The objective of this study was to evaluate the cost-effectiveness of abatacept, tocilizumab, and tumor necrosis factor (TNF) inhibitors as compared with rituximab in Finnish rheumatoid arthritis patients, who have previously been treated with TNF inhibitors.th line treatment until death. The patients’ baseline characteristics and regression models used in the simulation were based on observational data from the National Register for Biological Treatments in Finland. Direct costs comprised drug costs, administration costs, costs of switching, and outpatient and inpatient care, while indirect costs included disability pension and sick leaves due to rheumatoid arthritis. Several subgroup and deterministic sensitivity analyses were conducted.A patient-level simulation model was developed to predict costs and outcomes associated with four biological drugs  in the treatment of rheumatoid arthritis. Following lack of efficacy or adverse events, the patients were switched to another biological drug until all four options were exhausted. After that, the patients were assumed to receive a 6Drug costs were the lowest for rituximab, but when administration costs and costs of switching were included, drug costs were the lowest for TNF inhibitors. Abatacept was associated with the highest drug costs, whereas rituximab was associated with the highest healthcare costs. In total, TNF inhibitors had the lowest direct costs, while rituximab had the highest direct costs. The amount of quality-adjusted life years (QALY) gained ranged from 9.405 for rituximab to 9.661 for TNF inhibitors. TNF inhibitors, abatacept, and tocilizumab were dominant in comparison to RTX.TNF inhibitors, abatacept, and tocilizumab had lower costs and higher QALYs than rituximab, and therefore, they were dominant in comparison to rituximab. As TNF inhibitors had the lowest costs and highest QALYs, they were the most cost-effective treatment option. Rheumatoid arthritis (RA) causes significant costs for society due to the increased use of healthcare resources, sick leaves and early retirements. Consequently, effective anti-rheumatic therapies have the potential to reduce societal costs while also improving the patients’ quality of life. According to current Finnish Care Guideline, treatment of RA should be initially treated with a combination of methotrexate (MTX), hydroxychloroquine (HCQ), sulfasalazine (SSZ) and a low-dose glucocorticoid . In caseA previously published systematic review indicated RTX as the most cost-effective bDMARD among patients with an insufficient response to bDMARD treatment . Previouth line treatment until death. The outline of the model is presented in We developed a patient-level simulation model using R statistical programming language 3.2.2 to estimate costs and outcomes associated with different bDMARDs in the treatment of RA. The population consisted of RA patients who had previously used a TNF inhibitor as their first bDMARD and were about to begin their second bDMARD. The model simulated four alternative treatment regimens: ABA, RTX, TCZ and a second TNF inhibitor. In this simulation TNF inhibitors were considered together as a single group rather than as individual drugs due to the same mechanism of action and similar effectiveness ,13. DiffPatients were assumed to remain on any given treatment for at least 6 months. At the end of each six-month period, the model individually evaluated for each person whether he or she would continue treatment, discontinue it or die. The length of the time period was set to six months as this was the average time interval for routine care visits to rheumatologists in the (ROB-FIN).The simulation used regression models to predict outcomes and costs for each patient individually in each period . Each paPrimary data source for the model was the ROB-FIN and all assumptions were based on observed data unless otherwise mentioned. ROB-FIN has been described in previous publications ,18–20. IObserved ROB-FIN data from baseline visit as well as subsequent follow-up visits were used to construct the regression models later used to predict patients’ treatment response, utility and costs in the simulation. Independent variable selection for the regression models was based on Akaike Information Criteria although the choice of bDMARD therapy and its interactions with other variables were always included where appropriate. Missing data among disease activity parameters were imputed by multiple imputation whereas information on treatments and use of healthcare resources were considered complete. Patients lost to follow-up were treated as uninformative censoring while information on treatment discontinuations were utilized in the modeling.The model population were sampled with replacement among the patients included in ROB-FIN about to start their second bDMARD therapy thus preserving any potential correlation between the variables. The baseline variables included in the model comprised age, sex, weight, Body Mass Index (BMI), Health Assessment Questionnaire (HAQ), DAS28, time from diagnosis of rheumatic disease, Rheumatoid Factor (RF) status, and concomitant use of MTX, SSZ and HCQ along with the patients’ healthcare costs during the past 12 months. The patients´ weight, BMI and RF status were fixed at the baseline values.th line treatment options was assumed to be the same as averaged treatment response to all biological treatments in the model.Treatment effectiveness was defined as an achievement of at least American College of Rheumatology (ACR) 20% improvement, a moderate European League Against Rheumatism (EULAR) response, or a Disease Activity Score 28 (DAS28) value of less than 3.2 –16. The The patients’ underlying HAQ score was assumed to remain fixed at the baseline value. The change in HAQ score in comparison to baseline was modeled and subsequently predicted using a linear regression model. Any effect the treatment could have on these disease activity parameters was assumed to be temporary and re-evaluated in the next period.In addition to lack of efficacy, treatment might be discontinued due to adverse events and other reasons. The risk of discontinuation due to adverse events and other reasons besides lack of efficacy was observed to be 0.068 across all bDMARDs in average based on the available data in ROB-FIN. After every six-month time period 0.017, 0.010 and 0.009 per cent of MTX, SSZ and HCQ users discontinued the use of the said co-treatment, respectively.Mortality rate adjusted by age and sex for patient-level data was based on the life table in 2017 published by Statistics Finland . In thisQuality-adjusted life years (QALY) were calculated corresponding to EuroQol five dimensions questionnaire (EQ-5D-3L) utilities predicted from the HAQ scores utilizing a multinomial logistic regression model. The regression model was based on data from a survey including both HAQ and EQ-5D-3L conducted in Finland in 2009 .th line treatment option were considered to be same as the averaged costs of currently available biological treatments.Direct costs comprised drug costs, administration costs of infusions, costs of switching, outpatient and inpatient care, while indirect costs included early retirement due to RA and sick leave. Costs of drugs were based on the Finnish price list including the retail price without value added tax of drugs and the dose in the label . The cosPatients’ biannual healthcare and indirect costs were modeled using a linear regression model based on actual observed data and later predicted in the simulation using this model. Drugs in outpatient care were in 2019 euros and all other costs were converted to 2017 euros using the price indices of Statistics Finland .The model was stochastic in nature and the 1000 unique model runs with different seeds for random number generator quantified the variability in the results. Several subgroup and deterministic sensitivity analyses were carried out based on 300 model runs to explore uncertainty and heterogeneity of the model results. As ABA and TCZ can be administered either subcutaneously or intravenously, we evaluated the influence of the choice of the administration route on the results in the deterministic sensitivity analyses. Even though HAQ progression was associated with age and the number of biologic treatment in the base case analysis, we used annual HAQ progression rates of 0.03 and 0.06 in the sensitivity analyses . AlthougEthical approval for this study was granted by the Helsinki University Central Hospital ethical committee (73/13/03/00/2014). The study permit to use the patient records and cost data was granted by the Finnish National Institute for Health and Welfare (THL/1497/.5.05.00/2013), Finnish Population Registry (262/410/16) and the Social Insurance Institution of Finland (Kela 7/522/2016). Written informed patient consents were acquired from patients who had been included in ROB-FIN prior to the introduction of the electronic patient monitoring systems.At baseline, median age of patients included in the study was 56 years . Most ofLifetime drug costs without administration costs and costs of switching were the lowest for RTX, but when administration costs and costs of switching were included, drug costs were the lowest for TNF inhibitors . ABA hadTNF inhibitors, ABA, and TCZ had lower costs and higher QALYs than RTX, and therefore, they were dominant in comparison to RTX . TNF inhThe results regarding costs and QALYs from the subgroup and deterministic sensitivity analysis are presented in Based on our patient-level simulation model using real-world data from Finland, TNF inhibitors, ABA, and TCZ as a second-line biologic treatments for RA were dominant as compared with RTX. Even though a commonly referred threshold for cost-effectiveness has not been published in Finland, we have used the willingness to pay threshold of 40,600€, representing the Finland´s gross domestic product per capita in 2017 per QALY gained in this study.  AccordinConsistent with our results, previous studies showed similar effectiveness between bDMARDs in patients with RA failing on TNF inhibitor ,34, wherThe main advantage of this study was that real-world data on costs and effectiveness were based on registry data from the same population. Baseline characteristics of the model and population and the assumptions used in the model were mainly based on ROB-FIN data representing routine clinical practice. Compared to the previous studies mostly based on efficacy derived from RCTs, our simulation was based on observational data and therefore results are likely to be more generalizable to the Finnish healthcare setting . In addiOur model memorized changes in patient characteristics over time and enabled simulation based on individual patients’ history. The history of what had happened was an important aspect of the model because it affected the occurrence of future events, their consequences and valuations, and many other aspects of the simulation. The simulation followed up patients for their complete lifetime, because RA is a chronic disorder that progresses over time. As such, long-term consequences of any differences in disease progression, effect on life expectancy, or drug discontinuation rates were assessed. The first TNF inhibitor was not included in this model as it was assumed to be identical between the comparators and would not affect the results. Reasons for discontinuation of RTX treatment were severely underreported in ROB-FIN data and therefore, the risk of adverse events was assumed to be equal across all treatment regimens. Also, the discontinuation probabilities for non-biologic co-therapies were assumed to be similar between the users of different biologics.Because of high costs of original bDMARDs, interest has grown in biosimilars that are comparable to the reference medicinal product in terms of efficacy and safety. Many original biologic drugs have reached, or are approaching, patent expiry. This will lead to increasing development and use of biosimilar drugs in the future, offering considerable savings in comparison with the reference medicinal product. Price competition after patent expiry may also reduce the price of reference medicinal product, leading to remarkable cost savings. Biosimilars of ETN, IFX, and ADA were considered in this analysis. The price of RTX biosimilar used for the treatment of RA was not available, but we used price discount of 30% for RTX biosimilar in the sensitivity analysis based on the Finnish legislation concerning the confirming a reasonable wholesale price for medicinal products . HoweverA potential limitation of this study is related to the dosing interval of RTX. As compared with other biologics, RTX has a unique mode of action and long dosing interval. In maintenance treatment, RTX infusions were often administered on demand in Finland. Consequently, the patients treated with RTX came to visit the rheumatologist only after the effect of the previous infusion began to wear off, which is very likely to lead to an underestimation of the effectiveness of RTX in our data, and therefore, leading to overestimation of healthcare costs. Based on the registry data used in this study, RTX was administered every 7.98 months in maintenance treatment. Similar dosing interval of RTX was reported by Keystone et al , whereasOur patient-level simulation based on observational data showed that TNF inhibitors were associated with the lowest costs and highest QALYs, whereas RTX had the highest costs and lowest QALYs. TNF inhibitors, ABA, and TCZ were dominant in comparison to RTX. As TNF inhibitors had the lowest costs and highest QALYs, they were the most cost-effective treatment option.S1 FigACR = American College of Rheumatology response, bDMARD = biological disease-modifying anti-rheumatic drugs DAS28 = Disease Activity Score 28, EULAR = European League Against Rheumatism response, HAQ = Health Assessment Questionnaire, HCQ = hydroxychloroguine, MTX = methotrexate, SSZ = sulfasalazine.(TIF)Click here for additional data file.S1 Tableiv = intravenous, sc = subcutaneous.(DOCX)Click here for additional data file.S2 TableABA = abatacept, bDMARD = biological disease-modifying anti-rheumatic drug, EQ-5D-3L = EuroQol five dimensions questionnaire, ETN = etanercept, HAQ = Health Assessment Questionnaire, QALY = quality-adjusted life year, QoL = quality of life, RA = rheumatoid arthritis, TNF inhibitor = tumor necrosis factor inhibitor, TCZ = tocilizumab.(DOCX)Click here for additional data file.S3 TableABA = abatacept, bDMARD = biological disease-modifying anti-rheumatic drug, csDMARD, conventional synthetic disease modifying anti-rheumatic drug, ETN = etanercept, QALY = quality-adjusted life year, RA = rheumatoid arthritis, TNF inhibitor = tumor necrosis factor inhibitor, TCZ = tocilizumab.(DOCX)Click here for additional data file.


