Chronic inflammation and abnormal cholesterol metabolism are involved in the pathogenesis of gallbladder stone disease (GSD) and that of prostate cancer in experimental studies. We assessed the association between GSD and prostate cancer in this population-based study.P <.001) and the risk of prostate cancer  were greater in the patients with GSD than in those without GSD. Furthermore, the risk of prostate cancer increased with the time of follow-up after a diagnosis of GSD, particularly after 9 years of follow-up .The cumulative incidence of prostate cancer (log-rank test: We identified 9496 patients who were diagnosed with GSD between 1998 and 2011 from Taiwan's Longitudinal Health Insurance Database 2000 as the study cohort. We randomly selected 37 983 controls from the non-GSD population and used frequency matching by age, sex, and index year for the control cohort. All patient cases were followed until the end of 2011 to measure the incidence of prostate cancer.GSD is associated with an increased risk of prostate cancer, and the risk increases with the time of follow-up after a diagnosis of GSD. Prostate cancer is the most common cancer in men in Western countries. The incidence and prevalence of prostate cancer has steadily increased worldwide, and it accounted for 28% of newly diagnosed cancers in American men in 2010 and 22.2% of newly diagnosed cancers in European men in 2008 . FurtherGallbladder stone disease (GSD) is a worldwide disease; its prevalence increases with socioeconomic development, and it affects approximately 10% of the adult population in Western countries and 5% of the adult population in Taiwan , 7.Experimental studies have shown both chronic inflammation and abnormal cholesterol metabolism are involved in the pathogenesis of GSD and that of prostate cancer , 11, 12.P < .001). Figure P <.001).We selected 9496 patients with GSD as the case cohort and 37 983 patients without GSD as the control cohort Table . The meaThe incidence density rates of prostate cancer were 1.64 and 1.14 per 10 000 person-years for the GSD and non-GSD cohorts, respectively Table . The risThe risk of prostate cancer  was greater for the GSD cohort than for the non-GSD cohort after adjustment for age, occupation, urbanization level, and comorbidities of hyperlipidemia, diabetes, hypertension, BPH, urinary stones, urinary tract infection, obesity, asthma, CAD, COPD, stroke, and antihypertensive medication use in Cox proportional hazards regression  (Table We observed that 62.6% of the patients with GSD were older than 50 (mean: 56.4 ± 15.8) years, and GSD was more prevalent in the patients with white-collar jobs (48.1%) and in those living in ≥ level 2 urbanized areas (59.2%). The aging process is accompanied by increased biliary cholesterol secretion, decreased activity of cholesterol 7 α hydroxylase, decreased bile salt synthesis, impaired gallbladder motility, and increased exposure time to lithogenic factors , 22. WesOur results suggest the patients with GSD had more comorbidities. A low serum high density lipoprotein cholesterol (HDL-C) concentration can increase biliary cholesterol secretion, whereas a high triglyceride level can result in supersaturated bile and impaired gallbladder emptying , 25. InsAccording to our analysis, increased prostate cancer risk was associated with GSD, aging, white-collar and blue-collar jobs, the highest urbanization level (level 1), hyperlipidemia, and BPH Table . DysplasGSD and prostate cancer may be associated because of the common risk factors. Nevertheless, our results reveal that GSD is related to subsequent prostate cancer development after adjustment for age, occupation, urbanization level, hyperlipidemia, diabetes, hypertension, BPH, urinary stones, urinary tract infection, obesity, asthma, CAD, COPD, stroke, and antihypertensive medication use in Cox proportional hazards regression. Moreover, our results also suggest that the risk of prostate cancer increases with an incremental duration of follow-up after GSD diagnosis, particularly after 9 years of follow-up Table . We coulCholesterol metabolism and chronic inflammation are reported to be the major pathophysiological mechanisms for the association between GSD and prostate cancer , 9. BiliOur study had several strengths. First, it is the largest population-based study on the association between GSD and prostate cancer, and a longitudinal database with a 14-year observation period for a representative cohort of 1 000 000 residents was used to examine the association between GSD and the subsequent development of prostate cancer. Second, the recruited patients were sampled from the approximately 99% of Taiwan residents who are covered by the Taiwan NHI program.Our study also had several limitations. First, we should acknowledge the evidence derived from this