We also showed that both alignment-based and alignment-free methods can accurately predict the hosts of viruses. When alignment is difficult to achieve or highly time-consuming, alignment-free methods can be a promising substitute to predict the hosts of new viruses.Predicting the hosts of newly discovered viruses is important for pandemic surveillance of infectious diseases. We investigated the use of alignment-based and alignment-free methods and support vector machine using mononucleotide frequency and dinucleotide bias to predict the hosts of viruses, and applied these approaches to three datasets: rabies virus, coronavirus, and influenza A virus. For coronavirus, we used the spike gene sequences, while for rabies and influenza A viruses, we used the more conserved nucleoprotein gene sequences. We compared the three methods under different scenarios and showed that their performances are highly correlated with the variability of sequences and sample size. For conserved genes like the nucleoprotein gene, longer  EIDs continue to pose significant public health problems as shown by the recent outbreaks of West Nile virus, SARS, MARS, and H1N12. Rapidly identifying the reservoir of the new pathogenic bacterial or viral origins responsible for these diseases will help the containment, control, and prevention of the outbreaks4. Further, investigating the potential host of a virus can throw light on the evolutionary history of the virus, thus provide guidance on how to cut off the transmission path. The biological presumption for most of the host identification methods is that the more similar two viruses’ DNA/RNA sequences are, they are more likely to share the same host5.Viruses are ubiquitous and can reproduce and evolve very fast. Virus infections in human can cause various diseases and are a big threat to human health. Many infectious disease studies showed that virus cross-species transmissions are highly prevalent resulting in emerging infectious diseases (EIDs)6, BLAST7, or other alignment tools.With the availability of various databases containing different types of pathogenic microbial species, one of the most commonly used approaches for identifying the origin of the new pathogen responsible for an EID is to find similar sequences in the pathogen databases using alignment by the Smith-Waterman algorithmet al.8 used relative dinucleotide frequencies and discriminant analysis to infer the hosts of novel picorna-like viruses. Aguas and Ferguson9 developed a feature selection method and used random forests (RF) based on the diverged nucleotide or amino acid bases among a set of aligned molecular sequences to predict the host species of pathogens. Tang et al.10 developed a support vector machine (SVM) based method using mono- and dinucleotide frequencies as features to detect the original hosts of coronaviruses with high accuracy. Kargarfard et al.11 predicted the host range of the influenza virus using various machine learning approaches. Several new alignment-free statistics including k-mers  were developed recently13. It was shown that such measures are highly associated with the evolutionary distances estimated from alignment-based methods, thus validating the usefulness of alignment-free methods for the comparison of molecular sequences15. In this study, we investigate the effectiveness of alignment, alignment-free and machine learning based methods for inferring the hosts of viruses responsible for emerging infectious diseases.Recently, several alignment-free methods have been developed for the identification of the hosts of pathogenic species. Kapoor K-nearest neighbors (KNN) algorithm based on the alignment method and the alignment-free distance/dissimilarity measures for k-mer length from 3 to 6 and the number of neighbors K from 1 to 10. The results for the rabies virus, coronavirus, and influenza A virus datasets are given as Figs k-mers from 3 to 6.We initially calculated the prediction accuracies of the k-mer size to be 6. Based on the sample size and distribution for each dataset, we choose K = 1 as the number of neighbors in KNN for the rabies virus dataset, and K = 7 for the coronavirus dataset and influenza A virus dataset. For alignment-free distance measures, we use Manhattan distance as an representative as many of them have similar prediction accuracies.For clarity of presentation in the remaining of the paper, we let the Figure Figure We also use 10-fold and 20-fold cross-validation to estimate the prediction accuracy and the results are given in Fig. k-mer length of 6, respectively. Figure Similar to the rabies virus dataset, we first visually check the pairwise distance matrix using MDS and hierarchical clustering. Figure Parallel to the investigations for the rabies virus and the coronavirus, Fig. In this paper, we investigate the use of alignment-based and alignment-free distance methods and support vector machine to predict the host of viruses based on three virus datasets: rabies virus, coronavirus and influenza A virus. None of the three methods consistently outperforms other methods. For the rabies virus dataset, the alignment based and alignment-free methods perform similarly and both outperform SVM with a large margin. For the coronavirus dataset, SVM outperforms alignment based method followed by alignment-free method when the sample size is low. When the number of samples is large, eg. over 400, all the three methods perform similarly. Finally, for the influenza A virus, none of the methods performs well with prediction accuracies below 0.6. The alignment-free method does a little bit better than the alignment based method and both outperform SVM. Thus, this study shows both alignment-based and alignment-free methods can be effectively used to predict the hosts of viruses.K from 1 to 5 and become stable for K from 5 to 10. Therefore, we choose K = 7 for the coronaviruses and influenza A viruses datasets, and choose K = 1 for the rabies viruses dataset, since the sample size of the rabies dataset is small and the viruses are unevenly distributed in different host species, while the sample sizes of the other two datasets are much larger.Figures 16, while nucleoprotein gene is much more conserved, especially at the non-synonymous sites18. For conserved genes like nucleoprotein genes, longer k-mers than mono- and dinucleotides are needed to distinguish the sequences. Therefore, SVM using only mono- and dinucleotide does not perform as well as alignment based or Manhattan distance using 6-mers for the rabies and influenza A viruses. On the other hand, for highly divergent genes such as the spike genes, mono- and dinucleotide frequencies are enough to capture the differences among the sequences resulting in better performance of the SVM method.For both the rabies and the influenza A viruses, we use the nucleoprotein gene sequences, while for the coronavirus we use the spike gene. It was shown before that the spike gene evolves fast to better prevent the virus from detection by the hostOur study has several limitations. First, since viruses can jump from one host to another, a virus can belong to multiple hosts. We use the host which the virus is discovered from as its only host. However, the virus may also use other unknown species different from the one it was discovered from as hosts. This will influence the prediction accuracy for both alignment and alignment-free methods. Second, we only investigate three types of viruses and there are many other types of viruses available. More studies are needed to see the general applicability of our prediction methods.In conclusion, our study shows that both alignment based and alignment-free methods can be successfully used to predict the hosts of viruses. Therefore, when alignment is difficult or too time-consuming, alignment-free methods provide a promising alternative to predict the hosts of new viruses.We analyze three virus datasets with different characteristics: rabies, coronavirus, and influenza A virus, to see if consistent results related to the relative performance of alignment, alignment-free, and machine learning based approaches can be obtained.et al.5. The rabies virus is a single-stranded RNA virus and has a wide host range. We first investigate the rabies virus dataset from Streicker et al.5 consisting of 372 rabies virus samples from 23 bat host species. Among them, 148 viruses have complete N gene  sequenced. In this paper, we concentrate on the study of these 148 viruses. The accession numbers of the complete genomes and N gene of the viruses were provided in Streicker et al.5 and the corresponding gene sequences can be downloaded from NCBI genbank database using their accession numbers at https://www.ncbi.nlm.nih.gov/genbank/.The rabies virus dataset from Streicker et al.10. Tang et al.10 developed a SVM based method using mono- and di-nucleotide sequences to predict the host of coronavirus. We use the same data as in Tang et al.10 consisting of 724 coronavirus samples from 6 host species . Among them, 392 samples have complete genome sequenced, and 326 samples only have their spike genes sequenced. We extract the spike gene sequences from the complete genomes by checking the coding sequence annotation in NCBI and obtain additional 381 extracted spike gene sequences. Together with the original 326 sequences, we have a total of 707 all spike sequences and we focus on the investigation of these 707 spike sequences.The coronavirus dataset from Tang 19. Finally, we investigate the host of influenza A virus as in Kargarfard et al.11. We collect the avian influenza A virus from the Influenza Research Database19 and exclude those sequences with ambiguous host species such as chicken, duck, avian, and gull, and also those host species with less than 200 virus sequences in the database. We restrict the samples to the same taxonomic rank, and choose the level as “species” in the taxonomic hierarchy. The prediction can surely be easier for more general categories. There are six remaining avian host species: American Black Duck Anas rubripes, Blue-Winged Teal Anas discors, Green-Winged Teal Anas carolinensis, Northern Pintail Anas acuta, Northern Shoveler Anas clypeata, and Ruddy Turnstone Arenaria interpres, for further study. For each host species, we randomly choose 200 virus sequences in our study.Influenza A virus dataset from the Influenza Research Database20 for multiple sequence alignment using the default parameters and then use the software “Phylip”21 and choose the “F84” evolutionary model to calculate the pairwise distance using the alignment results as input. We also investigate several alignment-free methods for calculating the distances/dissimilarities between viral sequences using the CAFE package15 and these include Chebyshev, Euclidean, Manhattan, CVTree22, d2, 13, etc. The definitions of these distances/dissimilarities were given in Lu et al.15. Our objective is to evaluate if alignment-free approaches