Hyalomma rufipes nymph collected from a whinchat (Saxicola rubetra) on the island of Ventotene in April 2017. Partial genome sequences suggest the virus originated in Africa. Detection of the genome of this virus in Italy confirms its potential dispersion through migratory birds.We detected Crimean-Congo hemorrhagic fever virus in a  Orthonairovirus within the Nairoviridae family, has a negative-sense tripartite RNA genome characterized by high genetic diversity. The sequences of the circulating strains cluster in 6 genotypes (I–VI) reflecting their geographic origin; worldwide distribution is the result of efficient dispersion through migratory birds, human travelers, and the trade and movement of livestock and wildlife (Cervus elaphus) and, 6 years later, in 2 autochthonous human cases in the same region of Spain (Crimean-Congo hemorrhagic fever virus (CCHFV) is a vectorborne virus responsible for severe illness in humans, whereas other mammals usually act as asymptomatic reservoirs. The virus is transmitted through tick bites or by direct contact with blood or body fluids of infected vertebrate hosts. CCHFV, an The role of birds in the potential spread of the virus was confirmed by CCHFV detection in ticks collected from migratory birds in Greece in 2009 (https://twitter.com/promega). We assayed purified nucleic acids for the presence of CCHFV RNA and to refine the morphologic identification of the immature ticks. We used the RealStar CCHFV RT-PCR Kit 1.0  to reveal viral RNA and conducted molecular tick species identification by sequencing 340 bp of the 12S rDNA sequence , a trans-Saharan migratory bird distributed across Europe and western Asia that winters mainly in central Africa.We conducted tick sampling during March–May 2017 on the island of Ventotene, where a ringing station has been operating since 1988 as part of the Small Islands Project, a large-scale and long-term effort to monitor spring migrations of birds across the central and western Mediterranean. We ringed 5,095 birds and checked ≈80% for ectoparasites. We collected 14 adults, 330 nymphs, and 276 larvae from 268 passerines belonging to 28 species; 18 species were trans-Saharan migrants. We stored ticks in 70% ethanol until morphologic identification and assignment to a genus or, whenever possible, a species (https://www.illumina.com) with a slightly modified sequence-independent single-primer amplification protocol. We cleaned and denovo assembled raw reads by using SPADES 3.11 (http://cab.spbu.ru/software/spades). We identified 18 partial but reliably assembled contigs (97%–99% identity) that could be definitely assigned to CCHFV. We aligned partial sequences from small (485 bp) and medium  segments with 13 virus sequences representative of the 6 circulating virus genotypes. We performed cladistics analysis by using MEGA7 (https://www.megasoftware.net). Whatever the genome segment involved, the cladograms revealed that the strain identified in Italy originated in Africa and had a close affinity with those strains segregated in genotype III (We further sequenced the sample by using the Illumina Next 500 platform (Illumina, type III  but exclH. rufipes. Adults of this species have been sporadically collected in Europe (H. marginatum ticks are autochthonous in many Mediterranean countries. H. marginatum and H. rufipes ticks are competent vectors for CCHFV (The origin of the introduction of this virus is further supported by the geographic distribution of the infected tick, identified as Although detection of virus genome does not necessarily imply the presence of live virus that is able to spread locally, our findings, consistent with the recent autochthonous cases in Spain, underscore the need to monitor any introduction and circulation of CCHFV in southwestern Europe. Such monitoring should focus on sites where migrants rest or nest and where a local population of competent ticks and their hosts interact. Raising awareness of possible outbreaks should also include specific surveillance and contingency plans focused on categories of persons and animals at elevated risk for CCHFV infection.


Infection was associated with a high incidence of symptomatic disease but no congenital syndrome. We report demographic, epidemiologic, and clinical findings for a prospective cohort of pregnant women during the initial phase of Zika virus introduction into Yucatan, Mexico. We monitored 115 pregnant women for signs of active or recent Zika virus infection. The estimated cumulative incidence of Zika virus infection was 0.31 and the ratio of symptomatic to asymptomatic cases was 1.7 (range 1.3–4.0 depending on age group). Exanthema was the most sensitive clinical sign but also the least specific. Conjunctival hyperemia, joint edema, and exanthema were the combination of signs that had the highest specificity but low sensitivity. We did not find evidence of vertical transmission or fetal anomalies, likely because of the low number of pregnant women tested. We also did not find evidence of congenital disease. Our findings emphasize the limited predictive value of clinical features in areas where Zika virus cocirculates with other flaviviruses. It was first recognized in Brazil during 2015 in association with an outbreak of exanthematic disease, which was quickly linked to neurologic and immunological complications and congenital malformations  residing in the cities of Merida, Ticul, and Progreso de Castro in Yucatan State , weekly doctor follow-up by text messages, and complete access to a telephone to report any clinical signs in pregnant women, their newborns, or any family contact. Fetal ultrasonography was performed at enrollment and every 3 months. At the first visit, a questionnaire was given to establish the clinical–epidemiologic profile. The pregnancy follow-up ended when the pregnancy was completed by delivery or fetal loss or the participant withdrew from the study. After initial clinical evaluation  for blood and urine samples as described .We compared clinical and epidemiologic variables between pregnant women who were infected with Zika virus during pregnancy and women who remained Zika virus–negative by RT-PCR by calculating odds ratios and testing for their significance by using the Fisher 2-sided exact test. We evaluated differences in head circumference between groups of babies born to Zika virus–positive and –negative mothers by using the Wilcoxon signed rank test. Differences in p-values A total of 115 pregnant women were included in the study: 66 from Merida, 45 from Ticul, and 4 from Progreso de Castro. One third were positive for Zika virus by RT-PCR of blood, urine, or both, at the initial evaluation (26 women) or during follow up (10 women). The cumulative incidence of Zika virus infection in the cohort was 0.31. The symptomatic to asymptomatic ratio among PCR-positive patients infected with Zika virus was 1.7 , and the highest proportion was in women 20–29 years of age . Of the In subsequent monthly testing, 11 (42%) blood samples remained positive and 2 urine samples that were negative in the first test became positive. In the third interval, 4 (15%) blood samples remained positive, and only 1 (3%) remained positive during the fourth interval. No urine sample was positive in 2 consecutive monthly controls. No intermittent urine virus shedding was detected. Seven (50%) of 14 women with virus shedding in urine had clinical symptoms at the time of virus detection. For 5 patients, the positive urine sample occurred at the same time as the positive blood sample, and for 2 patients, urine was positive after the blood sample showed a negative result. Of the 10 patients in whom infection developed during follow-up, 7 had paired blood–urine samples; 3 of those had positive blood and urine samples, 3 had only positive blood samples, and 1 had only a positive urine sample. Two patients were positive only for the urine sample, and 1 was positive only for the blood sample. For 1 patient, a urine sample negative at the time of detecting the infection became positive in the subsequent monthly control while the blood sample became negative. One patient had a blood sample that remained positive for >1 time interval.We obtained the distribution of the cases per epidemiologic week for Yucatan State and the national epidemiologic curve , panel AIf one considers the probability of acquiring Zika virus infection in relation to the moment of conception, those women who conceived during 2016 during epidemiologic weeks 13–40 had a statistically significant increased risk for acquiring the infection during pregnancy than for women who conceived during epidemiologic weeks 41–52 of 2016 and 1–12 of 2017 . For those patients who were positive at the time of enrollment, it was not possible to identify precisely when they became infected. In patients who were detected infected during follow-up, 0 became infected in the first trimester, 5 in the second trimester, and 5 in the third trimester. The average age of pregnant women in the study was 25 years, and we found no major differences in age distribution between Zika virus–positive and Zika virus–negative mothers . We alsoMore than half (64%) of the women had >1 sign or symptom compatible with acute infection . We founA total of 17 Zika virus–positive patients had conjunctival injection, which was present in only 3 Zika virus–negative patients. For these 17 patients, this infection was associated with exanthema, and for 7 patients, this infection was associated with joint edema. Six patients had only exanthema. For subjective but unusual symptoms, such as retro-orbital pain (9 patients) and pruritus (11 patients), we observed that all but 1 patient with retro-orbital pain also had conjunctival hyperemia, and all had exanthema. Of patients with pruritus, all had exanthema, 10 had conjunctival hyperemia, and 6 had joint edema. The most frequent clinical findings among Zika virus–positive women were exanthema, arthralgia, and conjunctival hyperemia. Headache, retro-orbital pain, joint edema, and pruritus were the most specific signs and symptoms, but these symptoms had low sensitivity . The proAt the time of this study, all pregnancies were complete. Of these pregnancies, 3% were preterm, 2 for Zika virus–negative mothers and 1 for a Zika virus–positive mother . Two fetAedes mosquitoborne diseases for many decades. After introduction of Zika virus, routine measures to avoid vector propagation , as well as personal protection against mosquito bites, were implemented by the regional government. Such interventions failed to contain Zika virus propagation , it is expected that the specificity, positive predictive value, and diagnostic accuracy will decrease in relation to the differential diagnosis. The absence of fever, as well as the presence of exanthema, with or without other signs and symptoms, should alert the primary health system to suspect Zika virus infection in pregnant women at any time during the evolution of their pregnancy.The proportion of symptomatic cases observed in our cohort can be an expression of the bias in our enrollment strategy because patients referred by physicians from primary care facilities are more likely to be positive for Zika virus infection and symptomatic. During the study period, active circulation of chikungunya virus and dengue virus was reported in the area. Because our samples were not tested for these virus infections, the chance of a co-infection cannot be ruled out. This finding represents a major limitation when we analyzed the clinical approach to orient diagnosis.–,,,,–The risk for congenital disease among Zika virus–infected pregnant women has been estimated to be 1%–13% (–,There is recognized discordance in Zika virus detection on concurrent blood and urine samples (Aedes aegypti mosquitoes at locations other than their home (,,Women who work outside had an increased risk for contracting the infection, potentially reflecting differential exposure to Although knowledge of clinical manifestations, natural history, and epidemiology of Zika virus in the Americas is incipient, the clinical–epidemiologic scenario involving severe congenital disease that was manifested initially in Brazil has not been observed at the same magnitude in other countries. Our prospective study of a cohort of pregnant women in Yucatan, Mexico, showed the value of active surveillance in early detection of infections and point to the limited predictive value of symptoms in areas where Zika virus cocirculates with other flaviviruses. In our study of 115 pregnant women with active or recent Zika virus infections, we found no evidence of congenital Zika virus disease.