retrospective cohort study is generally of lower methodological evidence than that from randomized controlled trials because a retrospective cohort study is subject to many biases due to lack of the necessary adjustments for possible confounding factors. For adjustment, we used COPD diagnosis, obesity, and occupation and urbanization level to replace smoking habits, BMI, and socioeconomic status, respectively. GSD was consistently related to prostate cancer development according to multivariable Cox proportional hazards regression. However, the association between GSD and prostate cancer requires further studies to ascertain this association is an epiphenomena or a causal relationship. Second, the risk of GSD and prostate cancer might have been underestimated in our study if the patients did not seek medical consultation. However, the NHI program covers more than 99% of Taiwan's population, and the accessibility and affordability of health care are high in Taiwan. Finally, we could not individually review the medical records to validate the diagnosis of GSD and prostate cancer. However, the NHIRD covers a highly representative population of Taiwan because the reimbursement policy is universal and the government is the only buyer. All insurance claims should be scrutinized by medical reimbursement specialists and peer reviewed according to the standard diagnosed criteria in the study. The doctors or hospitals will face a lot of penalties if they made wrong diagnoses or coding. The diagnoses of GSD and prostate cancer based on ICD-9 codes in this study should be highly reliable through the accessibility and affordability of NHI program, high sensitivity and specificity of ultrasound in the diagnosis of GSD, and the linkage to the registry of Catastrophic Illness Certificate. In addition, some related studies about GSD or prostate cancer with the same diagnosed method and criteria by ICD-9 coding were already been published , 40.In conclusion, our population-based cohort study indicates that GSD is associated with an increased risk of developing prostate cancer. The risk of prostate cancer increased with the time of follow-up after a diagnosis of GSD, particularly after 9 years of follow-up. However, further studies are required to clarify whether GSD is a causal risk factor for prostate cancer.http://www.nhi.gov.tw/english/index.aspx) [http://w3.nhri.org.tw/nhird//date_01.html) are maintained in the National Health Research Institutes (NHRI) (http://nhird.nhri.org.tw/), which is established by the government as a nonprofit foundation. All encrypted data were deposited in an appropriate open and public repository, and the researchers can use the database for research after approval of formal application [A retrospective cohort study was conducted using registration and claims data sets from 1998 to 2011 obtained from the LHID2000.The Taiwan National Health Insurance (NHI) program is a universal, single-payer, and compulsory health insurance system with more than 99% population coverage for 23 million Taiwanese residents and more than 97% of the hospitals and clinics contracted (ex.aspx) . The LHIlication , 17. TheMale patients aged ≥20 years with newly diagnosed GSD  from 1998–2011 were identified using the LHID2000. The date of GSD diagnosis was defined as the index date. Patients with a history of prostate cancer (ICD-9-CM code 185) were excluded. The non-GSD cohort consisted of patients randomly selected from the LHID2000 without a GSD history. For each GSD case, 4 men without GSD were randomly selected as the control cohort through frequency matching according to sex, age (every 5-y span), and the index date year. The same exclusion criteria were also applied to non-GSD controls. Figure All the study subjects were followed up until they were diagnosed with prostate cancer or censored because of loss to follow-up, withdrawal from the insurance program, death, or the end of 2011. We ascertained the death, loss of follow-up, or withdrawal of NHI program from the NHI withdrawal history files kept in NHIRD. We ascertained the diagnosis of prostate cancer through a record linkage to the registry of Catastrophic Illness Certificate, a part of NHI program in Taiwan . The dat2), and level 4 indicated the least urbanization. We defined baseline comorbidities, including hyperlipidemia (ICD-9-CM code 272), diabetes (ICD-9-CM code 250), hypertension (ICD-9-CM codes 401-405), BPH (ICD-9-CM code 600), urinary stones , urinary tract infection , obesity (ICD-9-CM code 278), asthma (ICD-9-CM code 493), coronary artery disease (CAD) (ICD-9-CM codes 410-414), chronic obstructive pulmonary disease (COPD) , and stroke (ICD-9-CM codes 430-438). A history of antihypertensive medication use was included in the analysis.The socioeconomic variables used in this study comprised