions with the outside world. While SPs can evolve rapidly, their diversity and evolutionary dynamics across different taxonomic scales has not been investigated in detail. Here, we focused on the bacterial order Enterobacteriales , to carry out comparative genomics of two SP locus synthesis regions,  Polysaccharide capsules and lipopolysaccharides (LPS) with an O antigen, here broadly called surface polysaccharides (SPs), are the most diverse bacterial cell surface structures. They play a number of important biological roles pertaining to bacterial survival, including prevention from desiccation , 2, aidiThe potential for structural hyper-diversity of SPs stems from a heterogeneous ability of forming chemical linkage between various sugars into polysaccharide chains. This property was revealed in early work on bacterial carbohydrates , and undNevertheless, our understanding of SP evolution remains far from complete. SP genetics has predominantly been studied in a small number of medically relevant bacterial species, with little attention paid to comparative evolutionary dynamics or SP sharing between species. For example, it remains unclear how SP biosynthesis loci have been evolving in the context of different bacterial population backgrounds (whether defined by ecology or phylogeny), whether the long-term impact of horizontal gene transfer (HGT) on SP loci is the same in different taxonomic groups, and how selection on SP loci shapes bacterial population structure. These questions are important to answer since capsules and LPS directly interact with the immune systems of humans and other mammalian hosts, and are targets of current and future medical interventions, including glycoconjugate vaccines  and antiEscherichia, Salmonella, Klebsiella, Enterobacter) and the related families Erwinaceae (including Erwinia), Yersinaceae (including Yersinia and Serratia) and others which were recently removed from the Enterobacteriaceae family definition [cps and kps, which are involved in the biosynthesis of both capsules and O-antigens in Escherichia coli [cps and kps). We explore evolutionary dynamics and horizontal transfer within and between species and genera of Enterobacteriales, yielding the largest to date systematic analysis of the SP locus genetics in any bacterial family or order.Here we present an analysis of SP locus diversity and evolution in a bacterial order of medical importance—Enterobacteriales, which includes the well-known Enterobacteriaceae family , and a high-quality kps locus in 2352 genomes from 11 genera; the remaining genomes either contained poorly assembled locus sequences or were missing altogether  of genomes with kps also carried cps. The number of unique SP loci  detected per genus was strongly predicted by sample size , but not the nucleotide diversity captured by that sample , suggesting that the evolutionary history of SP loci involves horizontal transfer across genus boundaries . We then defined a locus similarity network for a given Jaccard distance threshold, J, where nodes are representative loci and edges link all loci whose gene content similarity is equal or greater than S = 1 − J. For the networks shown in Fig. cps and the kps network), which did not disappear even for S = 1 , Tatumella (Erwinaceae) and Rahnella (Yersinaceae) shared very similar cps gene complements; Erwinia (Erwinaceae) shared very similar kps gene complements with members of the Enterobacteriaceae family; see Fig. S between sets of LGFs detected in those genera: borderline significantly positive correlation for the cps region  and insignificant correlation for the kps region ; see also Supplementary Fig. cps region  and no significant correlation for the kps region .We observed a large heterogeneity in LGF sharing between genera: some genus pairs shared large numbers of SP genes while others shared none see Fig. . These LL0.004 lineages as a function of genome distance.  and unique locus structure, L0.004 lineages were defined as complete-linkage clusters distinct at the 0.4% genome distance threshold, and genome distance was calculated based on estimated ANI values; see “Methods”.) Results are shown in Fig. cps and kps , followed by a long tail expected from Fig. L0.004 lineages that shared a locus type, and compared genome vs. SP locus genetic distance between each pair , for many pairs the SP locus genetic distance was smaller than the whole-genome genetic distance, as would be expected from HGT-driven locus exchanges . We reasoned that, if the SP locus has been exchanged (directly or indirectly) between two diverged lineages via recombination, the SP locus genes should rank amongst the most genetically similar CDS for that pair of lineages  are explicitly shown in Supplementary Fig. To better understand the dynamics of locus-type sharing between divergent strains, we calculated a probability of sharing the same locus type between representative isolates from different air Fig. . While wges Fig. . To examCronobacter-driven between-species sharing of the kps locus; see Fig. Figure y = x). The slopes of the regression lines for these points were significantly greater than 1 . The SP locus genes also had more non-synonymous substitutions than other CDS in the Enterobacteriales genomes , suggesting comparatively relaxed purifying selection at the SP loci. These observations could reflect an overall accelerated evolutionary rate in SP locus genes, or the acquisition of highly divergent gene copies by HGT. To address these biases, we calculated the proportions of non-synonymous substitutions on the branches of each gene tree , and binned these