Aedes aegypti vector. Phylogeographic inference of temporal and spatial virus relationships showed that this variant emerged within the East, Central, and South African lineage between 2005 and 2008, most probably in India. It was also in India where the first large outbreak caused by this virus appeared, in New Delhi, 2010. More importantly, our results also showed that this variant is no longer contained to India. We found it present in several major outbreaks, including the 2016 outbreaks in Pakistan and Kenya, and the 2017 outbreak in Bangladesh. Thus, this variant may have a capability of driving large CHIKV outbreaks in different regions of the world. Our results point to the importance of continued genomic-based surveillance and prompt urgent vector competence studies to assess the level of vector susceptibility and virus transmission, and the impact this might have on this variant’s epidemic potential and global spread.In 2016, a chikungunya virus (CHIKV) outbreak was reported in Mandera, Kenya. This was the first major CHIKV outbreak in the country since the global reemergence of this virus in Kenya in 2004. We collected samples and sequenced viral genomes from this outbreak. All Kenyan genomes contained two mutations, E1:K211E and E2:V264A, recently reported to have an association with increased infectivity, dissemination, and transmission in the  During this time in Somalia, outbreaks of CHIKV were occurring in the neighboring Bula Hawa region, originating from Mogadishu. In Mandera town, 1,792 cases were detected, and an estimated 50% of the health work force was affected by this virus. A cross-border joint response was coordinated between Kenya and Somalia to control the outbreak.2 The disease also spread to the coastal city of Mombasa by the end of 2004, and further to the Comoros and La Réunion islands, causing large outbreaks in 2005–2006. On La Réunion island, unusual clinical complications were reported in association with CHIKV infection, and viral isolate sequences revealed the presence of an alanine-to-valine mutation in the E1 glycoprotein at position 226 (E1:A226V).3 This specific mutation was shown to confer significant increase in CHIKV infectivity for the Aedes albopictus vector, which was also the dominant mosquito species suspected to be responsible for the transmission of CHIKV on the island of La Réunion.5 Since then, the E1:A226V mutation has been observed in many of the genomes in the CHIKV lineage, spreading in the East, Central, and South African region (ECSA lineage), and has been shown to have emerged through convergent evolution in at least four different occasions.6The previous large CHIKV outbreak in Kenya occurred in Lamu Island in 2004, with an estimated 75% of the population infected.Ae. albopictus vector prompted additional studies on the genetic plasticity of this virus, looking for additional biomarkers associated with virus transmission capacity, fitness, and pathogenicity.8 Mutations with the ability to enhance infection in this vector are of increased importance, as Ae. albopictus is rapidly expanding throughout the world.9 The Ae. albopictus mosquito is believed to have originated in Asia and is today most commonly found in east Asia. Aedes albopictus is also common in some parts of South and Southeast Asia, India, and Africa, and it has shown increased spread to regions with lower temperatures, such as southern Europe, southern Brazil, northern China, and the northern United States.9 In Europe, this vector has been associated with autochthonous transmission of CHIKV.10The remarkable emergence and spread of CHIKV adaptation to the Ae. albopictus, most of the CHIKV transmissions in the world are associated with Aedes aegypti. Aedes aegypti is a container-breeding, domesticated mosquito mainly found in urban areas and feeding largely on humans during early and late daytime hours. Aedes aegypti originated from the ancestral zoophilic Ae. aegypti formosus native to Africa. Aedes aegypti now is most common in tropical and subtropical regions, such as Africa, India, Southeast Asia, and Australia.9 Recently, two mutations, E1:K211E and E2:V264A, have been reported in the CHIKV to be associated with increased fitness to Ae. aegypti vectors.11 These two mutations, in the background of the wild-type E1:226A, are believed to increase virus infectivity, dissemination, and transmission in Ae. aegypti, with no impact on virus fitness for the Ae. albopictus vector.11 E1:K211E was first observed in genomes sampled in 2006 from the Kerala and Puducherry regions, India, and simultaneous presence of both mutations was first observed in 2009–2010 in Tamil Nadu and Andhra Pradesh, India.13Although some of the more recent CHIKV outbreaks have been transmitted by Ae. aegypti, and vector competence studies using local vector populations have shown it is capable of transmitting the virus in this region.14 Given the recent large outbreak of CHIKV in the rural setting of Mandera County, Kenya, we analyzed CHIKV genomes sequenced from this outbreak for the presence of adaptive mutations associated with both Ae. albopictus and Ae. aegypti. Along with estimating origins and the time of emergence of a variant that carried two of these previously reported mutations, we also estimated time and origins of the Mandera CHIKV outbreak. Our results indicate the spread of a CHIKV that carries mutations previously associated with increased fitness for Ae. aegypti in Kenya. We also show that this variant is now connected with new large outbreaks in other regions of the world.In Kenya, the predominant CHIKV vector is The study was carried out on a protocol approved by the Walter Reed Army Institute of Research (#2189) and Kenya Medical Research Institute’s (KEMRI) Scientific and Ethics Review Unit (#3035) as an overarching protocol guiding investigation and reporting of arbovirus/hemorrhagic fever outbreaks in Kenya. Because the blood samples were collected from an outbreak and no human data were collected, this was deemed to be a nonhuman research study and no consent was required. The protocol was approved for additional analysis of outbreak samples and for publication of results.Supplemental Appendix 1. A subset of chikungunya-confirmed positive samples from acutely ill patients were further subjected to high throughput full-genome sequencing at the KEMRI laboratories. The prepared complementary DNA (cDNA) was quantified using Qubit 3.0 fluorimeter and the dsDNA HS Assay Kit . The cDNA was fragmented enzymatically and tagged using the Nextera XT DNA Library kit . Each sample was assigned a unique barcode sequence using the sequence libraries prepared with the Nextera-XT kit (Illumina), following the protocols and reagents supplied by Illumina, and sequenced on the MiSeq platform according to the manufacturer’s instructions (Illumina). Ten full genomes and five partial genomes were assembled by combining both de novo and reference mapping assemblies. De novo assemblies were performed using Abyss and Trinity, and reference mapping was performed using ngs_mapper.17 Sequences have been submitted to the GenBank under accession numbers MH423797–MH423811.From May 2016, following reports of widespread incidence of febrile illness with severe joint pains in Mandera  and its environs, samples were collected from suspected cases of all ages and gender, using standard practices by the KMoH staff. The case definition used was “any patient presenting with sudden onset of fever > 38.5°C, with severe joint/muscle pains and headache within the last 3–5 days within Mandera County, the person should either be a resident of or visiting Mandera.” Chikungunya infection was confirmed by CHIKV-specific reverse transcription polymerase chain reaction (RT-PCR) and partial genome Sanger sequencing at the KEMRI laboratories. Vero cell culture inoculations were performed on the samples to obtain isolates for in-depth studies. For method details on infection confirmation, virus isolation, and Sanger sequencing, see 18 The curation consisted of inference of a neighbor joining tree followed by linear regression of root to tip distances, given the sampling time. Genomes with too much or too little divergence as would be expected, given their root to tip distance and collection date, were considered as outliers and were removed from the dataset. In addition, all genomes with long stretches of Ns, genomes without collection date, and genomes without sampling location were also removed from the reference dataset. To determine the CHIKV lineage of the assembled genomes from Mandera, Kenya, these sequences were aligned to the constructed reference dataset (n = 466), using MEGAv7.19 This large dataset was also scanned for the presence of recombination using Recombination Detection Program version 4, with a minimum of three methods with significant (P < 0.05) recombination signal required to call a genome recombinant.20 Maximum likelihood (ML) trees were inferred using PhyML and RaxML, with general time reversible + gamma + invariant sites (GTR+G+I) (GTR+G for RaxML) model of evolution, as determined by jModelTest2.23 Node confidence values were derived by approximate likelihood-ratio test (aLRT) (PhyML) and bootstrap of 500 (RaxML). After evaluating the temporal structure using TempEst, the trees were used for an informed down-sampling of the dataset. For this analysis, only ECSA lineage genomes were kept as reference, identical genomes from the same location and sampling year were removed, as were genomes from a phylogenetically distant sub-clade only found to spread in Asia. The down-sampled dataset  was analyzed using the BEAST package v1.8.3, with GTR+G+I model of substitution, bayesian skyline plot, relaxed lognormal molecular clock, and asymmetrical trait (location) distribution.24 BEAST was run for 800 million generations, with subsampling every 80,000 and 10% burn-in. The maximum clade credibility (MCC) tree was summarized using TreeAnnotator. Ancestral states were reconstructed using ML, and all amino acid mutations at each node for the trees were mapped using TreeTime.25 Because viral genomes from several additional recent CHIKV outbreaks form India and Bangladesh were only sequenced in their E1 gene, we also inferred the ML tree of the partial E1 region including these sequences, using PhyML and TN93+G+I model of evolution, as determined by jModelTest2.All