have similar accuracies in predicting the hosts of virus sequences, but with much high computational efficiency.We compare the performance of alignment-based, alignment-free, and machine learning based approaches for inferring the hosts of viruses. For the alignment-based method, we first use the software “Clustal Omega”23 to project the virus sequences onto two-dimensional Euclidean space. MDS is a non-linear dimensionality reduction method that can reduce the pairwise distance matrix to lower dimensional space, while best recapitulating the original distance matrix. We also use hierarchical clustering with average linkage to visualize the relationship among the viruses, and intuitively assess whether the viruses infecting the same hosts are indeed closer than those infecting different hosts.To empirically see if viruses from the same host tend to be more similar to each other than those from different hosts, we first use MDS24 method based on the pairwise distance matrix for both alignment-based and alignment-free distances for predicting the host of the virus. The alignment-free distance measures include Chebyshev (Ch), Euclidean (Eu), Manhattan (Ma), CVTree (CVT), d2, k-mer sizes. For each virus, we choose the K viruses that are closest to the virus from the pairwise distance matrix, and then count the frequency of the hosts of the K viruses. We use the most frequent host as the predicted host of the virus. For machine learning based prediction, we use SVM based on mono– and dinucleotide frequencies (3 mononucleotide frequencies and 16 dinucleotide biases10). R package e1071 was used for SVM analysis with “C-classification” as the model type and “Radial” as the SVM kernel10.We apply KNN25 and N-fold cross-validation to evaluate the prediction accuracy. We implement this process for all the viruses and then compare the predicted host with its true host to obtain the prediction accuracy.We use LOOCVThe number of known sequences for each host can significantly affect the prediction accuracy. In order to quantify the effects of sample size on prediction accuracy, we randomly choose a certain number of sequences and then apply the KNN and SVM approaches to the set of sequences to obtain the prediction accuracy as described above. We repeat this process for a series of sample sizes to see how the prediction accuracy changes with sample size. We let the sample size change from 70 to 145 with a step size of 5 for the rabies virus dataset, from 100 to 700 with a step size of 50 for the coronavirus, and from 200 to 1100 with a step size of 100 for the influenza A virus dataset. For each sample size, we randomly choose 10 sets of sequences and calculate the prediction accuracy for each dataset.All data are publicly available online and can be found based on the information provided in Materials and Methods part.Supplementary information


Some have argued that the role of propagule pressure in explaining the outcomes of bird introductions is well-supported by the historical record. Here, we show that the data from a large published database (including 832 records with propagule information) do not support the conclusion that propagule pressure is the primary determinant of introduction success in birds. A few compendia of historical reports have been widely used to evaluate introduction success, typically by combining data from numerous species and introduction locations. Very few taxa, other than birds, have usable spatially explicit records of introductions over time. This availability of data inflates the perceived importance of bird analyses for addressing factors related to invasion success. The available data allow limited testing of taxonomic and site-level factors of introduction outcomes. We did find significant differences in effort and success probabilities among avian orders and across highly aggregated spatial regions. As a test of a standard and logical expectation of the propagule pressure hypothesis, we concentrated on introductions with the smallest propagules, because it is for these the hypothesis is most likely to be correct. We analyzed the effect of numbers released in small propagules (two through 10) for 227 releases. Weighted linear regression indicated no significant effect of propagule size for this range of release size. In fact, the mean success rate of 28% for propagules of 2–10 isn’t significantly different than that of 34% for propagules of 11–100. Following the example of previous analyses, we expanded the statistical test of propagule pressure to include the full range of release numbers. No significant support for the propagule pressure hypothesis was found using logistic regression with either logit or complementary log-log link functions. Successful species introductions are all alike in that every obstacle to their establishment must have been overcome: Unsuccessful introductions can each be unsuccessful for a different reason. This notion is an extension of the observation by Leo Tolstoy in his novel (Anna Karenina) describing happy vs. unhappy families. It has been extended previously to include the field of ecological risk assessments . Here, wPropagule pressure is but one reason introductions might fail and its primacy as an explanation has been greatly inflated. First, we present the arguments for the perceived primacy. Second, we critique the assumptions and third, the data presented to support their primacy. Fourth, we present evidence that strongly supports other explanations for why introductions fail. Then, in a new analysis, we consider a large published database that includes 832 records with propagule information. These data do not support the conclusion that propagule pressure is the primary determinant of introduction success in birds.These studies all relied on compilations of numbers of individuals released per species taken from historical records. There are two major problems with this approach. The first is the implicit assumption that individuals of species were released principally, or exclusively, for establishing the species and that releases were discontinued once the species was perceived to be “established.” The second implicit assumption is that for unsuccessful introductions the number released was either insufficient or not the cause of the failure. It must also be true that for successful introductions the actual number of individuals or introduction events needed is confounded by introductions into established populations.The popularity of this perspective has advanced despite numerous studies that show equally important roles for location-level factors  and specR0) was consistently more important than propagule pressure in determining introduction outcomes across three species-life-history types. The propagule pressure hypothesis involves too many assumptions to rigorously test or support it as the primary factor.Some, for example It is also clear that the data involved are too limited and contain too many inaccuracies and inconsistencies for such a conclusion , 2016. SAccumulating many examples of inappropriate tests  does not increase the value of the comparisons. Historical records that are typically used for these analyses commonly list just a sum of individuals released. In using these values, one must ignore the fact that birds often are released for reasons other than simple establishment. Indeed, some species have been introduced multiple times to a location in order to control insect pests, increase local biodiversity, provide game for hunters, and perhaps other human motivations . The numIn one of the few studies that examines several factors together—It would not be surprising if instances where introduction failure was due chiefly, or even exclusively, to inadequate propagule pressure exist. But, as noted by 2, whereas the entire Hawaiian archipelago encompasses just 16,663 km2. We know from previous studies  listed by Phasianus colchicus) listed by With respect to temporal aggregation, the total number of individuals released as reported in this dataset gives no indication of the time span over which the releases occurred. Thus, for a sum of 100 individuals released we cannot determine if this was many releases of a few individuals over many years or fewer releases of many individuals over a shorter time span. The problem expands as the sums increase: the 12,004 Chilean Tinamous . We used a simple analysis of variance to compare probabilities of success to the log-transformed propagule sizes across the different orders listed by The For all our univariate, linear regression and logistic regression analyses we used procedures in F = 0.06, p > F = 0.82). The release of between 2 and 10 individuals  of the probability of successful introduction against propagule sizes of two through 10 individuals see . Over thuals see  compriseThe second prediction of propagule pressure is that as the number of individuals released increases, the chances for establishment success also increase but only up to a point. We argue that previous analyses of propagule pressure effects on introduction outcome using logistic regression suffer from the degree of aggregation in the releases. In the f = 830). However, this is not compelling support for propagule pressure as a dominant predictor of introduction outcome. An analysis based on unfounded assumptions cannot be considered valid even if a statistically significant relationship emerges. Moreover, given the large number of degrees of freedom in this model, statistical significance could easily lead to improper interpretations.Although we have serious reservations about doing this analysis on individual releases for such a diverse set of species, locations and times, it has been a common approach in addressing this problem. With this caveat in mind, a single logistic regression on the individual releases yielded a significant positive slope estimate .In our re-examination of the Significant differences also occurred across introduction sites. The number of releases per location declined steeply after the four sites with the greatest numbers of releases: Australia; Hawaiian Islands; New Zealand; USA. Location-level variations were not the focus of Historical records of bird introductions have often been used to assess ecological and environmental factors associated with the probability of successful establishment. Although propagule pressure has often been advanced as a primary predictor of introduction outcome, we found no support for this hypothesis in a global data set with 832 records with propagule information. For the smallest propagules , where the strongest impact should be visible, we found no significant effect of propagule size. Similarly, across the full release range of propagule sizes we found no significant support for propagule pressure.10.7717/peerj.7637/supp-1Supplemental Information 1Numbers of individuals released per species arranged in groups.Click here for additional data file.