values by branch lengths across all HG trees , the two SP locus curves were similar within each species . These measures were not significantly correlated with the sample size per species , but confirmed significant differences in SP locus-sharing patterns between different species, with probability values ranging from 0.47%  in E. coli to 56%  in K. aerogenes using the L0.004 lineage definition.Next we examined patterns of SP locus-type sharing between lineages of the same species. Figure ies Fig. . The plaies Fig. , using aL0.004 or L0.01 lineage pairs in which locus-type sharing was attributable to one of the three categories in Fig. K. aerogenes, Enterobacter hormaechei and E. kobei and E. coli, recent locus exchange was the predominant mechanism of locus-type sharing between different bacterial lineages. By contrast, in Salmonellaenterica, Serratia marcescens and Cronobacter sakazakii, locus-type sharing could be rarely explained by recent locus exchange, hence pointing to vertical inheritance or independent acquisition as potential mechanisms. Finally, in K. pneumoniae both recent exchange and lack thereof contributed equally to locus-type sharing. Species with the more diverse SP locus were more likely to share locus-type via recent exchange, however this correlation was not statistically significant  loci are maintained in S. enterica lineages to the point that lineage typing has been proposed as a substitute for serotyping [K. pneumoniae, the cps K locus is a hotspot for recombination in many lineages, but in others is conserved for long evolutionary periods [To assess the contribution of recent exchange to locus-type-sharing between lineages, we considered only those species in which locus-type sharing was observed for at least 100 lineage pairs, and calculated the proportion of lineages , 54, wherotyping . In K. p periods . Our rescps and kps loci follow the typical structure of SP loci, comprising a core complement of genes required for SP expression, regulation and export (at the ends of the locus) and a variable complement of genes coding for assembly of oligo- and polysaccharides (in the middle of the locus). In addition to relocation of whole SP loci into new chromosomal backgrounds via HGT . The results reveal many instances of gain or loss of small numbers of genes (1–3) within individual bacterial lineages , althoucps (kps) LGFs were found in at least two species and 40% (24%) were found in at least two genera. We also found identical cps and kps LGF sequences in multiple species, indicating recent horizontal transfer of SP locus genes between diverse taxa. We hypothesised that different LGFs frequently move between different genetic backgrounds. To assess this, we quantified such moves as rates of jump (jump-rates) between species for individual LGFs using ancestral state reconstruction to model bacterial species as a discrete trait on each individual gene tree (for LGFs with >100 sequences), and measured the frequency of state transitions (i.e. species changes) per branch length (see “Methods”). The distribution of species jump-rates for the most diverse LGFs  locus region was detected in 22 (11) genera belonging to 5 (3) bacterial families. Importantly, we show that at least one third of all SP LGFs were detected in multiple species, and that most common gene families showed evidence of frequent jumps between species and locus families. This promiscuous distribution of SP LGFs is consistent with our observation of many cases of horizontal transfer of whole SP loci between bacterial lineages, species and genera. In addition, we found frequent alterations of the LGF content of the SP loci within closely related bacterial lineages, often through gain/loss of a small number of genes. Altogether, our study on the global SP diversity in Enterobacteriales provides robust evidence for SP loci being a major evolutionary hotspot, complementing evidence from previous separate reports on various members of this and other bacterial groups [To our knowledge we present the first systematic analysis of SP locus evolution across Enterobacteriales, or indeed any bacterial order. Previous studies focusing on individual SP loci, or the distribution of SP loci within individual species groups, have occasionally reported instances of individual loci transferring across species or genus bounds. However this study provides a unique view of evolutionary dynamics of SP loci across multiple scales. By examining the largest to date collection of SP loci in the Enterobacteriales, we showed that our two query locus regions are widespread across the order: l groups , 57, 58.S. enterica [S. enterica was a species in which we detected one of the strongest signals of cps locus preservation , it is still conceivable that the dataset includes assemblies that result from mixed or contaminated genome data. Hence we have taken a ‘safety in numbers’ approach, whereby all conclusions drawn from the obtained data are made based on statistical trends, not individual observations. Second, the genome set that we sourced from GenBank includes a mixture of single isolates and collections, sequenced for a variety of reasons and using various different sampling strategies. To mitigate the effect of sampling bias on our estimated parameters, we attempted to minimise it by down-sampling highly similar groups of isolates to single representatives, and estimated parameter uncertainty through resampling isolates with replacement (see “Methods”). Finally, using e strain ). Hence,Altogether, our study paints a picture of the evolutionary process shaping SP diversity in