age, occupation, and urbanization level. Occupation and urbanization level have been well defined in previous studies , 20. In t test to analyze continuous variables. The Kaplan–Meier method was used to depict the curves of cumulative prostate cancer incidence for the 2 cohorts, and the log-rank test was employed to examine the difference between the curves. We computed the incidence density rate (per 1000 person-y) of follow-up for each cohort. Univariable and multivariable Cox proportional hazards regression models were used to examine the effect of GSD on the risk of prostate cancer, which was expressed as a hazard ratio (HR) with a 95% confidence interval (CI). The multivariable models were adjusted for age, occupation, urbanization level, and the comorbidities of hyperlipidemia, diabetes, hypertension, BPH, urinary stones, urinary tract infection, obesity, asthma, CAD, COPD, stroke, and antihypertensive medication use. SAS Version 9.3 software  was used for data analysis. A significance level of P <.05 was used for the comparisons for 2-sided testing.The distribution of demographic data , comorbidities , and antihypertensive medication use were compared between the GSD and non-GSD cohorts by using a chi-square test to examine categorical variables and a


Sequencing of isolates from patients in Bahia, Brazil, where most Zika virus cases in Brazil have been reported, resulted in 11 whole and partial Zika virus genomes. Phylogenetic analyses revealed a well-supported Bahia-specific Zika virus lineage, which indicates sustained Zika virus circulation in Salvador, Bahia’s capital city, since mid-2014. Aedes (http://www.paho.org/hq/index.php?option=com_docman&task=doc_view&Itemid=270&gid=35262&lang=en).Zika virus is an arthropodborne RNA virus primarily transmitted by mosquitoes of the species The rapid geographic expansion of Zika virus transmission and the virus’s association with microcephaly and congenital abnormalities (http://chiulab.ucsf.edu/surpi/). Results of the metagenomic analyses and identification of co-infections with chikungunya virus are reported elsewhere . During April 2015–January 2016, acute Zika virus infection was diagnosed for 15 patients whose serum samples tested positive by a qualitative reverse transcription PCR (RT-PCR) by using primers targeting the nonstructural 5 gene . Pathoge4 copies/mL generated sufficient viral metagenomic data for complete genome assembly. For the remaining samples with lower titers, metagenomic next-generation sequencing libraries were enriched for Zika virus sequencing by using xGen biotinylated lockdown capture probes  designed to tile across all sequenced Zika virus genomes >10,000 nt in GenBank (http://www.ncbi.nlm.nih.gov/genbank) as of March 1, 2016. Capture probes were curated for redundancy at a 99% nt similarity cutoff. Enrichment was performed on the metagenomic libraries in pools of 8 libraries (including Zika virus–negative serum sample controls) by using the xGen lockdown probe protocol and the SeqCap EZ Hybridization and Wash Kit . Eleven Zika virus genomes with >40% genome recovery (mean 69.4% ± 2.0%) were assembled ; maximum-likelihood (ML) and Bayesian phylogenetic inferences were determined by using PhyML version 3.0 (http://www.atgc-montpellier.fr/phyml/) and BEAST version 1.8.2 (http://beast.bio.ed.ac.uk/), respectively. The best-fit model was calculated by using jModelTest2   Figure 2The updated phylogenetic analyses, including the newly identified clade C, suggest that Zika virus was introduced in Bahia during March–September 2014. An isolate from Maranhão in northeastern Brazil  is ancestral to the Bahia clade   Figure 2Our results suggest an early introduction and presence (mid-2014) of Zika virus in the Salvador region in Bahia, Brazil. Given the size of the cluster and statistical support for it, this lineage likely represents a large and sustained chain of transmission within Bahia state. Most cases of this Zika virus lineage clustered closely to a sequence from Maranhão, and we found evidence for an additional potential introduction to Bahia from Pará state. Consequently, Zika virus in Salvador during mid-2014 was likely introduced from other regions in Brazil rather than from outside the country. Current findings of Zika virus emergence in Bahia state during mid-2014 are consistent with first-trimester viral infection in pregnant women corresponding to the initial reported cases of fetal microcephaly, which began in January 2015  identified in this article comprises most Zika virus cases in the state. Brazil currently faces a major public health challenge from co-circulation of Zika, dengue, and chikungunya viruses (Details of library construction and next-generation sequencing methods, phylogenetic analyses, and data availability for this study, including supplemental figures.