Avian influenza viruses (AIVs) are a continued threat to global health and economy. Unlike other highly pathogenic AIVs, novel H5N8 disseminated very quickly from Korea to other areas in Asia, Europe and even North America following its first outbreak in 2014. However, the pathobiological features of the virus that favoured its global translocation remain unknown. In this study, we used a compartmental model to examine the avian epidemiological characteristics that would support the geographical spread of influenza by bird migration, and to provide recommendations for AIV surveillance in wild bird populations. We simulated virus transmission and translocation in a migratory bird population while varying four system properties: (i) contact transmission rate; (ii) infection recovery rate; (iii) mortality rate induced by infection; and (iv) migratory recovery rate. Using these simulations, we then calculated extinction and translocation probabilities for influenza during spring migration as a function of the altered properties. We find that lower infection recovery rates increase the likelihood of AIV translocation in migratory bird populations. In addition, lower mortality rates or migration recovery rates also favour translocation. Our results identify pathobiological features supporting AIV intercontinental dissemination risk and suggest that characteristic differences exist among H5N8 and other AIV subtypes that have not translocated as rapidly (e.g. H5N6 and H5N1). ASince the emergence of H5N1, many other influenza viruses have been isolated from birds. During 1999 and 2003, respectively, subtypes H7N1 and H7N3 were documented in Italy , and morAIVs are now regularly isolated in wild birds throughout the world. Due to outbreaks in humans and consequent bird culling, AIV remains a critical economic threat to the global poultry industry. Further, there is concern that a full-fledged human pandemic with a high case fatality rate might occur, should one of these subtypes mutate or reassort and gain the capacity for efficient human-to-human transmission ,18.Previous research has shown that bird migration is an important means of dissemination for AIV. Within the wild bird reservoir, type A influenza viruses persist in evolutionary equilibrium. Some highly pathogenic AIVs only cause mild symptoms in anseriformes species, which allows these birds to spread HPAIV globally during migration . OverlapUnfortunately, surveillance of AIV in wild bird populations remains limited. Most wild bird sampling is confined to dead birds and typically does not provide timely warning for preventive actions. Information that can help guide more effective surveillance is urgently needed.HPAIV H5N8 virus was first isolated in China in 2010. This virus originated from the H5N1 Gs/GD lineage . Since 2Unlike H5N8, which translocated from Asia to North America by bird migration through Beringia , many otIt remains unclear why H5N8 virus has been able to spread faster and farther by bird migration than either H5N6 or H5N1. In this paper, we use a compartmental model to evaluate how far an AIV can travel in a migratory bird population given varying epidemiological properties. We simulate virus transmission and translocation in a migratory bird population while varying four system properties: (i) contact transmission rate; (ii) infection recovery rate; (iii) mortality rate induced by infection; and (iv) migratory recovery rate. The objectives are to determine which features support the geographical spread of influenza by bird migration and to provide recommendations on surveillance of avian influenza in wild bird populations.2.2.1.We developed a model based on the form presented in . The modThe model consists of five equations (equation (2.1)) for each patch I1, I2 and R1, R2, respectively. Birds in the I1 and R1 compartments are not healthy enough to migrate but can move to I2 and R2 with a migration recovery rate (v). Birds in I1 and I2 can recover to R1 and R2, respectively, with an infection recovery rate (r). The migration recovery rate and the infection recovery rate are independent of each other. We include a mortality rate induced by infection in the I1 compartment. We do not consider cross-immunity and immunity loss in the model.Model parameters are defined in i at time t is i,tm = 0 if the birds are scheduled to be at patch i on day t, and i,tm = 1 if the birds are not scheduled to be at patch i on day t. Birth occurs beginning six weeks post-arrival at the breeding ground, continues for one month and occurs at a fixed rate of 40 new birds per day. A natural mortality rate Birds remain at each stopover site for at least 20 days, at the wintering ground for at least 91 days and at the breeding ground for at least 153 days. The average time of migration between neighbouring patches is 1 day. A migration rate matrix is used to simulate movement. Provided a bird is healthy enough to migrate, the rate of leaving patch r sites) .et al. [et al. [Previous studies have shown that environmental transmission is important for the transmission and persistence of AIV in a particular location. Breban et al.  and Roha [et al.  added anV, is affected by two processesI is the number of infected individuals, ω is the virus shedding rate and η is the virus decay rate in the environment. Dividing equation (2.3) by ω yields equation (2.2f), where Infected birds shed virus into the environment where the virus can persist. In this form, the virion population in the environment, et al. [50. After dividing equation (2.3) by ω, the environmental transmission rate becomes We assume that, at each patch, infection occurs through both contact transmission and environmental transmission. The environmental transmission per susceptible bird is derived from Breban et al.  and is g2.2.β), the recovery rate (r), the mortality rate induced by infection (v). For contact transmission rate, we followed the assumptions of Galsworthy et al. [r = 1/3 to r = 1/3 (days−1), based on previous modelling and virus experiments [−1) and the migration recovery rate from v = 1/100 to v = 1/1 (days−1). All parameters' ranges are shown in α  model and varied four model parameters: the contact transmission rate  and integrated with a time step of 0.2 days for 365 days. We used Latin hypercube sampling (LHS) to sample different parameter values based on In these stochastic simulations, the virus can go extinct within the bird population. To measure the likelihood of virus survival and translocation, for each patch and combination of parameters, we calculated the fraction of runs for which the virus is present in the bird population at a given patch, as well as extinct. We then examined all the simulations to determine the combination of pathobiological features that favour virus persistence and geographical dispersal, or, conversely, virus extinction and geographical isolation.3.Patch 5, the summer breeding ground, is the critical site for intercontinental dissemination of virus, as it is where bird populations from different regions would potentially mingle. We therefore focus on the presence and extinction of virus during spring migration (patches 1–5). Viruses with parameter combinations supporting a greater likelihood of reaching patch 5 are more likely to spread intercontinentally.−1), virus reaches patch 3 for the majority of simulations, whereas for an infection period of 3 days (an infection recovery rate of 1/3 day−1), the majority of distinct parameter combinations have a 0.25 or less likelihood of reaching patch 3. A similar but stronger distinction is seen for patch 5. For almost all parameter combinations with an infection period of 3 days (an infection recovery rate of 1/3 day−1), the virus never reaches patch 5, whereas with a longer infectious period (a lower infection recovery rate), there is a high likelihood of AIV reaching patch 5. The marginal distribution of extinction exhibits a perfect symmetric pattern with Among the four parameters, infection recovery rate exhibits the strongest effect. With an infection period of 13 days  based on the result shown in To further investigate the features that always support translocation to a given patch versus never supporting translocation during spring migration, we plotted density histograms for each of the four parameters . Always −1). For parameter combinations that never translocated to patch 5, the relationship is reversed but weaker.There is an increasing likelihood of always translocating to patches 3 and 5 as mortality rates decrease; further, there is an increasing likelihood of virus never translocating as mortality rates increase. For migration recovery rate, with a higher migration recovery rate, virus is more likely to always translocate to patch 5. The strongest effect manifests for the infection recovery rate parameter. Certain arrival of the virus at the summer breeding grounds virtually requires a lower recovery rate; approximately 80% of parameter combinations that always translocated to patch 5 had an infectious period longer than 11 days . If the virus is found to possess high transmissibility, a low infection-induced mortality rate and, in particular, a low infection recovery rate (a longer infectious period), more resources should be deployed to monitor and control the spread of the virus and preventive actions will be needed more broadly across the world.Our study has a number of limitations and areas for future exploration. Our model only simulated the translocation of virus within one homogeneous migratory bird population; however, interactions can occur among different populations and species at staging sites and breeding grounds during migration. Such mixing could allow reintroduction of a virus into a bird population; alternatively, the virus could be translocated by multiple populations . We alsoet al. [In the study of Breban et al. , virus rPathogenicity also differs among young and adult birds . Young bWe also did not consider immunity loss or cross-immunity in the model. It has been observed that immunity in birds wanes following recovery , and somDespite these limitations, our model results indicate that the geographical dispersal of avian influenza is particularly sensitive to the length of the infectious period, and that viruses with a longer infectious period (i.e. a lower recovery rate) are more likely to disperse rapidly. These findings present a working hypothesis that could be tested in both the field and laboratory.