Enterobacteriales. It provides support for the idea that SP biosynthesis loci are diversity-generating machines, optimised to produce novel phenotypes while minimising the fitness cost of producing suboptimal combinations . DiversiArsenophonus, Biostraticola, Brenneria, Buchnera, Budvicia, Buttiauxella, Calymmatobacterium, Cedecea, Citrobacter, Cosenzaea, Cronobacter, Dickeya, Edwardsiella, Enterobacter, Erwinia, Escherichia, Ewingella, Gibbsiella, Hafnia, Klebsiella, Kluyvera, Leclercia, Leminorella, Levinea, Lonsdalea, Mangrovibacter, Moellerella, Morganella, Obesumbacterium, Pantoea, Pectobacterium, Phaseolibacter, Photorhabdus, Plesiomonas, Pragia, Proteus, Providencia, Rahnella, Raoultella, Saccharobacter, Salmonella, Samsonia, Serratia, Shigella, Shimwellia, Sodalis, Tatumella, Thorsellia, Trabulsiella, Wigglesworthia, Xenorhabdus, Yersinia and Yokenella. Note that the definition of Enterobacteriaceae has now been updated to include a subset of these genera, with the rest assigned to new families within the order Enterobacteriales [5), following by neighbour-joining tree construction using rapidNJ [Arsenophonus and Sodalis as these genera were rare and did not form monophyletic clades (n = 6); (ii) isolates with a temporary genus name Candidatus that could not be curated using BacSort (n = 6); (iii) isolates which could not be assigned to any of the 53 genera. The resulting 27,383 isolates were classified into 45 genera, and assigned into 39 monophyletic genus-groups with the following joint groups: Buchnera/Wigglesworthia, Erwinia/Pantoea, Escherichia/Shigella, Klebsiella/Raoultella, Proteus/Cosenzaea and Serratia/Gibbsiella. For some isolates, especially those descending from rare species in the dataset, species name-reconciliation was problematic. Hence, new species categories  were defined based on the structure of the distance tree. Monophyletic species groups retained the original species names , while polyphyletic groups within a genus were split into monophyletic clades with a new unique name  were assigned to the category ‘Other’.The main dataset consisted of 27,476 genomes from NCBI RefSeq (April 2018) belonging to the 53 genera defined as Enterobacteriaceae by the UK Standards for Microbiology Investigations : Arsenopteriales ; hence oteriales  with ‘-fteriales  with ske rapidNJ . We remoN50 < 10,000 and Ncontigs > 800. All isolates belonging to the remaining, small clusters were pooled together and their GC content and total genome length plot revealed two subgroups of isolates . To classify an isolate into the non-animal class, the “isolation source” category had to contain an “env” string. Using these criteria, we found 12,383 isolates of human origin, 4269 isolates of animal origin, 499 isolates of non-animal origin; 8551 did not fulfil the classification criteria and 1632 isolates were missing environmental data altogether. The list of all isolated with the corresponding environmental classes can be found in the Supplementary Table Many NCBI RefSeq isolates come from the NCBI Pathogen Detection database, which stores environmental metadata of the bacterial isolates submitted by the users. These include “isolation type” , “host” (host species) and “isolation source” . We used these parameters to classify isolates into three environmental classes: human, animal or non-animal, otherwise classifying them as “unknown” (information missing) or “other” (unfulfilled classification criteria). To classify an isolate into the human class, the “host” category had to match cps locus was defined by the presence of flanking genes galF and gnd and/or ugd, and is associated with the synthesis of group 1 and 4 capsules [cps locus in all species , Nm (number of best match genes missing in the detected locus), Ne (number of extra genes present in the detected locus), and Nc (number of contigs on which locus was found). The best match was defined as a good match if any of the following conditions was met:The capsules  (see exa complex ), the gaherichia , 72 and Shigella   or other sugar-synthesis genes ; (ii) if there was a single contig with only core genes; (iii) if no core genes were found. In the first round, all new loci present in a single contig were extracted, and loci without gene duplications or stop codons and with all core genes present were added to the reference database, altogether comprising 994 reference sequences. In the second round, all loci which had a good best match, as defined above, or new loci in a single contig (Nc = 1) were extracted. Loci had transposons removed using ISFinder , and frameshift mutations corrected using Decipher R package [cps locus, and 18,401 had the locus, which was then extracted.If the condition was not met but g Prokka  with a c package . Accordikps locus was defined by the presence of flanking genes kpsF, kpsE, kpsD, kpsU, kpsC, kpsS, kpsT and kpsM (group 2 capsules) and kpsD, kpsM, kpsT, kpsE, kpsC and kpsS (group 3 capsules), as reviewed in [E. coli [cps locus, except that the locus was defined missing in the case of absence of any of the three genes kpsM, kpsT, kpsC, and the extended reference database had 106 sequences. With those definitions, 24,759 isolates were missing the kps locus, and 2356 had the locus extracted.The iewed in   was calculated between the SP loci in both isolates asU is the number of LGFs in a union of all LGFs in both loci and I is the number of LGFs in an intersect of all LGFs in both loci. Locus types were defined by connected components of a locus similarity network clustered at J ≤ 0.1. A representative set of loci for all Enterobacteriales was chosen as single