Turnip mosaic virus was determined by Sanger sequencing. After seven years of serial passaging by mechanical inoculation, the isolate was resequenced by RNA sequencing (RNA-Seq). Eighteen single nucleotide polymorphisms were identified between the isolates. Both isolates had 96% identity to isolate AUST10.The complete genome sequence of an Australian isolate of  Turnip mosaic virus is a Potyvirus member within the Potyviridae family. Due to the wide distribution and extensive host range, Turnip mosaic virus (TuMV) is considered to be the second most damaging crop virus across 28 different countries and regions . RNA was isolated from mechanically inoculated Nicotiana benthamiana and cDNA synthesized as previously described  analysis using the TruSeq RNA library synthesis kit (Illumina) and the MiSeq platform (Illumina). After quality trimming, 33,752,578 RNA-Seq reads were obtained, and the Geneious version 8.1.7 software was used to map 1,482,837 reads to the TuMV-QLD1a reference genome. The average depth of coverage was 15,905×, with maximum coverage of 26,792×, and minimum coverage of 578×.Here, we report the complete sequence of an Australian isolate of TuMV, designated TuMV-QLD1a, from a field-grown escribed . Ten oveRaphanus plants but does infect Brassica plants systemically causing phenotypic symptoms  in length. However, 18 single nucleotide polymorphisms (SNPs) were identified between the two isolates. Of these, two were heterogeneous SNPs in TuMV-QLD1a that were found to be uniform in TuMV-QLD1b (A/G→A and A/G→G). Conversely, five different heterogeneous SNPs emerged in TuMV-QLD1b. An amino acid alignment revealed 10 amino acid changes from TuMV-QLD1a to TuMV-QLD1b. Both isolates share 96% identity with the AUST10 isolate (accession number AB989634), which was also collected in Queensland, Australia, in 1996 (symptoms . This suKX641465 and KX641466, respectively.The GenBank accession numbers for TuMV-QLD1a and TuMV1b are


Deformed wing virus is an important contributor to honey bee colony losses. Frequently queen failure is reported as a cause for colony loss. Here we examine whether sexual transmission during multiple matings of queens is a possible way of virus infection in queens. In an environment with high prevalence of deformed wing virus, queens (n = 30) were trapped upon their return from natural mating flights. The last drone’s endophallus (n = 29), if present, was removed from the mated queens for deformed wing virus quantification, leading to the detection of high-level infection in 3 endophalli. After oviposition, viral quantification revealed that seven of the 30 queens had high-level deformed wing virus infections, in all tissues, including the semen stored in the spermathecae. Two groups of either unmated queens (n = 8) with induced egg laying, or queens (n = 12) mated in isolation with drones showing comparatively low deformed wing virus infections served as control. None of the control queens exhibited high-level viral infections. Our results demonstrate that deformed wing virus infected drones are competitive to mate and able to transmit the virus along with semen, which occasionally leads to queen infections. Virus transmission to queens during mating may be common and can contribute noticeably to queen failure. Apis, Vespula, Pogonomyrmex and AttaApis (honey bees), where it has been well characterised23Apis mellifera L., mating occurs at a considerable distance to the queen’s colony, at so-called drone congregation areas (DCA)2Within the Hymenoptera, polyandry is uncommon and only known from the eusocial genera Apis may be considered implausible. Nonetheless, a considerable number of studies found the reproductive organs of queens and drones to be frequently and highly infected with deformed wing virus (DWV), occasionally along with other viruses8121314151617Despites its benefits, multiple mating also introduces risks to honey bee queens, and consequently, to the colony. The queens face dangers from this mating behaviour, for instance through predation or adverse weather conditions910Varroa destructor mites, its prevalence and virus titres are usually low1923Varroa mite from the Asian A. cerana to A. mellifera and its subsequent worldwide spread261928232526Varroa mite may act as an intermediate host for DWV, although this role is not fully understood, yet28311922Infection with DWV is common in honey bees192021Varroa mites are able to reproduce on both worker and drone brood on their new host A. mellifera, although with a strong preference for drone brood, in contrast to the mites’ exclusive reproduction on drone brood of the original host A. cerana3412Varroa parasitisation, since the development time of the queen pupa is too short for the mite to complete its reproductive cycle. Therefore, one of the possible explanations for the reported high prevalence of DWV in queens could be virus