Hemorrhagic fever with renal syndrome (HFRS) is an emerging zoonotic infectious disease caused by hantaviruses which circulate worldwide. So far, it was still considered as one of serious public health problems in China. The present study aimed to reveal the stabilities and variations of hantavirus infection in Hebei province located in North China through a long-term retrospective observation.The epidemiological data of HFRS cases from all 11 cities of Hebei province since 1981 through 2016 were collected and descriptively analyzed. The rodent densities, species compositions and virus-carrying rates of different regions were collected from six separated rodent surveillance points which set up since 2007. The molecular diversity and phylogenetic relationship of hantaviruses circulating among rodents were analyzed based on partial viral glycoprotein gene.Rattus norvegicus, was the dominant rodent species and Seoul virus S3 sub-genotype which is continued but slightly evolving perhaps to be the sole pathogen for local HFRS cases of Hebei province.HFRS cases have been reported every year in Hebei province, since the first local case was identified in 1981. The epidemic history can be artificially divided into three phases and a total of 55,507 HFRS cases with 374 deaths were reported during 1981–2016. The gender and occupational factors of susceptible population were invarible throughout, however age of that was gradually aging. The annual outbreak peak always present in spring, while the main epidemic region had gradully altered from south to northeast. Surveillance of rodents revealed that residential rodents significantly possessed higher density and virus-carring rate than field rodents. The house rat, This long-term province-wide surveillance and epidemiological analysis has revealed the stabilities and variations of hantavirus infection in North China. In order to improve current prevention and control strategies of HFRS in China, all surveillance should be continuously enhanced and variations should be paid more attentions.The online version of this article (10.1186/s12879-019-4402-8) contains supplementary material, which is available to authorized users. Hemorrhagic fever with renal syndrome (HFRS) is an emerging zoonotic infectious disease caused by hantaviruses which circulate worldwide posing the ongoing public health threat in most countries of Europe and Asia –3. HFRS Hantaviridae belonging to the Bunyavirales order [Hantaan virus (HTNV) or Seoul virus (SEOV). HTNV usually causes more severe symptoms than SEOV and they were naturally carried by Apodemus agrarius and Rattus norvegicus, respectively [Hantaviruses now have been considered as a single family, es order . So far,es order , 6. Due es order , 2. It hectively .Bunyavirales, hantavirus possesses a tri-segmented and negative single-stranded RNA genome consisting of small (S), media (M) and large (L) segment, which encodes nucleocapsid (N) protein, glycoprotein precursor (GPC) and RNA-dependent RNA polymerase (RdRp), respectively. During the virus replication, GPC will be co-translationally cleaved into the glycoproteins Gn and Gc by cellular signal peptidase. The heterodimer of Gn and Gc will be finally constructed into spikes and appeared upon the virion surface [As a member from  surface . It had  surface , 2. Mean surface .The province of Hebei is located in the Northern China plain, and surrounds Beijing, the capital of China Fig. . In thisThe course of HFRS can be ranging from frequently asymptomatic to a lethal outcome , and herIn order to figure out the potential rodents population and virus-carrying rate of Hebei province, four separated surveillance points  were set up since 2007 and two more points (Guantao county and Kuancheng Man Autonomous county) were added since 2015  accounted for the most (84.8%) cases. In addition, long-term surveillance also showed that annual age-peaks of HFRS patients were mildly but continuously aging since 1988 when age information of cases start to be collected   Fig. a. For thP < 0.001)  Table , Fig. 4bSeoul virus S3 genotype . The phylogenetic analysis of the nucleotide sequences showed that hantaviruses circulating in Hebei province evolved slowly and they all belonged to All variations of the viral nucleotides we identified are substitutions without any indel and most of them are non-signification. Among the 36 amino acid sequences of the viral partial glycoproteins, three sites  of five sequences are shown with single mutation compared to the strain isolated from Qinhuangdao in 1993  and the spring peak was mainly caused by SEOV carried by house rat (R. norvegicus) [The bigger spring peaks of HFRS were obvious and consistent annually warning people to decrease the frequency of contaction with rodent populations at this time. While, it is hard to interpret that the smaller winter peaks are going to be unapparent. Previous epidemiological study has elucidated that the winter peak was generally associated with HTNV carried by striped field rat (vegicus) , 26. TheBased on this long-term surveillance, we discovered that the high incidence region of HFRS has gradully shift. While, the principal factor of HFRS transmission dynamic is still unclear. It has been suggested that the climate variability may drive the dynamics of human HFRS infection, because the climate, such as temperature and rainfall can provide favorable conditions for both rodent population growth and virus transmission –29. UrbaAs a zoonotic disease, the incidence of HFRS has a close relationship with the local population of rodents, including host density and virus-carrying rate . The annA. agrarius the natural host for HTNV, was also seldom to be found even in the field areas of Hebei province since 2007. However, the house rat, R. norvegicus predominantly existed in both residential areas and field areas leading SEOV probably to be sole pathogen for HFRS infection in this northern China province. For the past few years, more and more evidences has proven that due to global warming or the usage of rodenticides the asian house rat, Rattus tanezumi which naturally exit in southern China has expanded their vivosphere northward and aggressively influenced local rodent population including R. norvegicus [R. tanezumi has not been proven to be a natural host for SEOV, this migration might incompletely explain the shift of HFRS high incidence region of Hebei province. Thus far, in order to comprehensively illustrate the transmission dynamic of HFRS, continuous surveillance of rodent population should be enhanced in the future.Epidemiological characteristic and clinical manifestation of HFRS can be determined by virus genotypes which are further influenced by host speicies . In centrvegicus , 37. BecBased on phylogenetic analysis, we have known that hantaviruses circulated among high-incidence region of HFRS evolved slightly during the past decade and there was no apparent geographic segregation indentified. Here we modestly gave a hypothesis that there was no extraneous hantavirus invaded recently and only native strains of hantavirus were circulated throughout in Hebei province from 1990s to date. Meanwhile it has been shown that SEOV, probably the only hantavirus species which globally spread, has spread into west world owing to the omnipresence of its rodent host , 39. TheR. norvegicus probably is sole pathogen for HFRS in Hebei province and further molecular evidence elucidates that viruses circulated during past decade are homologous but they are still slightly evolving. Continued surveillance of hantavirus infection in both humans and host rodents is needed to fight against this infectious zoonotic disease.In conclusion, this long-term province-wide surveillance in northern China indicates that HFRS epidemic periodically occurred and the high-incidence region has shift. SEOV carried by house rat Additional file 1:A completed ARRIVE guidelines checklist. (PDF 1208 kb)Additional file 2:Complete statistics of rodent density, virus-carrying rate and species composition of each surveillance points in Hebei province since 2007 to 2016. (XLSX 15 kb)


Nyamaviridae, Orthomyxoviridae, and Peribunyaviridae and have been designated as San Jacinto virus, Mason Creek virus, and Buffalo Bayou virus, respectively. Their potential public health and/or veterinary importance are still unknown.This report describes and characterizes three novel RNA viruses isolated from dead birds collected during West Nile virus surveillance in Harris County, TX, USA (the Houston metropolitan area). The novel viruses are identified as members of the families  In 2002, when West Nile virus (WNV) was first detected in Texas , the HarNyamaviridae, Orthomyxoviridae, and Peribunyaviridae that were also isolated from dead birds in Harris County during the study period.From 2002 to 2014, a total of 9047 dead birds were submitted to UTMB for culture; 1644 of the birds (18.17%) yielded WNV. Other known viruses recovered from the brain cultures of dead birds from Harris County during this 13-year period included Newcastle disease virus (NDV), eastern equine encephalitis virus (EEEV), Mermet virus (MERV), and Flanders virus (FLAV) . In thisHarris County, which includes the city of Houston, is located in the northern coastal plain of the Gulf of Mexico, a 50-m swath along the Texas Gulf Coast. Harris County covers a geographic area of more than 1768 square miles. It is the largest county in Texas; its human population was estimated to be 4,538,000 in 2015. The climate is classified as humid subtropical. The average annual rainfall in Houston is 1145 mm, with an annual mean temperature of 20.5 °C. A more complete description of the study area was given previously , and BBAV . Raw sequencing data are available upon request to the corresponding author.Viral RNAs were extracted using TRIzol LS reagent  as described previously . Viral Rhttp://www.dbs.dtu.dk/services/NetNGlyc/). The genetic relatedness of the isolates SJCV and MCRV, as well as the recovered genomes of BBAV and other viruses were first inspected by the Blastx algorithm available from the NCBI (http://www.ncbi.nlm.nih.gov/genbank/).The size, organization, terminal regions, open reading frames (ORFs), encoded proteins, and their conserved motifs were annotated using applications available in the Geneious suite. Protein functions were verified using InterPro . The ideMultiple amino acid sequence alignments were conducted using ClustalX  or in MUSuckling mouse brain, sucrose‒acetone-extracted antigens, and mouse hyperimmune ascitic fluids (HIAFs) were prepared for isolate DO 200, as well as for Nyamanini (NYMV), Midway (MIDWV), Sierra Nevada (SNVV), and Newcastle disease (NDV) viruses, as described previously ,17. The Sturnus vulgaris) collected in Crosby, Harris County, TX on 20 June 2013 (Sample DO 200 was the brain homogenate from a dead European starling (une 2013 . The samCyanocitta cristata) collected in Katy, Harris County on 27 July 2011  were observed to be forming at the cell surface a. SpheriNyavirus, family Nyamiviridae, order Mononegavirales ) , Shuangagavirus) , and twogavirus) ,29  . SgIV-1 gavirus) . KHUV anaviridae  and so aThe potential public health or veterinary importance of SJCV, MCRV, and BBAV is unknown, as are their prevalence and host range in Harris County. These are subjects for future investigation. Likewise, the pathogenesis of the three viruses should be examined in several avian and possibly mammalian species.The discovery of the three novel viruses during our WNV surveillance program also reinforces the value of virus culture in arbovirus surveillance programs. Many public health and veterinary diagnostic laboratories now simply screen animal carcasses or pools of hematophagous insects by PCR or other nonculture techniques for the detection of RNA of viruses of interest . While these techniques are very sensitive, provide faster results, are more economical, and reduce the risk of accidental infection of laboratory personnel, they generally detect only the viruses for which one has appropriate primers. These techniques usually do not detect novel or unexpected viruses.For discovering novel viruses, metagenomic analysis is now the current gold standard, as it allows the detection and genetic characterization of all associated viruses in a sample. However, metagenomic analysis is not a feasible technique for diagnostic laboratories that test thousands of field samples annually because of the per-sample cost, the labor involved in sample preparation, and the sophisticated equipment required. Culture is not ideal, since its sensitivity is dependent on the culture system(s) used and the growth characteristics of the virus(es) present in a sample. Nonetheless, primary culture still allows for the detection and isolation of some novel or unexpected viruses that would otherwise remain unknown.