full-genome CHIKV genomes were downloaded from the GenBank and curated in TempEst.26 The presence of site-specific selection was determined by a likelihood approach using FEL and by Bayesian method FUBAR with a probability level threshold of 0.95.28 Episodic selection was investigated using MEME, and any selection acting on tree branches was determined by aBSREL.30 Selection analyses were performed on the large full-genome dataset used for the ML trees (ECSA only) and the small dataset used for BEAST analyses. FUBAR was performed on separated structural and nonstructural genes from both datasets because of their large sizes. Positive selection on a site was determined present if the site was found to be under selection by all three methods that determine site-specific selective pressures . Structure figures of CHIKV E1E2 (Protein Data Bank ID:3J2W31) were visualized and prepared using PyMOL .Selection analyses were performed using the HyPhy package.A total of 15 samples from the CHIKV outbreak in Mandera, Kenya, collected in May and June 2016, were sequenced. Ten of the sequenced samples produced full CHIKV genomes and five of the samples had partial genomes. No recombination was detected in the genomes. Maximum likelihood trees inferred by PhyML and RaxML were concordant and showed that all Kenyan genomes belonged to the ECSA lineage of CHIKV. Consistently with their outbreak origins, the genomes from Mandera clustered in a monophyletic clade defined by very short branches, indicating limited genetic diversity . The KenAe. aegypti vectors from India. Further investigation of amino acids from the genomes surrounding the Kenyan samples in both the ML and MCC phylogenetic trees revealed a cluster of genomes, sampled from various regions of the world , also containing the two Ae. aegypti fitness-associated mutations in the background of E1:226A . Although the node support for the ancestor of viruses with and without the two mutations was low, probably because of lack of sampling that would reveal the exact evolutionary relationships, it was estimated to have existed in India because of well-supported ancestral nodes preceding this one. Thus, these results indicated that the variant with possible increased fitness to Ae. aegypti most probably arose in India sometime between 2005 and 2008. The background E1:226A amino acid , indicating that the virus with dual E1:K211E and E2:V264A mutations emerged by the end of 2008 in this area of the world. The cluster shared a common ancestor with genomes from India, which did not contain the two suggested d strain . Rather,Ae. aegypti fitness-associated mutations , and MEME (P < 0.05) in all tested datasets. Position E2:264 did not show any evidence of positive selection. No branch-specific selection was detected by aBSREL. Positions found to be under positive selection by all methods are listed in To investigate whether the E1:K211E and E2:V264A mutations appeared because of selective pressure and adaptation of the virus, we performed several tests for the presence of positive selection, on both the large CHIKV dataset used for the ML trees and on the smaller dataset used for BEAST analyses. Significant presence of positive selection in position E1:K211 was detected by FUBAR (probability > 0.99), FEL .39 Shortly thereafter, in 2007, a variant carrying the Ae. albopictus–adaptive E1:226V mutation was recorded in the province of Kerala.40 Both variants were found circulating in the following years in the country, between 2007 and 2013.42Our results confirm previous findings that reemerging CHIKV reached Kenya in mid-2002.Ae. albopictus and Ae. aegypti are prevalent vectors, and the presence of the recently described Ae. aegypti fitness-enhancing mutation E1:K211E was first observed in the Kerala–Puducherry CHIKV outbreak of 2006.12 Shortly thereafter, E1:K211E was accompanied by the second Ae. aegypti fitness-associated mutation, E2:V264A. This novel dual mutation variant was first observed in Kerala in 2009; however, the viruses from the Kerala–Puducherry CHIKV outbreak of 2006 were not sequenced in the E2 region.13 These results support our estimate that the Ae. aegypti variant carrying the E1:K211E and E2:V264A mutations probably arose in India sometime between 2005 and 2008. We show that this variant evolved directly from the E1:226A wild-type virus, and not from the E1:A226V virus with increased transmission efficiency by Ae. albopictus. In fact, the E1:K211E and E2:V264A mutations have not yet been detected in the naturally occurring Ae. albopictus–adapted virus. However, it is worth investigating whether the whole reemerging ECSA genotype virus has other amino acid residues that make it generally more fit for spread by Ae. albopictus.43 In that case, the two reported Ae. aegypti fitness mutations might represent compensatory adaptation of ECSA CHIKV, selected by reintroduction of an Ae. albopictus–adapted virus into regions infested with Ae. aegypti.In India, both 44 Following this, CHIKV continued to circulate in discrete regions throughout India, and in 2016, the dual mutation variant appeared in another large outbreak in New Delhi.45 Simultaneously, it was also driving the outbreaks in Pakistan and Kenya.46 The Kenyan outbreak was estimated to have originated from a virus imported by 2015 and was most closely related to genomes from India. It is important to note that the implications of spread from India to Kenya are limited by sampling gaps, and India might not have been the direct source of the Kenyan outbreak. Indeed, the connection between the Somalian and Kenyan outbreaks indicates that the virus was most probably introduced from Somalia and, thus, that this variant is most likely spreading in African countries other than Kenya. Further sampling and sequencing in this region will reveal whether this is the case, or if the strong social, cultural, and human movement ties between Kenya and India aided in the dispersion of the virus between the two continents. Importantly, however, our results indicate that the variant carrying the two mutations with possible increased fitness to Ae. aegypti is no longer confined to India, but is spreading and associating with large outbreaks in other countries and other continents of the world. Interestingly, all recent CHIKV ECSA lineage genomes from the Ae. aegypti prevalent regions of the Indian Ocean sub-lineage contain the two Ae. aegypti fitness mutations. More sampling and surveillance are needed to determine whether the emergence of this new variant can thus lead to the replacement of the endemic wild-type virus.Our analyses suggest that the first indication of the E1:K211E and E2:V264A variant’s association with major outbreaks comes from India, where it caused an outbreak in New Delhi in 2010.47 No publicly available genomes exist from this event as of yet, but given the proximity of the Ae. aegypti–associated dual mutation virus outbreak of 2016, and given this variant’s association with large outbreaks, it is very possible that it is also responsible for this recent occurrence in Mombasa. Furthermore, other simultaneous CHIKV outbreaks have been reported in proximate regions to both Kenya and India, the 2016 outbreak in Mozambique and the 2017 outbreak in Dhaka, Bangladesh.49 The virus from the Dhaka outbreak contained the K211E mutation, and despite the lack of E2 sequencing, our analyses of the E1 region placed it within the Ae. aegypti dual mutation cluster. Thus, these results suggested that this outbreak was also caused by the variant with mutations previously associated with increased CHIKV fitness for Ae. aegypti.49 Sequencing and analyzing complete viral genomes from these and other countries will aid in the tracking of this variant’s spread, and it will provide insight into its possible replacement of the wild type in the Ae. aegypti–prevalent regions of the world.After the 2016 Kenyan outbreak resolved, a new CHIKV outbreak started in the coastal city of Mombasa in 2017–2018, with a large proportion of cases (70%) reporting severe joint pain and high fever.50 Viral genetics, and the appearance of adaptive vector mutations, has previously been shown to play a role for the vector competence and spread of CHIKV by Ae. albopictus.4 The genetic impact of the recently described E1:K211E and E2:V264A mutations for the spread of CHIKV by Ae. aegypti is still not very well known. Our structural modeling suggests that these two mutations introduce changes in charge and hydrophobicity of the CHIKV envelope surface proteins. Previous studies in chikungunya and other alphaviruses indicate that changes in the E1/E2 proteins affect pH sensitivity and can have dramatic effects on structure and virus production levels.53 This phenomenon is seen with surface viral glycoproteins from other viruses as well, including HIV-1 and respiratory syncytial virus, where variants with reduced conformational flexibility have improved expression.55 Vector competence has been shown to vary greatly between different populations of Aedes mosquitos, implying importance of vector genetics, in addition to changes in important viral surface protein residues, in driving the spread of CHIKV.57 Thus, the possible impact of E1/E2 structural changes by the E1:K211E and E2:V264A mutations on virus infectivity, dissemination, and transmission by Ae. aegypti in different parts of the world could differ, and should be examined further. However, the presence of positive selection on E1:K211E, coupled with the increased spread of E1:K211E and E2:V264A CHIKV variant throughout the world, highlights the importance and the urgency of such studies. The possible level of increased susceptibility of these vectors to CHIKV infection may provide information for implementation of additional or alternate vector-control strategies.The epidemic potential of a vector-borne agent depends on several interconnected intrinsic and extrinsic factors, such as viral and vector genetics, vector and host competence, vector abundance, temperature, and rainfall.Ae. aegypti, is capable of rapid spread in the regions of high Ae. aegypti presence. It has been associated with several recent large CHIKV outbreaks in Africa and Asia, showing capacity for global spread. Our study highlights the importance of further sampling, genomic surveillance, and vector competence studies to promptly assess the epidemic potential of this CHIKV variant.In conclusion, we show that the CHIKV variant, carrying the E1:K211E and E2:A264V mutations previously associated with increased fitness to Supplemental appendix