transmission from infected drones via natural mating.While it has previously been shown that infected drones are able to fly and frequently can reach DCAs20The presence and integrity of a queen is essential for a honey bee colony; therefore, queen health has a direct impact on colony health. Failure or loss of the queen is known as a discernible factor of colony loss36373817All experimental queens were raised in the same nurse colony, and queen production was monitored closely for the presence of DWV and other viruses. All queen larvae and royal jelly samples collected from uncapped queen cells were free of DWV (data not shown). The tissues of most of the newly emerged queens either had no virus at all or contained low titres of DWV (for definitions of virus titres refer to Varroa infested hives in Flakkebjerg (n = 47 drones) showed high levels of DWV.All drones sampled from the healthy drone provider colonies on Nekselø were free of DWV. In contrast, the endophalli from nine drones captured in front of the high Among the 30 queens allowed to mate in Flakkebjerg, nine returned from mating flights of more than 7 min without exhibiting mating signs upon their return. However, they started oviposition of fertilised eggs shortly after, thus confirming that they had successfully mated. From the 21 remaining queens, 29 mating signs were collected, either one or two endophalli from each queen . The DWVAmong the unmated queens, virus levels were generally low and at a maximum reached a medium viral level. One of the unmated queens Un.2,  was compThe queens that mated with healthy drones on Nekselø, similarly only reached medium viral levels. Among the 72 analysed tissue samples from 12 queens mated on Nekselø (N.1 to N.12), only seven tissue samples were found to be infected at a medium level in the abdomen (n = 3), thorax (n = 2), head (n = 1) and sperm (n = 1) . The remThis subgroup consisted of eight queens with little to no DWV titre in all examined tissues  and showed a high DWV load in most examined tissues. One of these queens was found dead (M.30) soon after the onset of oviposition, and a post-mortem RT-qPCR confirmed high viral titre of DWV in the head, thorax and abdomen. Of the eleven endophalli collected from six of these queens, two had low titres, seven a medium level, and two showed high viral titres .It has been shown that Varroa parasitation on drone health and performance are well documented35428Although adverse effects of per se. It may also be possible that some queens are not susceptible to DWV infection. In fact, one queen  and queens mated in Flakkebjerg (DWV infected drones). High titres of DWV only occurred in some queens of the Flakkebjerg group. Alternative transmission routes that could have led to this finding can be ruled out, since the two control groups (unmated queens and Nekselø queens) were maintained in the same apiary and mostly showed low infection levels. While horizontal transmission routes for DWV have been described223146Discolouration of the ovaries, previously described as a pathological reaction to virus infectionVarroa infestation of drone brood and virus transmission during mating. Consequently, infected queens might represent a potential source for distribution of DWV across honey bee populations worldwideA healthy queen is an essential requirement for a strong honey bee colony. In the social context of the colony the queen seems to enjoy the benefit of a certain level of protection against infections. Experimental data suggest that queens and infected workers may avoid contact and only engage in trophallaxis when there is no alternative46811It is well documented that a DWV infection shortens the life expectancy of worker honey bees37383949Varroa mites (0 mites were found in a sample of 300 bees) and essentially free (no virus detected in a sample of 60 worker bees) of the most common bee viruses including: DWV, Black Queen Cell Virus (BQCV), Sacbrood Virus (SBV), Chronic Bee Paralysis Virus (CBPV) and ABPV complex , Kashmir Bee Virus (KBV) and Israeli Acute Paralysis Virus (IAPV)) using the technique previously describedIn July 2012, a large group of sister queens were produced from a healthy colony following the standard procedureAfter six days, fifteen uncapped queen cells containing larvae and royal jelly were sampled and stored at −80 °C. The rest of the queen cells were caged individually (post capping) and transferred to an incubator. Upon emergence, 10 queens were also sampled and stored at −80 °C. The remaining 50 queens were introduced into mating nucs containing 200–250 worker bees2 to stimulate oviposition without mating2 treatment. The entrance of each mating nuc was covered with a queen excluder that allowed workers to pass, but prevented the queens from exiting.Mating nucs of group 1 (n = 8) were placed in the experimental apiary Flakkebjerg. Queens in this group were