Variation exists in the resource categories included in economic evaluations, and National Institute for Health and Care Excellence (NICE) guidance suggests the inclusion only of costs related to the index condition or intervention. However, there is a growing consensus that all healthcare costs should be included in economic evaluations for Health Technology Assessments (HTAs), particularly those related to extended years of life.We aimed to quantify the impact of a range of cost categories on the adoption decision about a hypothetical intervention, and uncertainty around that decision, for stable coronary artery disease (SCAD) based on a dataset comprising 94,966 patients. Three costing scenarios were considered: coronary heart disease (CHD) costs only, cardiovascular disease (CVD) costs and all costs. The first two illustrate different interpretations of what might be regarded as related costs.Employing a 20-year time horizon, the highest mean expected incremental cost was when all costs were included (£2468) and the lowest when CVD costs only were included (£2377). The probability of the treatment being cost effective, estimating health opportunity costs using a ratio of £30,000 per quality-adjusted life-year (QALY), was different for each of the CHD (70%) costs, CVD costs (73%) and all costs (56%) scenarios. The results concern a hypothetical intervention and are illustrative only, as such they cannot necessarily be generalised to all interventions and diseases.Cost categories included in an economic evaluation of SCAD impact on estimates of both cost effectiveness and decision uncertainty. With an aging and co-morbid population, the inclusion of all healthcare costs may have important ramifications for the selection of healthcare provision on economic grounds.The online version of this article (10.1007/s41669-018-0068-1) contains supplementary material, which is available to authorized users. Interventions in patients with coronary heart disease (CHD) or at risk of CHD present significant costs to the UK National Health Service (NHS), may reduce the risks of CHD events such as acute myocardial infarction (MI), may reduce the risk of other (non-CHD) health events and can potentially improve survival patients , 2. One In an economic evaluation the incremental costs incurred by a new intervention and the health benefit it generates are compared to the health benefit of activities that could have been funded with the same resources elsewhere . There iThe debate considers two types of costs: related and unrelated. Garber and Phelps  define uIn practice, whether costs are related or unrelated is based on a judgement of whether they are plausibly related to either the condition or the intervention being considered. This is a subjective assessment and is argued to result in analyses that impose arbitrary restrictions on the costs that are considered relevant . FurtherA review of published economic evaluations of the treatment of MI finds considerable variation in the types of costs included, reflecting both the subjectivity in determining relevance and variation in recommendations among guidelines. Nineteen economic evaluations published since 2006 are categorised according to the cost categories included: CHD costs only, CVD costs only or all costs . CHD costs form the smallest cost category, including costs exclusively related to CHD, e.g. costs attributable to MI. CVD costs include not only all costs attributable to CHD but all costs related to the cardiovascular system more broadly, e.g. ischaemic and haemorrhagic stroke. The CHD costs category is therefore a subset of the CVD costs category. These categories are both subsets of the all costs category, where all costs are included regardless of to which health conditions they are attributable, e.g. they therefore include CVD and non-CVD costs such as those attributable to cancer. Of the 19 studies, five were found to include CHD costs only, eight CVD costs only and six evaluations included all costs. The main difference between studies including CVD costs rather than CHD costs was that costs from stroke events were included. Studies considering related costs only tended to be those that were trial-based analyses, where only CHD/CVD-related data were recorded, or model-based approaches including only CHD or CVD events. One observational data study considered only CVD costs where the analysis was performed on a dataset with detailed information collected on only a select number of CVD endpoints . Of thosGiven the variation in practice by analysts in conducting economic evaluations of MI treatment and the difficulty in distinguishing what costs are related from those that are not, it is important to consider the implications of which costs are included in an economic evaluation. In this paper a recently published ‘real-world’ cost-effectiveness model is used to consider a hypothetical intervention that reduces the risk of CVD health events for patients with SCAD to investigate the impact of including different cost categories on expected cost effectiveness and decision uncertainty. This analysis represents the first attempt to consider the implications of collecting broader cost data not only in terms of addressing concerns around the expected incremental costs, but also in terms of any impacts upon the associated decision uncertainty.In order to illustrate and quantify the importance, in terms of incremental cost and decision uncertainty, of the analyst’s decision around which cost categories to include, three different scenarios are specified using the CALIBER model . The thrThe model is evaluated probabilistically, with 999 iterations run in total. For each iteration of the model eight key results are recorded: quality-adjusted life-years (QALYs) associated with the intervention and control groups and costs associated with the intervention and control groups, according to three different costing scenarios. Two of the three scenarios represent plausible approaches to including only related costs as per the NICE guidance on this issue, one of which is narrow and the other slightly broader. In the CHD costs only and CVD costs only scenarios, only healthcare costs that are identifiable as CHD and CVD through the ICD 10th edition (ICD-10) codes of the recorded Health Resource Group (HRG) are included.Cost results are first considered in isolation. The mean costs in the treated and control groups are presented along with summary statistics of the incremental costs, illustrating the effect of the three different scenarios on the location and dispersion of the distribution of estimated incremental costs, in Table Mean costs over a 20-year time horizon for both the treated and control groups increase with the inclusion of additional cost categories. In terms of incremental costs, including all costs results in the highest mean incremental cost. The CVD costs only scenario gives the lowest mean incremental cost. Variability of incremental costs increases when more cost categories are included based on the width of the 95% credibility interval. The distribution of incremental costs is presented in Fig. Figure To investigate the relative effect of unrelated costs in extended years of life compared with those in life-years not extended by the intervention, we investigate the distribution of incremental costs for different time horizons up to 20 years, with changes over time likely to reflect the increasing impact of costs in extended years of life. These results are summarised in Figs. Figure It is also useful to consider the impact of incremental costs on cost effectiveness in order to illustrate how these findings could affect decision-making and decision uncertainty. In all scenarios the mean incremental QALY from treatment is 0.084. These are the denominators in the estimates of the incremental cost-effectiveness ratios (ICERs) provided in Table For illustrative purposes we consider a decision maker who regards a treatment as cost effective when the ICER is less than £30,000 per QALY gained . Whilst The proportion of iterations with a positive net health benefit, i.e. the probability of being cost effective, varies across the three scenarios: CHD costs (70%), CVD costs (73%) and all costs (56%). These percentages reflect the distribution of net health benefit in the different scenarios where both the location and dispersion of the distributions is influenced by the choice of cost categories to be included.This paper assesses the cost effectiveness of a hypothetical intervention using a previously published model based on ‘real-world’ data to demonstrate that the inclusion of unrelated costs will affect both the mean incremental costs, ICER and decision uncertainty.Unambiguously, costs will be greater when more categories are included (given costs are non-negative). It is harder to predict how the different cost category scenarios will affect the estimated incremental costs that are required for economic evaluation. The treatment under consideration here reduces the risk of cardiovascular events, each with associated CHD, CVD and non-CVD costs, in normal years of life (i.e. not extended years of life). Each of these events is potentially fatal, and so the treatment extends life. This distinction is important when thinking about the overall effect on incremental costs. In normal years of life, including more cost categories increases the magnitude of the cost savings that are brought about by the reduced risk of cardiovascular events.In addition to the effect on mean incremental costs, this paper also explores the effect that the different scenarios have on decision uncertainty. Again, this effect needs to be thought of as two separate underlying processes. The first of these is, when considering a technology that has positive incremental costs and incremental QALYs, the proximity of the mean ICER to the ‘cost-effectiveness threshold’ being used in the analysis.Where there is no extension of life effect from a treatment, including unrelated costs will not affect the mean ICER, in expectation, as these additional costs would be the same on average regardless of treatment received. Decision uncertainty, however, will be affected given the variability of costs. As such, under these circumstances, the addition of truly unrelated costs amounts to adding noise and increased variability into the model.Adding noise and variability can lead to greater uncertainty