Major neutralization sites of HPV6 were mapped on the pseudovirus cryo-EM structuremAb 15F7 binds HPV6 capsid with a novel top-rim binding modality and confers a post-attachment neutralizationmAb 17D5 binds capsid in top-centre manner but unexpectedly prevents virus from attachment to cell surfaceHuman papillomavirus type 6 (HPV6) is the major etiologic agent of genital warts and recurrent respiratory papillomatosis. Although the commercial HPV vaccines cover HPV6, the neutralization sites and mode for HPV6 are poorly understood. Here, we identify the HPV6 neutralization sites and discriminate the inhibition of virus attachment and entry by three potent neutralizing antibodies (nAbs), 5D3, 17D5, and 15F7. Mutagenesis assays showed that these nAbs predominantly target surface loops BC, DE, and FG of HPV6 L1. Cryo-EM structures of the HPV6 pseudovirus (PsV) and its immune complexes revealed three distinct binding modalities – full-occupation-bound to capsid, top-center-bound-, and top-rim-bound to pentamers – and illustrated a structural atlas for three classes of antibody-bound footprints that are located at center-distal ring, center, and center-proximal ring of pentamer surface for 5D3, 17D5, and 15F7, respectively. Two modes of neutralization were identified: mAb 5D3 and 17D5 block HPV PsV from attaching to the extracellular matrix (ECM) and the cell surface, whereas 15F7 allows PsV attachment but prohibits PsV from entering the cell. These findings highlight three neutralization sites of HPV6 L1 and outline two antibody-mediated neutralization mechanisms against HPV6, which will be relevant for HPV virology and antiviral inhibitor design. Human papillomavirus (HPV) infection is a leading cause of genital warts and cervical cancer . More thThe current vaccines on the market were designed based on the mode of self-assembly of L1 to form virus-like particles (VLPs). L1 can interact with cellular receptors, such as heparan sulfate proteoglycans (HSPGs) or laminin-332 on the cell surface to drive primary viral attachment to host cells . The HPVTo establish infection, HPV undergoes a complicated process that includes receptor binding, a post-attachment conformational change, furin cleavage, and internalization . HSPGs, Virus neutralization is defined as an abrogation of virus infection through antibody-virus associations. Previous studies have highlighted the different neutralization mechanisms mediated by HPV nAbs . AntibodIn this study, we tested three potent neutralizing antibodies against HPV6 using epitope mapping and structural analysis. Through homologous loop-swapping of the L1 protein between HPV6 and HPV16, we identified the BC, DE, and FG loops as the key surface loops responsible for type-specific antibody recognition. To verify the variability in binding, we constructed cryo-EM structures of HPV6 PsV complexed with Fabs and unveiled the epitope locations targeted by these nAbs. Using immunofluorescence, we identified three patterns of binding and overall two modes of neutralization. Overall, these findings offer insight into the mechanism(s) of low-risk HPV neutralization mediated by antibodies.HaCaT cells and 293FT cells were cultivated in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum (FBS). These cells were used for HPV6 PsV production (293FT cells), immunofluorescence microscopy (HaCaT cells), and the PsV neutralization assay (293FT cells).280nm before dilution in PBS and storage in a final concentration of 1.0 mg/ml at −20°C.BALB/c mice for hybridoma preparation were immunized subcutaneously three times with HPV6 VLPs absorbed with aluminum adjuvant as described in our previous study . A set o50) values, measured using the Reed-Muench method . In. In24]. Overall, we comprehensively characterized and structurally mapped the major neutralization epitopes of HPV6, our results suggest two neutralization mechanisms for HPV6 nAbs: most nAbs neutralize HPV6 by interfering with the attachment phase of virus infection, whereas some nAbs (e.g. 15F7) affect virus penetration and entry into the host cell; albeit, in such cases, the Ab-bound virus still attaches to the ECM and/or the cell surface . It shouClick here for additional data file.


This general problem is accounted for by the theoretical framework of evidence accumulation models, which capture individual differences in (at least) 2 distinct ways. Differences affecting accumulation rate produce positive correlation. But this is cancelled out if individuals also differ in response threshold, which produces negative correlations. In the models, subtractions between conditions do not isolate processing costs from caution. To demonstrate the explanatory power of synthesizing the traditional subtraction method within a broader decision model framework, we confirm 2 predictions with new data. Thus, using error costs or RT costs is more than a pragmatic choice; the decision carries theoretical consequence that can be understood through the accumulation model framework.The underpinning assumption of much research on cognitive individual differences (or group differences) is that task performance indexes cognitive ability in that domain. In many tasks performance is measured by differences (costs) between conditions, which are widely assumed to index a psychological process of interest rather than extraneous factors such as speed–accuracy trade-offs . Relatedly, reaction time (RT) costs or error costs are interpreted similarly and used interchangeably in the literature. All of this assumes a strong correlation between RT-costs and error-costs from the same psychological effect. We conducted a meta-analysis to test this, with 114 effects across a range of well-known tasks. Counterintuitively, we found a general pattern of weak, and often no, association between RT and error costs (mean  Our meta-analysis reveals that RT costs and error costs from the same psychological effects do not correlate, contrary to widespread assumption. This is explained if people vary in both caution and cognitive abilities. We demonstrate this by simulating data from 4 models in the broad family of evidence accumulation models. Individual differences in behavior should not be assumed to solely reflect individual differences in ability in a cognitive domain. No man can be acquainted with all of psychology today. . . [There is] plentiful evidence that psychology is going places. But Whither? . . . The personality, social and child psychologists went one way; the perception and learning psychologists went the other; and the country between turned into desert. (pp. 671–673)Sixty years ago prominent psychologists worried about an inevitable parting of ways between two disciplines of psychology, as eloquently highlighted by The different sides across the desert followed different approaches: on one side, differences between individuals were the very focus of study, while on the experimental side “individual variation is cast into that outer darkness known as ‘error variance’” , p. 674.One of the cornerstones of experimental psychology is the subtraction method , in whicThese kinds of performance costs typically occur in both reaction times (RT) and error rates, and terms such as “Stroop effect,” “implicit association effect,” “attentional cost,” or “switch cost” can refer to either RT costs or error costs interchangeably. As such researchers tend to assume that both reflect the same underlying mechanisms, and whether to use error or RT costs is seen as a pragmatic choice rather than one with theoretical consequence. For some paradigms it is traditional to focus on one measure, for example, RT costs in task switching or the IAT, but it is nevertheless expected that effects of interest will also be reflected in error rates .Supplementary Material A). Similarly, in a meta-analysis of 12 studies examining flanker and Simon effects in children with attention-deficit/hyperactivity disorder  has been so successful and ubiquitous in experimental research, that when moving to study individual differences, it is rarely questioned whether individual differences in RT costs or error costs primarily reflect processing ability, or whether they might in fact reflect other factors such as differences in strategy. When not using costs, but rather absolute accuracy or RT in tasks, it is appreciated that strategy, cautiousness, and other factors may contaminate individual differences. For example, in numeracy tasks it has been illustrated how absent correlations between tasks can be explained by dissociating information processing and caution using a quantitative model . For mosHowever, across the literature are many hints implying all is not well with the assumptions underlying correlational research with cognitive performance costs. The contamination of RT costs by processes not specific to the domain of interest has been discussed previously . Miller In order to obtain a more complete representation of behavioral performance, some authors have used composite measures of RT and accuracy , 1983. HIn Part 1 of this article, we perform a meta-analysis to test the widespread theoretical assumption underpinning the use of performance costs as indexes of ability in specific cognitive domains. This assumption predicts a positive correlation between performance measures—error costs and RT costs—within the same task. This assumed correlation supplies an implicit justification to choose either measure on pragmatic grounds without theoretical consequence (or to combine them into a single metric). We test the correlation for 114 experimental effects taken from 43 different studies, encompassing 13 prominent paradigms across experimental psychology, using both new data and reanalysis of previously published data from many labs . To anticipate, we find little or no correlation in the majority of cases; for example, an individual’s Stroop effect measured by errors is clearly not interchangeable with their Stroop effect measured by RT, and likewise for nearly all the other common effects we analyze.Should we be alarmed by this? From most theoretical standpoints, this general pattern seems surprising and potentially undermines the conclusions of many studies, reviews and meta-analyses. But from one family of theoretical perspectives is it not alarming, as we illustrate in Part 2 of the article using four different models  drawn asThe models capture individual differences in (at least) two distinct ways. The first corresponds to differences in accumulation rates (processing or selection efficiency). When individuals vary only in their selection efficiency, this produces a positive correlation between RT costs and error costs, as commonly assumed. The second corresponds to response threshold (caution), where differences would produce a negative correlation between error costs and RT costs. Note that because we are dealing with costs calculated through subtraction, not absolute RT and error rates, this negative correlation is not a simple speed–accuracy trade-off. However, a key theoretical consequence of these models is that threshold and processing efficiency interact, and a subtraction between conditions does not control for caution differences between individuals.If participants vary in both accumulation rate (selection efficiency) and threshold, then an overall correlation between error and RT costs is not expected, despite both being outcomes of the same decision and control mechanisms. We illustrate that this is not a feature of any specific model, but a property the family shares. Thus, the framework of accumulator models appears fruitful for understanding individual differences in performance on choice decision tasks.In Part 3 of the article we test with new data two predictions arising from the modeling framework. First, reducing variance in response caution by emphasizing speed cf.  should mWe identified a list of widely used and cited speeded choice tasks for which performance can be measured with either RT costs or error costs , and for which we were able to access at least one suitable dataset from open science resources, our own studies, or from colleagues.https://osf.io/) for each task by name, as well as searching Google for “(task name) dataset.” We required data sets to have an associated article or preprint, in order to identify necessary study information. (c) A further 12 correlations from eight different tasks were collected in our own lab. Six of these correlations come