treated twice with COVarroa mites. The viruses’ status test was performed by sampling 10 to 15 mature drones upon their return to each drone provider hive. The drones from each hive were pooled, freeze-dried and homogenised, and analysed for virusesMating nucs of group 2 (n = 12) were placed on the island mating station, Nekselø, and the queens were allowed to mate freely. For the purpose of mating, an adequate number of mature drones were provided by twelve colonies that are regularly inspected for health and treated against Mating nucs of group 3 (n = 30) were also placed in Flakkebjerg and equipped with queen excluders in front of the mating nucs, as explained above (Mating nucs group 1). These mating nucs were continuously observed on sunny afternoons, between 13:00 and 18:00. When a queen appeared at the entrance for an orientation or a mating flight, the queen excluder was removed and as soon as she departed, the excluder was reinstalled to prevent her unobserved re-entryVarroa mites for the last three years before the experiment, and were allowed to produce drones freely. Prior to the experiment, analysis of fifty worker bees per drone colony confirmed that they all had high levels of DWV, but only showed a minor infection with ABPV complex viruses. In addition, mature drones (n = 47) were captured upon returning to the hives, and their endophalli were individually analysed for viruses.The drone provider colonies in Flakkebjerg (5 colonies) had not been treated against All the queens from the three experimental groups were removed from their mating nucs six weeks after the onset of oviposition. The queens were anaesthetised using carbon dioxide, sacrificed and dissectedAll tissues except the spermatheca and sperm were freeze-dried for three days at 0.0009hPa and −93 °C. Lyophilised tissue samples were crushed and homogenised for 30 s at 500 Hz. Micro-pestles were used to homogenise the spermatheca and sperm tissues. Total RNA was extracted from each sample using NucleoMag 96 RNA Kit. A two-step real time RT-qPCR assay was carried out to quantify the viral load of DWV, SBV, and the ABPV complex as described previouslyVirus copy numbers in each sample were quantified using the absolute quantification method described previouslyHow to cite this article: Amiri, E. et al. Deformed wing virus can be transmitted during natural mating in honey bees and infect the queens. Sci. Rep.6, 33065; doi: 10.1038/srep33065 (2016).


HerPV may have ecological and spatial and temporal patterns similar to Hendra virus and could serve as a sentinel virus for the surveillance of this highly pathogenic virus. The suitability of HerPV as potential sentinel virus is further assessed by determining the serological prevalence of HerPV antibodies in fruit-eating bats from Australia, Indonesia, Papua New Guinea, Tanzania and the Gulf of Guinea, indicating the presence of similar viruses in regions beyond the Australian border.In 2011, an unusually large number of independent Hendra virus outbreaks were recorded on horse properties in Queensland and New South Wales, Australia. Urine from bat colonies adjacent to the outbreak sites were sampled and screened for Hendra and other viruses. Several novel paramyxoviruses were also isolated at different locations. Here one of the novel viruses, named Hervey virus (HerPV), is fully characterized by genome sequencing, annotation, phylogeny and  Mononegavirales, such as filoviruses, lyssaviruses and henipaviruses, has led to increased interest in bats, the oldest known mammals on earth , Pteropus poliocephalus [n = 60], Pteropus scapulatus [n = 41], Pteropus conspicillatus [n = 40] and from bats the species of which was not determined (n/d) [n = 36] (Eidolon helvum) to 65% (Pteropus conspicillatus)  . One-waycephalus .Bats were sampled in differing locations in Australia  and in Indonesia, Timor [n = 20], Papua New Guinea (PNG) [n = 16] and Africa (Tanzania [n = 8] and the Gulf of Guinea [n = 12]). Antibodies reacting to HerPV were detected in bat sera from all countries examined. The prevalence ranged from 13% (PNG) to 70% (Indonesia) . One-wayEidolon helvum) to 48% (Pteropus conspicillatus). One-way ANOVA calculations did not reveal any significant differences in the ratio between detection of reactive antibodies and their ability to neutralize HerPV in bat sera from differing species.All bat sera found reactive in IIFAT screening were subjected to serum neutralization tests. The percentage of bat sera causing neutralization of HerPV ranged from 0%  to 33% (Victoria). One-way ANOVA calculations did result in significant differences in the detection of reactive antibodies in bat sera from Australia in comparison to Indonesia (**) and Africa (**) .Pteropus conspicillatus (78–80%), while the lowest number was observed in Pteropus poliocephalus (10%)  were tested for the presence of henipavirus-binding