and there are two reasons why this might be a concern. The first is that there will be reduced power to detect a statistically significant effect, since standard errors will increase. The second concern, more prevalent within health economic evaluation, is that the decision uncertainty will be inflated, thus reducing the probability of making the right decision. Of course, the analyst will never know what is truly related and truly unrelated and so identifying something as noise with certainty is impossible. This is not to say that unrelated costs should never be included because of the potential of introducing noise, but it is important to consider the implications of adding in what might be truly unrelated costs in normal years of life. This issue has been overlooked in the literature on this topic to date.As already discussed, there is reason to caution against including unrelated costs in some cases where there is no (or little) mortality effect. In addition, obtaining data on unrelated costs may not be without cost to the analyst, especially obtaining data that are both very unlikely to be related and are realised many years in the future—although this may be getting less costly with time  as a groLack of appropriate and robust data will be an issue in many circumstances. Experimental evidence typically does not have a sufficiently long time horizon to identify the occurrence of all future related and unrelated events and their associated costs. As an alternative, the use of observational data from administrative sources is becoming increasingly prevalent and is well-suited to estimate parameters with large numbers of observations. Observational data are typically collected over a longer period of time; however, by design it is not as robust as randomised trial data when it comes to estimating the incremental cost caused by treatment, with potential bias coming from unobserved confounding and selection effects. With both randomised controlled trial (RCT) and observational evidence there is the issue of sufficient breadth of study design to identify all related and unrelated costs. By their very nature, such studies would be unlikely to be focused on collecting events unrelated to the primary intervention and, as such, data collection would have to be sufficiently flexible or broad to capture all costs (related and unrelated). Routine data sources, such as HES in the UK context, which capture all healthcare-related interventions and hospital visits are another possibility. HES has recently been used to compute average costs by age, sex and index of multiple deprivation (IMD) group . This soEven in the absence of observational data, it is still possible to place a reasonable estimate on these future unrelated costs parameters. There are methods that can be used to illustrate the potential range values for these parameters, reflecting their degree of uncertainty . This cawhich costs matter and why, but should not be used to say how much different costs matter outside of the context analysed. For example, within the context analysed, it can be seen that the variability of incremental costs is generally low, irrespective of which costing scenario is used. This results from the large number of observations upon which the system of equations is estimated, and hence granting a degree of precision that would not be attainable in smaller studies.Because the model itself concerns a hypothetical intervention, the specific results should not be over-interpreted, nor can they be generalised for assessments of how much of an impact the inclusion of unrelated costs will have in different contexts. The results do allow us to identify the types of effects that will result from including unrelated costs. To put this another way, this paper contributes to the literature by illustrating It is suggested that economic evaluations should include estimates of incremental healthcare costs that fall within the healthcare budget and not just the intervention costs associated with the treatment. However, in moving beyond intervention costs, the analyst faces challenges in determining which healthcare costs (unrelated and unrelated) are truly caused by the treatment, which can be particularly demanding when there are variable and noisy cost data, and when the knowledge around the natural history of a disease is not well-known, as in the case of acute MI survivorship. With an aging and co-morbid population, the inclusion of all healthcare costs may have important ramifications for the selection of healthcare provision on economic grounds.which costs matter—in the context of SCAD, where little is known about what is truly related or unrelated and there is considerable variation in analytical practice concerning cost categories to include in economic evaluation. It has been shown that costs matter, not just in terms of the point estimate of cost effectiveness, the expected ICER, but also in terms of decision uncertainty. Where adding unrelated costs in evaluations without an impact on survival, this may simply add noise to the data and therefore will reduce the probability of making the right decision.This paper contributes to this discussion by illustrating the importance of different types of costs—The inclusion of unrelated costs is important, especially when the treatment is expected to extend life, and so including all cost categories is at least recommended as a sensitivity analysis .Supplementary material 1 (DOC 85 kb)Below is the link to the electronic supplementary material.


Chlorella virus system, which is not only the best characterized algal-virus system, but is also responsible for much of the foundation in the field of aquatic virology.The scope for ecological studies of eukaryotic algal viruses has greatly improved with the development of molecular and bioinformatic approaches that do not require algal cultures. Here, we review the history and perceived future opportunities for research on eukaryotic algal viruses. We begin with a summary of the 65 eukaryotic algal viruses that are presently in culture collections, with emphasis on shared evolutionary traits  of each known viral type. We then describe how core genes have been used to enable molecular detection of viruses in the environment, ranging from PCR-based amplification to community scale “-omics” approaches. Special attention is given to recent studies that have employed network-analyses of -omics data to predict virus-host relationships, from which a general bioinformatics pipeline is described for this type of approach. Finally, we conclude with acknowledgement of how the field of aquatic virology is adapting to these advances, and highlight the need to properly characterize new virus-host systems that may be isolated using preliminary molecular surveys. Researchers can approach this work using lessons learned from the  Viruses infecting eukaryotic algae are extremely diverse. They have been reported with DNA or RNA genomes in various architectures  and sizes 4.4 to 638kb) . Some vi8kb . SomIt is a relatively recent realization that algal viruses are ecologically significant. In fact, the whole history of algal virus research has occurred primarily in just the last half century . While tChlorella ,,30,29,30Incerta sedis [Pyramimonadales and Raphidophyceae were not abundant in the TARA Oceans 18S dataset, select species in these groups are known bloom-formers [Phaeophyceae and Rhodophyceae). Abundant lineages without an algae-infecting virus include photosynthetic Dictyochophyceae, the Prasino Clade 7 group, the Chryso/Synuro group, and the Apicomplexans—though some of the highly represented lineages could be attributed to non-photosynthetic members. Establishing well characterized host-virus systems in these lineages could be very useful for bloom-forming algae of these lineages. For example, it would be appealing to isolate a Pseudochattonella (Dictyochophyceae) infecting virus, as the host alga is responsible for fish kills. In 2016 Pseudochattonella was responsible for a massive fish kill in Peru amounting to an economic loss of ~$800 million dollars [Mesodinium rubrum depends on ingestion of photosynthetic cryptophytes to obtain necessary organelles  [Emiliania huxleyi, where viral-infected cells are ingested by zooplankton at different rates than non-infected cells [The diversity of algal viruses mirrors that of their hosts, bearing in mind that the name “algae” does not denote a common evolutionary relationship. Indeed, algae have been observed in freshwater, marine, and terrestrial systems, in unicellular, colonial, or multicellular forms, and in disparate taxonomic lineages. Nevertheless, the diversity of algae can be depicted using an existing taxonomic framework that includes seven “supergroups” consisting of Excavata, Amoebozoa, Opisthokonta, Archaeplastida, the SAR group , and a series of non-delineated, “cryptic” organisms collectively referred to as the ta sedis . Beyond ta sedis  to illus-formers ,38,39,40 dollars . In anotnucleus) . Virusesed cells ,44.Emiliania huxleyi cells have been observed to simultaneously contain both small (50–60 nm) and large (185–200 nm) intracellular VLPs [Pyramimonas orientalis [Mimiviridae [Phycodnaviridae [Aureococcus anophagefferens to its known dsDNA virus AaV as well as to uncharacterized ssDNA viruses [Eukaryotic algal viruses in culture collections have been isolated from ~60 alga species . Most oflar VLPs . Similarientalis , but curientalis . It is piviridae ,96, and aviridae ,33 virus viruses , althougHeterosigma akashiwo, Chaetoceros tenuissimus, Micromonas pusilla, and Heterocapsa circularisquama, and in all cases the different virus types infect the same host strain [Heterosigma akashiwo viruses HaRNAV and HaDNAV is especially intriguing given these viruses exhibit opposite infection dynamics; the RNA virus has a high viral production rate, but a slower lytic cycle, whereas the DNA virus quickly replicates but produces fewer particles [Heterocapsa viruses [Mimiviridae or Phycodnaviridae , EsV-1 (Phaeoviruses), and EhV-86 (Coccolithoviruses) yielded only 14 conserved homologs from a pool of ~1000 genes [Phycodnaviridae [Most dsDNA viruses infecting algae are members of the NCLDV group, with the proposed exception of Tsv-N1 . Algal-Nivirinae , but forrviridae . To date00 genes . A more aviridae ,104,105.Micromonas pusilla and Chlorella variabilis viruses) with differences in latent phases, burst