from a previously published article  Data from five studies was made available to us by colleagues. See Supplementary Material C for additional details on how the data were extracted. See We used the following strategies to search for relevant literature: (a) PsycINFO and Web of Science. Our search terms were any of the task names: “flanker,” “Stroop,” “Simon,” “antisaccade,” “remote distractor,” “snarc,” “Navon,” “task-switch,” “implicit association test,” “attention network test,” and “lexical decision;” in combination with any of the terms: “RT cost,” “RT cost,” “error cost,” “accuracy cost,” “latency cost,” and “cost.” We supplemented this search by manually searching Google and Google Scholar with the same terms, and scanning the reference lists of eligible articles. We included unpublished research dissertations in our search. (b) Then, we searched for additional data sets from which RT costs and error costs could be calculated. We searched within the Open Science Framework , and it is not always clear what constitutes a neutral stimulus cf. . We did Supplementary Table C1 for additional information for each study. Only five of these articles discussed the relationship between RT costs and error costs in any way, and we outline the content of such discussion in the Discussion section of Part 1 and the General Discussion section.Where the raw trial by trial data were available , we applied a common preprocessing and outlier removal pipeline . Where we only obtained summary data for each participant (k = 25), the calculation of individual’s RT costs and error costs reflect the authors’ original outlier removal strategy. From each dataset, we extracted the sample size and trial number, which are reported in Where studies involved data collection over multiple sessions, we collapsed across sessions if possible. In some cases e.g.,  some parr and Spearman’s rho) were calculated for the correlation between RT costs and error costs for each effect. Initially, we used Pearson’s r estimates in the meta-analysis because they were more common in existing reports. We then reran the analysis using Spearman’s rho estimates to minimize the impact of outliers in some data sets. In the conventional interpretation of these effect sizes, 0.1 is considered small, 0.3 is a medium effect size, and 0.5 is a large effect size . I2 values of 25%, 50%, and 75% are interpreted as low, moderate, and high levels of heterogeneity, respectively  indicated that overall there was a small correlation between RT costs and error costs , with a very high degree of between study heterogeneity (I2 = 99.9%). As can be seen in r values ranged between −.45 and .78, with 79% of the absolute values falling below what is typically considered to be a moderate effect size . Clearly individual differences in RT costs and error costs are not behaving as expected if they were interchangeable measures of the same cognitive processes.The meta-analysis using Pearson’s size .3; . Rerunnip = .009). Inspection of absence (the middle- and lower-right section of the plot is relatively sparse). This is also influenced by the lexical-decision task data sets, which had relatively large positive correlations and sample sizes. The trim and fill analysis simulated studies with positive correlations to correct for this asymmetry, though the corrected estimate was still small (r = .25).To assess and control for potential biases, we conducted Egger’s test , followeNone of the published data sets we included were collected for the purpose of examining the correlation between RT costs and error costs, and it is unlikely that the size of that correlation formed any part of the publication decision process or the choice to make the data sets available (the correlation was not reported in most cases). Publication decisions in some studies would have depended on within-subject effects and hence favored low between-participants variance . However, examination of r = .15 . A high degree of heterogeneity was again observed (I2 = 99.9%), though trial number no longer significantly predicted effect size .Metaregression analysis indicated that the number of trials administered significantly predicted the size of the effect, with more trials associated with larger effects . Notably, the two moderate negative correlations we observed in the flanker task were in Parkinson’s patients  and Stroop (r = .46) tasks in The flanker task showed a wide range of correlations across 17 data sets  robust effect on both metrics at a group level? One possibility is that the use of difference scores obscures a “true” underlying relationship. For statistical reasons, difference scores typically show less reliable individual differences than their component measures, and this will attenuate the correlations between them and other variables . Previouexpected . The samr ∼ .5. Similar levels of reliability are reported for other tasks .response caution” .The potential contribution of caution to differences in absolute RT and accuracy  is one oTo illustrate the effects of individual variation in response caution and selection efficiency, we simulated the patterns of RT costs and error costs produced by the DDM, the LBA, the DMC, and ALIGATER. Each simulation consisted of 50,000 trials per condition. The ranges of parameters used in our simulations were informed by previous simulations using these models where available, as well as our own simulations. For brevity, we use the terms “congruent” and “incongruent” to refer to all tasks, thus encompassing congruent/baseline/target/valid and incongruent/alternate/distractor/invalid conditions respectively. The general results of our simulations are not dependent on the choice of either a congruent or neutral condition as a baseline cf. , as the In this model, basic congruency effects are captured by differences between mean drift rates for congruent and incongruent trials . To simulate individual differences in caution, we let boundary separation (a) vary between 0.07 and 0.16 in increments of 0.015. To simulate individual differences in selection efficiency, mean drift rates for incongruent trials varied from 0.1 to 0.4 in increments of 0.05 . Parameters describing between-trial variability in drift rates (η), mean start point bias (z), and within trial-noise (s) were held constant across simulations see  in MatlaIn this model, congruency effects are captured by differences between mean drift rates on congruent and incongruent trials . To simulate individual differences in caution, we varied the response boundary parameter (b) from 250 to 550 in increments of 50. To simulate individual differences in response selection, mean drift rates for incongruent trials varied between 0.95 and 0.65 in increments of 0.05 . The drift rates for the incorrect response accumulators were fixed to 1 minus the drift rate for the correct response. Parameters describing start point variability (A) and between trial variability in drift rates (s) were held constant for all simulations see . The LBAIn this model, congruency effects are captured by the amplitude of automatic activation . To simulate differences in caution, we varied boundary separation (b) between 35 and 65 in increments of 5. To simulate differences in selection efficiency, we varied the amplitude of automatic activation between 10 and 28 in increments of three. Parameters describing the drift rate for the controlled process (μc), time to peak automatic activation (τ), the shape parameter of the starting point distribution (α), the shape parameter of the automatic activation function (a), and within-trial noise (σ) were fixed for all simulations see . The DMCIendo) from 0.01 to 0.022 in increments of .002. Parameters describing the mean drift rates , between trial variability in rise rates (η), reactive inhibition delay (δendo), mutual inhibition strength (w), and mutual inhibition delay (δw) were fixed across all simulations . This would create correlation between measures. In other words, general slowing can “look like” domain specific deficits in traditional measures.This also means that traditional analyses of RT costs are difficult to interpret when comparing populations with different mean RTs see also . But if Nondecision time reflects the total duration of perceptual and motor processes, which often represent a sizable proportion of RTs. Individual differences in nondecision time are therefore highly relevant to attempts to link individual differences in mean RT to constructs such as general intelligence e.g.,  or mentaAs increasing nondecision time is assumed to slow RTs in both conditions equally it would not affect the RT cost. It is also assumed the nondecisional processes do not affect accuracy, so it would not affect the correlation between RT costs and error costs. However, the assumption very much a simplification, and depends on the definition of what is visual processing and what is goal-directed information accumulation. Indeed, this distinction has no clear mapping onto visual information flow through the brain, which is sensitive to attention/relevance from the earliest stages. There are some paradigms where differences in nondecision time between conditions have been explicitly implicated  noise. Starting point variability is often invoked to account for fast errors, which would be likely if the accumulation process sometimes begins close to the boundary for the incorrect response . This enIn our simulations, variation in response caution led to negative correlations between RT and error costs, whereas variation in selection efficiency led to more positive correlations. Therefore, the model framework predicts that reducing variability in response caution—and thus increasing the proportion of variance accounted for by selection efficiency—would lead to more positive correlations.In their examination of the relationship between average accuracy and average RT in numerical cognition, Supplementary Material E. For brevity, we give an overview here. In the first study, 57 participants performed both the flanker and a manual Stroop task in two sessions taking place 4 weeks apart. In the second study, 81 participants performed the flanker task and a random dot motion discrimination task in a single session. At the beginning of speed-emphasis blocks, participants were asked to “Please try to respond as quickly as possible, without guessing the response.” For accuracy blocks, participants were told “Please ensure that your responses are accurate, without losing too much speed.” For standard instruction blocks, participants were instructed “Please try to be both fast and accurate in your responses.” Feedback was also manipulated to encourage speed and/or accuracy in accordance with the instructions.Detailed methods for these experiments are in The same inclusion criteria and RT cut-offs described in Part 1 were applied; the number of participants included in the analysis for each task, session, and study is shown in To test whether the correlation between RT and error costs is more positive under speed instructions relative to standard instructions, we adopted a meta-analytic approach. First, for each dataset, we calculated the Pearson correlation between the RT costs and error costs in speed and standard instruction conditions separately. We then applied the Fisher’s z-transform  to the cp < .001), indicating that the correlation between RT and error cost is indeed more positive under speed instructions. Note that this effect was fairly consistent, with