antibodies by the Luminex binding test. There were variations in the percentage of Hendra virus positives depending on the origin of the sera, ranging between 10% in Victoria to 67% in the Northern Territory and 64% in Queensland, respectively; reactivity to Nipah virus had a similar range with 19% in Papua New Guinea and 75% in the Northern Territory. We found the highest number of positive sera from 39 were t) Tables .Rubulavirus isolated from bats. The initial phylogenetic tree, comprising the short HerPV RdRP sequence, showed HerPV clustering to Archimota virus 2 from Africa [Rubulavirus. This is further supported by the low antibody cross-reactivity to Menangle virus or Tioman virus, the lack of cross-neutralization and the phylogenetic analysis demonstrating that HerPV is distinct from the other bat-derived rubulaviruses. The donor dependent nature of the sera, coupled with different methods of antibody production  would most likely explain the variability in reactions observed with the anti-Menangle virus sera from pigs and rabbits. The ability of HerPV to grow in different cell cultures indicates a potential opportunistic broad host range for this novel virus, as with Hendra virus. Of all cell lines tested, it was only primary bat brain and spleen cells in which no replication could be detected.Hervey virus (HerPV) is a novel bat paramyxovirus named after the place of its first isolation, Hervey Bay in QLD, Australia. The virus is similar in genome organization, length and protein annotation to other proposed members of the genus m Africa . In contThe Hendra virus outbreaks in 2011 were exceptional in many ways. Distinct outbreaks were more numerous than in the other years. Outbreaks were located further south and west than previously reported, and 17 of them were in close proximity to each other (40–700 km). Bat urine was sampled from bat colonies adjacent to a number of outbreak sites. Hendra virus was successfully isolated from a number of these urine samples, demonstrating that Hendra virus was indeed circulating in these colonies. Along with a number of other paramyxoviruses, Hendra virus was detected in urine from all three locations where HerPV was isolated. HerPV was detected in under-roost urine samples from three different locations approximately 700 km apart over a 16-day period: in Boonah on 11 July 2011 and 26 July 2011, in Hervey Bay on 20 July 2011 and in Nambucca Heads on 27 July 2011.Pteropus scapulatus.To investigate the diversity of HerPV in pteropid bats, we tested reactivity of sera from a variety of pteropid species to HerPV by IIFAT and the sera positive by IIFAT were then subjected to HerPV SNT. All four Australian pteropid bat species showed similar high reactivity to HerPV at 43–65% Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


Information on costs of managing adverse events (AEs) associated with current treatments in metastatic melanoma is limited. This study estimates costs of AEs in eight countries: Australia (AU), Canada (CA), France (FR), Germany (GE), Italy (IT), the Netherlands (NL), Spain (ES), and the UK.A literature search was conducted to identify grade 3/4 AEs from product label, published trials, conference abstracts, and treatment guidelines. Resource utilization for the management of each type of AE was determined via interviews with 5 melanoma clinicians in each country. Outpatient and inpatient costs were estimated for each type of AE using country-specific tariffs or government/published sources.In outpatient settings, the most costly AEs per incident included cutaneous squamous cell carcinoma (CSCC) , anemia , peripheral neuropathy , and immune-related diarrhea . In inpatient settings, the most costly AEs per hospitalization included hypophysitis , dyspnea , elevated liver enzymes , CSCC , peripheral neuropathy , and diarrhea .Costs of managing AEs can be significant, and thus effective treatments with lower rates of severe AEs would be valuable.The online version of this article (doi:10.1007/s10198-015-0757-y) contains supplementary material, which is available to authorized users. Melanoma is a globally significant condition, with approximately 200,000 incident melanoma skin cancers and an estimated 46,000 fatalities occurring from advanced forms of the disease in 2008 . About 8Prior to the introduction of newer treatments for metastatic melanoma in 2011, progress in melanoma treatment had been slow and survival rates had essentially been unchanged for decades. Before the introduction of the newer agents, nearly all patients with regional or distant metastases were treated with on-label and off-label older conventional agents as monotherapy or in combinations and regimens. Three of the most commonly used older conventional agents are dacarbazine (DTIC), fotemustine (only in some countries in Europe) and interleukin-2 (IL-2) (only in the US) , 4. NotaIn recent