sizes, and genome size [Aureococcus anophagefferens Virus (AaV) encodes a more even mixture of host, bacterial, archaeal, and viral genes [Chlorella algae is an endosymbiont of Paramecium that is certainly in close proximity to bacteria, the non-ancestral genes carried by the virus are evenly dispersed across its genome [It is anticipated that any single algal host can be permissive to many closely related virus variants, whereby phylogenetic comparisons of their core genes will reveal distinct clades . In total, diatoms are a collective of an estimated 12,000–30,000 species, representing one of the most abundant phytoplankton groups in freshwater and marine environments . Most di5–6.0 kb . The gen Zealand . Phyloge Zealand ,111. TheMicromonas pusilla, forms its own genus within the family Reoviridae (unassigned order) and has been proposed to be the ancestral line of the Reoviridae based on its placement between clades that demonstrate turreted or non-turreted virions [Chondrus crispus virus (CcV), a toti-virus like entity. CcV represents an extraordinary case of a putative quasispecies virus that was accidentally discovered when a small band of dsRNA (~6 kb) was observed during host genomic preparation for sequencing [Chondrus crispus and Micromonas pusilla are ecologically important algae, characterization of their relationship with these viruses is important and perhaps reflective of a need to search for more dsRNA viruses associated with algae.Algae-infecting viruses with single (ss) and double-stranded (ds) RNA genomes have also been isolated and characterized, although most attention has been focused on the ssRNA isolates. Both virus groups encode an RNA-dependent RNA polymerase (RdRP), as well as proteases and helicases that can be used to infer distant evolutionary relationships. Most information on dsRNA algal viruses has been derived from the original isolation papers describing the evolutionary relationships of the isolates. MpRV, a dsRNA virus of  virions . The othquencing . Similarquencing . Since bHeterocapsa and Heterosigma are the sole members of the families Alvernaviridae (unassigned order) and Marnavirdiae , respectively [Bacillarnavirus  includes formal members Chaetoceros socialis forma radians RNA virus, Chaetoceros tenuissimus RNA virus 01, and Rhizosolenia setigera RNA virus 01. Other diatom viruses Csp03RNAV, AglaRNAV, and CtenRNAV type II are putative members of Bacillarnavirus based on phylogenetic relationships of replicase or structural proteins [Chaetoceros sp. in addition to Chaetoceros tenuissimus [ssRNA viruses have received considerably more attention since their hosts are common marine phytoplankton with some species capable of forming harmful blooms ,115,116.ectively ,112, whiuissimus . These vMimiviridae major capsid protein (AMmcp) [polB primers based on observations that environmental datasets tend to amplify prasinoviruses, even though these may be environmentally abundant viral types [Phaeocystis globosa infecting viruses, and these groups phylogenetically cluster into different families [Developing algal-virus model systems in the lab can inform much on the biology and ecology of algal viruses, but dependence on these systems is a limiting step. The ability to determine viral geographic distributions, population fluctuations, and diversity ultimately depends on analysis of environmental samples. Microscopic methods , flow cy (AMmcp) . For refal types . The genfamilies . Diversifamilies , but thiMimiviridae specific mismatch repair gene (MutS) [A recent clone library of PCR amplicons generated using the two mcp primer sets demonstrates a wide diversity of algal viruses isolated from marine and freshwater environments . This ste (MutS) . Novel ge (MutS) ,133. Thie (MutS) . Moreovee (MutS) .While diversity can be addressed with degenerate primer PCR amplification, one of the major drawbacks of this approach is that it is generally not suitable for quantitative measurements . Indeed,Bacilladnaviridae [Smacoviridae [Because community scale genomics and transcriptomics are not dependent on target amplification, they are better suited for resolving viral diversity and can in some cases allow for the assembly of complete viral genomes. Though this is more readily accomplished in small RNA and DNA viruses ,109,141,oviridae , and manoviridae ,144. Somvia binding) of poly-A containing nucleic acid  to focus on the active viral community [While becoming more common, sequencing entire viral communities remains challenging and each experimental step must be considered in the context of existing biases and the project objectives. Virus particles have very low nucleic acid contents, necessitating amplification, concentration, or enrichment to obtain adequate sequencing depth. Simple approaches to do this involve concentration of environmental samples via filtration  or chemiommunity . This isommunity . Though ommunity ,152, butThe viral sequences generated from any sequencing approach are subjected to a general analytical workflow involving quality filtering, assembly, annotation, and diversity analyses. Many tools are available to perform this bioinformatic workflow , but fewAn example of a bioinformatics workflow using a custom marker gene database to interpret NGS sequences  is shown in Information on virus abundance or activity can be inferred by mapping trimmed metagenomic or metatranscriptomic reads back to viral contigs normalized for between-sample comparisons . However, there are some caveats to consider when examining environmental metatranscriptomes. Transcript abundance is not directly related to viral abundance for two reasons: First, biases are known to exist for highly transcriptionally active viruses, and second, single host organisms can support high viral loads. Moreover, virus metatranscriptomes can be contaminated with chimeras generated during assembly, remnant viral genes may be expressed from cells , and genAureococcus anophagefferens) linked the brown alga to its known virus, AaV, demonstrating the ability to extract known relationships with this approach. Several other clusters were generated from the same study, including smaller networks of single virus-host pairs and expected associations between Prasinophyceae and Phycodnaviridae. Roux et al., 2017 [Mimiviridae and their extended alga-infecting members to drastically expand the diversity of known virophage hosts.Another enticing application of community sequence data is the potential to deduce biological interactions using co-occurrence or network analyses. This is a relatively new approach that was developed for microbiome communities but has the potential to identify novel virus-host pairs . Two stul., 2017  focused p-values of 0.001 should be used for high-precision network detection and rare OTUs should be removed prior to network construction.Altogether, predictions stemming from the studies noted above demonstrate how network analyses can generate testable hypotheses for future studies of algal virus-host interactions. By deducing sequences of virus-host pairs, one can attempt to confirm probable virus-host interactions. For example, a variation of fluorescent in-situ hybridization, deemed phageFISH, could be used to label virus and host genes in infected cells . Additioprotocols.io  and with cross-discipline collaborations fostered at research workshops funded by organizations like the Gordon & Betty Moore Foundation (GBMF) and the Canadian Institute for Advanced Research (CIFAR). The development of long-read sequencing methods, preemptively deemed “third-generation sequencing”, may address many of the issues with short-read assembly and viral quantification. DNA barcoding has been suggested as a cheap, reliable method to quickly track virus populations, and has recently been shown to recapitulate general viral community structures using sample volumes no bigger than a cup of water [The opportunities for algal virus ecologists are at an all-time high. Bioinformatic tools are becoming more accessible to a wide variety of scientists through the creation of publicly available genomic databases and graphic interfaces that mediate interactions with traditional command-line software . At the of water . New virof water , as closof water . Even beChlorella virus system, the Van Etten lab has also shown that Chlorella viruses are biochemically novel in multiple ways. Virion proteins are glycosylated using a unique viral encoded machinery [Chlorella viruses have the smallest potassium ion channels that function to depolarize host membranes, concomitantly inhibiting secondary infection and host metabolite transporters [Chlorella viruses are ubiquitous in freshwaters across the globe, despite their hosts being sequestered as endosymbionts of Paramecium bursaria [Paramecium bursaria catalyzes this contact by making the endosymbiotic algae available to Chlorella viruses in the environment [Chlorella viruses are more resistant to environmental degradation than other algal viruses, and can even overwinter under ice [James L. Van Etten, for whom this special issue is in honor of, has spent the last forty years laying the foundation for aquatic virology. Not only did he open doors for other algal virus researchers to join the field, but he has set the standard for characterizing the biology and ecology of isolated algal virus systems. Along with the genomic, transcriptomic, and proteomic work done in the achinery ,172, andachinery . Many ofachinery . Chlorelsporters . Within sporters ,176. Aloironment ,178. Anonder ice . Collect


In Volume 12, issue 2, 2018, in the article “Migratory birds in Southern Brazil are a source of multiple avian influenza virus subtypes” by Jansen Araujo, Maria Virgínia Petry, Thomas Fabrizio, David Walker, Tatiana Ometto, Luciano M. Thomazelli, Angelo L. Scherer, Patricia P. Serafini, Isaac S. Neto, Scott Krauss, Robert G. Webster, Richard J. Webby, Edison L. DurigonBelow reference had been used incorrectly in the article in the reference section:Virus Genes. 2013;46:54‐62.46. Wei F, Li J, Ling J, et al. Establishment of SYBR green‐based qPCR assay for rapid evaluation and quantification for anti‐Hantaan virus compounds in vitro and in suckling mice. The correct reference should beLancet Respir Med. 2013;1: 771‐778.46. Wei S‐H, Yang J‐R, Wu H‐S, et al. Human infection with avian influenza A H6N1 virus: an epidemiological analysis. The authors apologize for the error.