none of the data sets showing a more positive correlation under standard instructions. This is consistent with the accumulation model framework.We limit our coverage of the results to the correlations between RT and error costs. The size of the effect that we observe (.19) is small by commonly used criteria , though Supplementary Material E). Note that while our hypothesis was derived from comparing speed with standard instructions as in The enhanced correlations under speed instructions do not arise simply from expanding the variance of the constituent variables; the standard deviation of the RT cost decreased from 30 ms to 18 ms on average, while it increased from 5% to 7% for the error cost (see In our logic we assumed that speed instructions both lower thresholds (response caution) and reduce its variance across participants see . That isOne might also ask whether these findings are consistent with alternative models of the speed–accuracy trade-off. Two prominent explanations are the fast-guess model , 1970 anSupplementary Material I. Briefly, increased correlation between RT costs and error costs under speed emphasis is compatible with a deadline model. Reduced deadline variance acts like reduced threshold variance, limiting the ability of participants to trade errors for longer RTs in the more difficult condition. A fast guess account does not predict a more positive correlation, as an equal number of fast guesses are added to both conditions irrespective of their difficulty.Both models have fallen out of favor in recent years due to their inability to capture data from a range of speed-accuracy experiments  using the Simon task. In the same subjects, we compared the correlation between RT costs and error costs when trials are randomly intermixed , compared with blocks of congruent and incongruent trials administered separately . We predicted that the correlation between RT and error costs would be more positive in intermixed trials. Detailed methods are reported in Supplementary Material F. The data are available at https://osf.io/btsrw/.Anecdotal support for this hypothesis can be found in The correlations between RT and error cost measures can be seen in p < .001. Note that both were significantly different from zero, with a significant negative correlation observed in blocked trials.As predicted, a modified Pearson-Filon test  showed tNote that the overall error rates and RTs were larger for mixed trials  compared with blocked trials , while RT costs and variance in the RT costs was greater for blocked trials. Therefore, the higher correlation in mixed trials does not arise simply from an increase in variance. This pattern is consistent with participants decreasing their caution (to a variable extent) when they anticipate there will be no difficult trials. The degree to which they do this then drives the correlation between error costs and RT costs. In contrast, where trials are intermixed within blocks, caution cannot be adjusted between trial types, and the correlation between RT costs and error costs are driven more by variation in response selection.A second notable observation from As psychologists explore what In the second part of this article, we illustrated how subtractions do not control for threshold (caution) differences within the framework of decision models. In turn, this means such models predict that RT costs or error costs are rarely interchangeable as performance measures—they would only be strongly correlated when threshold variance is very low. Evidence accumulation models provide a theoretical framework across cognitive psychology and cognitive neuroscience cf., . They haThe decomposition of speeded decisions into (at least) two components does come at a cost of increasing the complexity of interpretations. However, this complexity may be a necessity rather than a handicap. Theorists have noted that there is a tendency in the literature to attribute variation on a given task almost directly to variation in a single cognitive function, such as executive control, numeracy, or inhibition . VerbrugWe expand upon these recommendations in three key ways. First, we focus on the common practice of subtracting one condition from another, which is often assumed to control for differences in caution. Second, we demonstrate that inconsistent relationships between effects in RTs and effects in accuracy are widespread. These inconsistencies permeate domains of psychology that are at the forefront of initiatives focused on understanding cognitive deficits in clinical conditions, such as executive control, attention and response inhibition e.g., .Third, we demonstrate that interpreting correlations between RT costs and error costs with respect to mechanisms of response selection and response caution is not specific to a given model. It has been noted that there is a high level of mimicry between the LBA and DDM, and that despite different architectures, often one would interpret effects with respect to the same underlying processes . The DMCDecision models also allow for other mechanisms to be incorporated. For example, biases due to stimulus probabilities or incentives e.g.,  can be cConsidering speed and accuracy in conjunction has a long history in psychology in the context of the speed–accuracy trade-off SAT; . PachellFor many research questions, response caution might be considered a nuisance parameter that confounds the effect of interest. For example, if a researcher is interested in individual differences in attention, then they are likely interested in the efficiency of information processing, either on average or with respect to some stimulus manipulation. This is the very logic behind subtracting between conditions, which was assumed to allow such processes to be examined in isolation. But caution is an interesting and fundamental component of decision-making. A wealth of literature exists examining the cognitive and neurological mechanisms underlying response caution, in both clinical and nonclinical populations . For somFor decision models themselves, there is an ongoing debate whether caution is adequately captured by a simple threshold that does not vary within trials. For example, mechanisms by which the level of required evidence decreases over time have been proposed . These pOur simulations cover only a selection of evidence accumulation models used in the literature, though most models implement mechanisms of response selection and response caution in comparable ways. For example, the leaky competing accumulator LCA; , implemeWe selected four distinct models to illustrate common behavior, not to emphasize any differences. It is also worth noting that some apparent differences between models are just different ways of achieving a similar goal. For example, ALIGATER contains an explicit selective inhibition mechanism, whereas inhibition is implicit in the DMC. Both amendments to the basic models were introduced to ensure nonlinear dynamics—that initial strong support for irrelevant information diminishes while support for relevant information is maintained. The DMC is thus compatible with an explicit mechanism of top-down inhibition .Some model differences reflect the task or modality in which the model is typically applied. For example, ALIGATER simulations assume equal mean initial rise rates for both target and distractor; an assumption also made by other models of eye movement tasks e.g., , where aThe distinction between these models and their applications is not always clear cut, however , and neiThe assumptions made about perceptual  processes have theoretical implications. For example, the distributional shape of nondecision time variability has recently been questioned . WhereasEven more counterintuitively for cognitive scientists, using different response modalities  changes the sensory part of nondecision time, with knock-on consequences for response selection phenomena . This isAbsent correlation between RT costs and error costs in the Stroop task was previously noted by These effects could also be described within a decision model framework, given that we would expect individuals to adopt different levels of caution in blocks of different congruency proportions . A previous study examining the relationship between diffusion model parameters measured from choice RT tasks and a latent WM factor observed a positive correlation between WM and drift rate, and a negative correlation between WM and boundary separation . Thus, iIn the domain of task switching, the reliability and validity of the traditionally used RT costs has also been questioned . These dThese methods are not without their criticisms, however. The use of residual scores as an alternative to difference scores have a long history , though Perhaps the largest advantage the decision model framework has over these alternative scoring methods is that the composite scores lack a theoretical justification for their respective methods of combing accuracy and RT into a single metric . Lohman We have discussed the correlation between RT costs and error costs in the context of evidence accumulation models, though theorists have raised concerns about the interpretation of RT measures outside of this framework e.g., . FurtherWhere While both the IDRT and RAM frameworks broadly capture how the latencies of different stages contribute to RT measures, they are agnostic to the nature of the cognitive processes underlying response selection. Further, they do not discuss the relationship between RT and accuracy. This is because both frameworks assume tasks are performed with minimal errors. In reflecting on the divide between individual differences and experimental research, 10.1037/bul0000164.supp


Prunus avium cv. 0900 Ziraat) from Turkey identified a new member of the genus Robigovirus designated cherry virus Turkey (CVTR). The presence of the virus was confirmed by electron microscopy and overlapping RT-PCR for sequencing its whole-genome. The virus has a ssRNA genome of 8464 nucleotides which encodes five open reading frames (ORFs) and comprises two non-coding regions, 5′ UTR and 3′ UTR of 97 and 296 nt, respectively. Compared to the five most closely related robigoviruses, RdRp, TGB1, TGB2, TGB3 and CP share amino acid identities ranging from 43–53%, 44–60%, 39–43%, 38–44% and 45–50%, respectively. Unlike the four cherry robigoviruses, CVTR lacks ORFs 2a and 5a. Its genome organization is therefore more similar to African oil palm ringspot virus (AOPRV). Using specific primers, the presence of CVTR was confirmed in 15 sweet cherries and two sour cherries out of 156 tested samples collected from three regions in Turkey. Among them, five samples were showing slight chlorotic symptoms on the leaves. It seems that CVTR infects cherry trees with or without eliciting obvious symptoms, but these data should be confirmed by bioassays in woody and possible herbaceous hosts in future studies.High throughput sequencing of total RNA isolated from symptomatic leaves of a sweet cherry tree ( The world production of cherries has reached about 2.3 million tons and is mainly distributed in Asia (43%), Europe (37%) and America 18%) [8% [1]. TPrunus species. Within the last 5 years, seven of these viruses and one viroid have been discovered in Prunus hosts among which four were described on cherries [Prunus species [Prunus virome and to identify viruses potentially responsible for the diseases with unknown or poorly characterized etiology. A number of diseases of sweet cherry that