representatives of all loci within each species. This led to a set of 2654 cps loci and 332 kps loci .e-value threshold of 10−10 in each genome assembly, with the top hit for each locus recorded. Each isolate was then assigned a combination of 53 numbers, corresponding to an allele index at each ribosomal gene. Isolates with more than five missing alleles were regarded as with an unknown rST. Finally, isolates with identical allele combinations (disregarding the missing loci) were clustered into rSTs. The detected rSTs were used to define representative datasets by: (i) identifying unique combinations of rST and locus type Ci , and (ii) for each unique rST/locus-type combination choosing the isolate with the greatest n50 value.We identified rST for each isolate in reference to a standard typing scheme , using aC1 definition). A simple pairwise genome search yielded over six million pairs sharing the locus type in the cps region. As the large majority of these pairs were isolates belonging to the same clonal family (and thus most likely sharing the locus through common ancestry), we filtered this dataset in two steps. First, we only considered genome pairs belonging to different lineages (under the L0.004 definition). Second, we only considered a single representative of a combination of rST and a C1 locus type (as defined above). This resulted in 7000 isolates forming 99,177 pairs, and in 1114 isolates forming 23,928 pairs for the cps and kps loci, respectively. For each pair, we then downloaded predicted coding regions in the relevant assembly from NCBI RefSeq [cps or kps regions. Next, we used mmseqs2 with 10−50e-value threshold to: (a) align SP locus protein sequences in one genome against those in the other genome, (b) all other protein sequences in one genome against those in the other genome. If a protein in one genome gave a hit to multiple proteins in the other genome, only the most similar sequence was retained. Then, we created a distribution of the genome protein sequence comparison by (a) randomly sampling np protein sequences (where np is the number of homology groups common to both loci), (b) calculating the mean percentage similarity between them, and (c) repeating this process 10,000 times. The locus type was considered recently exchanged via recombination if the locus proteins were amongst the top 5% of the most similar sets of proteins . This resulted in a total of 36,019,427 proteins clustered into 28,441 HGs. Gene sequences assigned to each HG were then aligned at the protein level with mafft [cps and kps proteins with mmseqs align with an e-value threshold of 10−32. HGs in which 90% of proteins had hits to one of the protein categories in the reference genome (‘SP locus’ or ‘other’) were assigned to that category, otherwise ignored. Considering only clusters with 100 sequences or more as informative, we obtained 26,231 assigned to the ‘other’ category and 261 HGs were assigned to the ‘SP locus’ category; 73 HGs were disregarded.Among all representative isolates (see above), 7403 isolates had a th mafft  with defth mafft . Maximumth mafft  with ‘–gth mafft  with the4 bootstrap sample and the median and 95% confidence intervals of the obtained distribution were reported. The locus-wide species jump rates were calculated in the same way based on concatenated alignments of all LGF present in all isolates for a given SP locus. In that calculation, isolates with less than three low-to-middle frequency genes (20% cut-off) were discarded as in these cases locus-family assignment may have been problematic leading to false-positive locus-family jumps.All nucleotide sequences assigned to a given LGF were aligned with mafft with default settings. Next, a maximum likelihood tree was build using FastTree with ‘–gtr –gamma’ options and midpoint rooted. We then used the asr_max_parsimony function from R package castor to infer bacterial species as ancestral states at internal nodes of the tree , and concps and kps locus, a subset of isolates where the locus was identified in a single contig. This was done to avoid mistaking gene absence due to inability to assemble the full locus with gene absence due to actual gene gain or loss. We then considered a dataset of single representatives of the rST and C0 locus types. Then, for each lineage defined at the L0.004 level, we generated a neighbour-joining tree based on pairwise whole-genome distances using rapidNJ [i and j, was calculated as a co-occurrence of gain or loss events between i and j, namely as a proportion of a sum of all branch lengths in which i and j were found to be co-gained or co-lost to the sum of all branches in which i or j were found to be gained or lost.To quantify SP gene flow within bacterial populations, we first identified, for both  rapidNJ  as well  rapidNJ  to inferCVij between LGFs i and j was estimated as i present and those with LGF j present, and Rij is a correlation coefficient reported by the Mantel test using matrices of pairwise distances between sequences from co-occurring LGFs. Mantel test calculation was performed using ecodist R package [Co-evolution coefficient  package  using Ki10.6084/m9.figshare.816684510.6084/m9.figshare.816679410.6084/m9.figshare.816681510.6084/m9.figshare.1127900010.6084/m9.figshare.11322116The data generated in this study  can be found under the following links:cps and kps regions in Enterobacteriales genomes, as well as all locus reference sequences used in this study, are available at: https://github.com/rmostowy/fastKaptive.The software used to identify SP locus sequences in Supporting InformationSupplementary Tables S1, S2, S3 and S5