Isaria fumosorosea, and Hirsutella citriformis were found to be efficient against the Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Liviidae). However, the susceptibility to these fungi increases when the psyllid infected with Candidatus Liberibacter asiaticus (Las), which is transmitted by D. citri and causes citrus greening disease. In this study, we examined the Las‐infected and Las‐uninfected D. citri susceptibility to entomopathogenic fungi at different temperature regimes (5–40°C). When D. citri adults exposed to cold temperature (5°C), they showed less susceptibility to entomopathogenic fungi as compared with control (27°C). Irrespective of infection with Las, a significantly positive correlation was observed between temperature and percentage mortality caused by different isolates of I. fumosorosea, 3A Ifr, 5F Ifr, PS Ifr, and H. citriformis isolates, HC3D and 2H. In contrast, a significantly negative correlation was found between temperature and percentage mortality for 3A Ifr for both Las‐infected and Las‐uninfected psyllids. Detoxification enzymes, Glutathione S‐transferase levels in D. citri showed a negative correlation, whereas cytochrome P450 and general esterase levels were not correlated with changes in temperature. These findings revealed that detoxification enzymes and general esterase levels are not correlated with altered susceptibility to entomopathogenic fungi at the different temperature regimes. Conclusively, temperature fluctuations tested appear to be a significant factor impacting the management strategies of D. citri using entomopathogenic fungi.Some entomopathogenic fungi species,  DiaphorD. citri   and Hirsutella citriformis Spear   values of chemical insecticides for D. citri, based on the temperature regimes have been previously investigated  was investigated and compared with control (27°C). Furthermore, experiments were conducted to assess Las‐infected and Las‐uninfected D. citri detoxification enzymes activity levels; glutathione S‐transferase (GST), cytochrome P450, and general esterase, at different temperature regimes. Tiwari et al. , Fujian China from Murraya paniculata (L.) Jacq.  Plants (Orange Jasmine). The stock populations were maintained for about nine generations prior to experiments on the same host plants kept in mesh cages (50 × 50 × 50 cm) in greenhouse at a temperature of 27 ± 1°C, a photoperiod of 14:10 light/dark , and 75 ± 5% relative humidity (RH), with no insecticide exposure. To obtain clean and homogenous colony of Las‐infected and uninfected psyllids colonies for the experiments, the apparently clean above stock populations were transferred onto Orange jasmine plants grown from seeds in an insect‐proof greenhouse at 28°C, 40% RH and L16: D8 for 4 months in mesh cages (50 × 50 × 50 cm). The 4‐month‐old seedlings were infected by grafting of Orange jasmine with four pieces of bud wood sticks from a PCR‐positive HLB source. The infection was determined and confirmed using PCR as described by Tatineni et al.  plates and were maintained at 25 ± 2°C in complete darkness. For bioassay, entomopathogenic fungi conidia were scraped off from 2‐week‐old plates with a sterilized spatula and suspended in 20 ml of autoclaved deionized water containing 0.03% Tween 80. The conidial suspensions were vortexed in for 5 min to produce homogenous conidial suspensions and then filtered through Miracloth. Fungal conidia were counted using a Neubauer Hemacytometer, and concentrations were determined through dilution. The required conidial suspension with a standard concentration of 1 × 108 conidia/ml was obtained for the five entomopathogenic fungal isolates by serial dilutions containing 0.03% Tween 80 (Fluka) as a wetting agent.In this study, five entomopathogenic fungal isolates from two genera; 2.1D. citri and transferred onto clean citrus plants in mesh cages (50 × 50 × 50 cm). The mesh cages containing D. citri were shifted to climate box set at 5°C, 55% RH, and at a photoperiod of 14:10 hr L:D or were maintained at 27°C (control) for 1 or 2 weeks before the experiment. Psyllids from 5°C temperature were assessed using a leaf‐dip Petri dish method described by Kumar, Poehling, & Borgemeister  and HLB‐infected colonies of emeister ) and Tiwemeister . Plasticemeister  were pla2.2Ifr, or HC3D‐treated adults maintained for 48 hr at six different temperature regimes . Each entomopathogenic fungal isolate was repeated three times at each temperature, and for each replication 80–100 adult psyllids were tested. Psyllids of mixed gender were transferred onto leaves sprayed with entomopathogenic fungal isolate suspensions at the concentration of 1 × 108 conidia/ml, using the Petri dish method as described above. About 80–100 adult psyllids were shifted to each Petri dish. Psyllids which survived from each treatment after 48 hr exposure were collected and used immediately for detoxifying enzymes expression levels assays.The effect of temperature on three detoxifying enzymes expression levels was studied by using the Las‐uninfected colony. Treatments consisted of 5F The enzymes were prepared by following previously described methods  were replicated three times, where each replicate comprised of n = 80–128 psyllids. Each entomopathogenic fungal treatment and the control were replicated three times, and the whole bioassay was repeated twice. Psyllid mortality was calculated 48 hr after shifting to the growth chamber. Psyllids were considered dead when seemed their sides or backs and not able to move when touched with a soft camel hair brush.Entomopathogenic fungi bioassays were conducted using a leaf‐dip Petri dish method as described above. Petri dishes containing the sprayed leaves at 1 × 10D. citri was transferred into a sterile 2 ml microcentrifuge tube  consisting of 80% ethanol, and kept at −20°C for further analysis. After mortality data were calculated, DNA was again extracted from the exposed Las‐infected psyllids, just for reconfirming Las infection by using quantitative real‐time PCR following the previously described protocol , whereas the correlation analyses between detoxifying enzymes expression levels and temperature were determined separately for each entomopathogenic fungi and enzyme combination. SPSS 19.0 statistics software was used to perform all the data analysis.The mean mortality (%) among psyllids exposed to various entomopathogenic fungi was analyzed using Analysis of Variance (ANOVA) test, and correlation analyses were conducted at  Abbott, . To calc33.1D. citri were exposed to cold stress (5°C) for 1 week, they were significantly less susceptible to 5F Ifr than control psyllids at 27°C  or HC3D  had nonsignificant effect on cytochrome P450 activity at all the temperature regimes  and HC3D . General esterase activity levels of psyllids treated with 5F Ifr  or HC3D  had positive effect of temperature  or HC3D  had nonsignificant relationship. However, when comparing the cytochrome P450 and general esterase activities  or HC3D   or HC3D .Psyllids treated with 5F 3.3F4,50 = 63.40, p = .0034) and temperature  had each a significant effect on D. citri mortality; however, the relationship effects between these factors was nonsignificant . Data analysis showed that the temperature regimes had significant effects on the mortality of psyllids for 3A Ifr , 5F Ifr , PS Ifr , HC3D  and 2H  , PS Ifr , HC3D , and 2H . In contrast, there was a significantly negative correlation between temperature and psyllids mortality for 3A Ifr .Our results showed that the interactions between psyllids treatments (Las‐infected and Las‐uninfected psyllids), temperature and entomopathogenic fungus had a significant effect on the mean mortality (%) of psyllids Table . In Las‐F5,117 = 15.23, p = .0001) and temperature  and the interaction between these factors had significant effects on D. citri mortality . For all entomopathogenic fungi tested, temperature had a significant effect on psyllids mortality for 3A Ifr , 5F Ifr , HC3D  and 2H . In contrast, temperature had no significant effect for PS Ifr  , PS Ifr , HC3D , and 2H . It was also observed that a significantly negative correlation exists between temperature and psyllids mortality for 3A Ifr .For the Las‐uninfected psyllids, entomopathogenic fungi , 5F Ifr , PS Ifr , HC3D  and 2H  at the various temperature regimes  and herbivore susceptibility to biopesticides may help to understand and design better management strategies against the pests. Our results showed that alterations in psyllids susceptibility to entomopathogenic fungi are due to changes in temperature, which are not related to changes in detoxifying enzymes expression levels. However, psyllids exposed to cold stress are less susceptible to entomopathogenic fungi I. fumosorosea and H. citriformis than control. Thus, seasonal variations in temperature and entomopathogenic fungi may have a major impact on the management of D. citri under field conditions.Recently, biopesticides are developed as the best tool for management of herbivores like None declared.