were first described already in the 1940s are presumed to have a viral etiology based on the graft-transmissible nature of these diseases [To date, 44 viruses and three viroids have been described in the nine main cultivated cherries . This nu species . The avadiseases . The rusdiseases . Trees adiseases  reporteddiseases . The extdiseases . Betaflexiviridae family. Within this family, different virus species are expected to have less than 72% nucleotide identity (or 80% amino acid identity of encoded proteins) in the coat protein (CP) or replicase/RNA dependent RNA Polymerase (RdRp) genes. Viruses from different genera usually have less than about 45% nucleotide identity in these genes [Robigovirus, in the Betaflexiviridae family by the International Committee on Taxonomy of Viruses (ICTV) since 2016 [Robigovirus  and is chosen based on the symptoms associated with these viruses in sweet cherry cultivars. This genus also includes the cherry green ring mottle virus (CGRMV), another well-known Prunus virus [Robigovirus genus. Different virus-like sequences have recently been reported to be associated with CRMD and CNRMD; they were identified as Cherry rusty mottle-associated virus (CRMaV) and Cherry necrotic rusty mottle virus (CNRMV), respectively , both bese genes . Their dnce 2016 . The namus virus . The twoRobigovirus genus currently consists of five species represented by four viruses infecting Prunus spp. and one virus infecting palms. CRMaV and CNRMV are associated with rusty mottle symptoms, while cherry twisted leaf associated virus (CTLaV) is associated with a milder and delayed expression of rusty mottle symptoms [The symptoms . Althougsymptoms . Africansymptoms . Robigovirus genus from cherries and the results of an epidemiological survey conducted in the most important cherry producing regions in Turkey. In Turkey, there has been some detection and characterization studies on commonly known cherry viruses like prune dwarf virus (PDV), prunus necrotic ringspot virus (PNRSV) and apple chlorotic leaf spot virus (ACLSV) ,19 but nThe HTS yielded a total number of 8,344,550 reads  ranging from 84 to 151 nucleotides. After quality trimming by BBDuck and Dedupe, 3,080,225 unique reads were obtained. The reads were assembled into 61,439 contigs summing 25,190,414 nucleotides. The maximal, minimal and median lengths were 13,954, 91 and 268 nucleotides, respectively. −5 identified a contig of 8444 nucleotides with high homology  to the cherry rusty mottle associated virus (CRMaV). The contig  had a match on 428 amino acids with an identity of 74% . No other plant virus was identified in the generated contigs. Further confirmation was carried out by a BLASTX search on the non-redundant (nr) database  were retrieved from The National Center for Biotechnology Information (NCBI) for further analysis. The sequences were aligned against the viral contig and the highest identity (57.1%) were found in CTLaV which was in agreement with BLASTX results  of 1 × 10database . Subsequ results . Betaflexiviridae  protein (RdRp gene of CVTR showed 51.9–58.7% and 43–53.1% identities at nt and amino acid (aa) levels, respectively with other robigoviruses (RdRp: (1) a methyltransferase domain (residues 43–352) which is found in a wide range of ssRNA viruses, including Hordei-, Tobra-, Tobamo-, Bromo-, Clostero- and Caliciviruses; (2) an RNA dependent RNA polymerase (RdRp-2 super family) domain (residues 1691–2012); (3) peptidase-C23 super family (residues 1045–1132); (4) viral helicase1 super family (residues 1226–1475); and (5) 2OG-Fe (II) oxygenase superfamily (residues 752–845). The genome sequences based on HTS and Sanger sequencing were deposited in GenBank under accession numbers MH177869 and MK600387, respectively.In order to validate the HTS results, overlapping primers along with 5′/3′ RACE  were use protein . The RdRoviruses . As seenFlexiviridae, CVTR has three-partite movement proteins called triple gene block (TGB). The size of these three overlapping ORFs are as follows: TGB1 corresponds to 675 nt which codes for a protein of 225 aa (25 kD). It shares 44.7–60% aa and 52.4–58.8% nt identity to the other five robigoviruses. TGB2 has 351 nt  and shares 39.8–44.4% sequence identities at the amino acid level, and 48.7–53% at the nt level to the above mentioned viruses. TGB3 is 195 nt in length  and shares 38–44.8% identities at the amino acid and 54.9–58.5% at nt levels, respectively. Finally, ORF 5 is 798 nt in length, encodes for a 268 aa coat protein (30 kD). A comparison of this ORF revealed that CVTR shares sequence identity to the other five viruses in a range of 45.3–50.6% at the amino acid and 52.4–57.6% at nt levels, respectively  is observed. They fall into four distinct clades that correspond with distinct symptoms in cherry and woody indicator hosts [Foveavirus, although the protein identity is lower than 40% with this genus. Moreover, phylogenetic analyses of the replicase genes of Betaflexiviridae viruses do not justify placing the cherry viruses in genus Foveavirus. Amino acid and nucleotide identities within and between the four closely-related cherry viruses, as a general rule, satisfy the molecular criteria used in the family that different species should have less than about 72% nt identity (or 80% aa identity) between their respective CP or replicase genes [Betaflexiviridae, cherry virus Turkey (CVTR) can be considered as a new species in genus Robigovirus.Four viruses belonging to equenced . Two of or hosts . This gese genes . AccordiQuinivirinae subfamily, Betaflexiviridae family  and LG+G model [CP aa sequences inferred by using the ML method based on the General Reverse Transcriptase + Freq. model [Robigovirus genus, and particularly related to CTLaV, CRMaV, CGRMV and CNRMV, and to a lesser extent to AOPRV.The phylogenetic tree reconstructed from full-genome reference sequences showed clustering of the CVTR into a group along with four robigoviruses , members of e family . Two clo+G model , CVTR waq. model  resultedq. model  full genBetaflexiviridae family are flexuous filaments and usually 12–13 nm in diameter (range 10–15 nm) and from 600 to over 1000 nm in length, depending on the genus [Robigovirus genus was recently assigned, there are not many electron micrographs available to directly compare the particles of CVTR in this study. Transmission electron microscope observations of ultrathin sections of Kwanzan cherry trees affected by green ring mottle disease, caused by CGRMV, revealed the presence of flexuous, rod-shaped virions ranging from 1000 to 2000 nm in length and were 5–6 nm in diameter [Robigovirus genus.Viral particles were purified from leaves of the cherry tree, and analyzed by HTS. Electron micrographs showed filamentous particles , with a he genus . Due to diameter . Beside diameter . All theIn order to investigate the incidence of CVTR in four different provinces belonging to three geographical regions of Turkey with major relevance in cherry production , 156 samples were collected and tested by RT-PCR. The map of the surveyed areas and the locations of positive samples for CVTR are shown in RdRp region) and 7336F/8134R , designed according to the sequence of CVTR genome . Seventeen amplicons from the CP region (MH986197-MH986213) and six amplicons from the replicase region (MH986214-MH986219) were sequenced and deposited in GeneBank  to 99% (BUR8) identity with the original CVTR isolate  in Bursa (Marmara Region) and also in sour cherries (unknown cv.) . We also tested the same 17 CVTR-positive samples for the presence of other prunus viruses  some of which are already known to be widespread in Turkey . Only fiThe sample from one cherry plant showing slight chlorotic spots on the leaves grown in Bursa province, Turkey (isolate BUR12) was collected in May 2017 and submitted to high throughput sequencing of total RNA. In addition, 156 cherry samples were collected from four provinces, belonging to three geographical regions , where cherry production is economically important in Turkey. Among collected samples, only five sweet cheery plants in Bursa province were exhibiting some chlorotic spots and mottling.RNA extraction was carried out using RNeasy Plant Mini Kit (Qiagen) and the sample preparation buffer (RLT buffer) was supplemented with Plant RNA Isolation Aid (ThermoFisher Scientific). The RNA was treated with Turbo DNase (ThermoFisher Scientific) and concentrated by precipitation in sodium acetate and ethanol. After resuspending the extracted RNA, the quality and quantity were checked by Nanodrop (Thermofisher), Picogreen (Thermofisher) and 2100 Bioanalyzer System (Agilent). The ribosomal RNA was removed using the Ribo-Zero™ Plant Leaf Kit (Illumina) and the library was prepared using the TruSeq Stranded Total RNA Library Prep Kit (Illumina). The samples were sequenced on the Illumina Nextseq 500 platform with paired sequencing of 2 × 150 nt at the GIGA facilities of Liège University .https://www.geneious.com) was used for sequence analysis. BBDuck (version 36.92) plugin removed low quality reads using default settings. Furthermore, duplicated reads were removed by Dedupe plugin integrated into Geneious with a k-mer = 31. The trimmed reads were then de novo assembled into larger contigs by SPAdes [ftp://ftp.ncbi.nlm.nih.gov/refseq/release/viral). A single hit was retrieved for each contig. Contigs with homologies with plant viruses were further analyzed by BLASTX on the non-redundant (nr) protein database from NCBI [https://web.expasy.org/protparam/).The reads were trimmed for adapters and sorted by sample using Basespace (Illumina). The quality of sequences was further analyzed by FASTQC v0.11.7 . The Geny SPAdes  with a krom NCBI . The conrom NCBI . Complet® RACE 5′/3′, Clontech) according to manufacturer’s instructions [After the assembly of HTS data, several overlapping primer pairs were designed to validate deep sequencing results by Sanger sequencing. The sequences of the primers used to complete the CVTR genome sequence, are presented in ructions . BrieflyRdRp genes  72 °C for 1 min were performed, followed by a final extension at 72 °C for 10 min. The amplified fragments were visualized by agarose gel electrophoresis and their specificity confirmed by Sanger sequencing .For the survey of virus presence in cherry orchards, extraction of total nucleic acids (TNAs) was performed by using RNeasy Plant Mini Kit . The virus was detected by RT-PCR. First 5 µL of RNA were reverse transcribed using random hexamer primer (Thermo Fisher Scientific) and cDNA was amplified using detection primer pairs which amplifies coat protein and https://viralzone.expasy.org/294). Multiple alignments of nucleotide sequences or of deduced amino acid sequences were performed using the ClustalW program implemented in MEGA v7 [RdRp and CP genes, were constructed from aligned sequences using the maximum likelihood by best fitting methods and bootstrapping evaluation of branching validity. Nucleotide and amino acid identities were determined by pairwise sequence alignment with Muscle program embedded in Geneious [https://www.ebi.ac.uk/Tools/psa/emboss_needle/).The phylogenetic tree was reconstructed from nucleotide reference sequences of Tymovirales listed in ViralZone  in the replicase and CP genes [We describe a new virus, designated cherry virus Turkey (CVTR), identified from a slightly symptomatic cherry tree by HTS analysis. Considering the accepted species demarcation molecular criteria for the family CP genes , CVTR shCP genes , investi