years, significant progress has been made in the fight against metastatic melanoma. Since 2011, six agents have been approved for the treatment of advanced melanoma: ipilimumab, a CTLA-4 antibody; two BRAF inhibitors, vemurafenib and dabrafenib, a MEK inhibitor, trametinib, available for patients with BRAF-mutant melanoma, which occurs in 40–50 % of patients with melanoma; pembrolizumab and nivolumab, PD-L1 inhibitors, were approved in the second half of 2014, and were not yet available at the time this study was conducted , 7.While chemotherapy and IL-2 treatments are most likely to lead to hematologic toxicities such as neutropenia or anemia, BRAF inhibitor studies show greater rates of squamous cell carcinomas and/or keratoacanthoma, and grade 3 or 4 adverse events with the MEK inhibitor treatment include hypertension and rash. Treatment with ipilimumab is associated with a number of grade 3 and 4 adverse events, the majority of which are immune-related, which may involve the gastrointestinal, liver, skin, nervous, endocrine, ocular, or other organ systems .Although trial data demonstrate the extent of AEs experienced on each course of therapy, the economics of managing AEs around the world is not well understood, especially given the different AE profiles from newer therapies. Therefore, this study explores the costs in Italy, Spain, Germany, France, the Netherlands, the UK, Canada, and Australia related to managing the more frequent therapy-related toxicities to better understand the burden of AE-related economic impact.This study estimates payer perspective costs associated management of AEs occurring while patients receive treatment with monotherapy agents that are approved for use and/or referenced in treatment guidelines for first or second line treatment of metastatic melanoma in any of the included countries –14. ProdAppropriate clinical studies from which to identify a list of severe (grade 3 or 4) adverse events associated with the treatment options of interest were collected via a literature search. Searches were conducted in PubMed, and additional information was provided by publicly available sources from relevant professional conferences, and materials referenced in product prescribing information. Search terms included the drug name  along with “melanoma” and “metastases” or “metastatic” or “advanced”, and “clinical trial”. In order to ensure the study captured all relevant AEs across considered therapies, the highest-quality study was selected for each therapy by line of treatment . Study selection criteria prioritized phase 3 studies (unless no phase 3 studies were available), large sample size, and the use of a guideline-recommended dosing regimen. This was done to ensure that AEs were treatment-related, which may be less clear in earlier-phase studies. Severe adverse events, grade 3 and 4 as defined by the National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events (CTCAE), were identified in the selected studies. Any AEs reported in less than 1 % of patients across all selected studies were excluded from the analysis, and additional abnormal lab value AEs were excluded following the first two clinical expert interviews upon indication that no active management would occur. Resource utilization responses during these first two interviews also supported grouping AEs by general treatment approach or degree of management required for later costing, although this did not affect the interviews. An interview guide containing the final list of AEs was compiled by type of therapy to allow clinicians to provide resource utilization answers according to AE etiology where relevant.A total of five melanoma clinical experts were interviewed in each country to document their professional management of each AE. Initially, experts were approached if they had recent publications in the field of advanced/metastatic melanoma, melanoma society or conference participation; some participants were identified with the assistance of recruiting services. Physicians were required to treat at least 5 cases of advanced/metastatic melanoma per month; this number was chosen to allow participation in countries with lower melanoma treatment density , although 10–20 cases per month were preferred in other countries . The individuals were experienced in the fields of medical oncology or dermatology.For each grade 3 or 4 AE per therapy type in the interview guide, physicians were asked to provide details for how they would treat each event. The interview guide was arranged by therapy type, with a list of drugs for which each adverse event occurred to allow physicians to note whether AE treatment would differ by melanoma therapy. Specific questions were translated into local language in Germany, Italy, and Spain, capturing the English-language questions of “what percentage of the time would you hospitalize a patient with this AE,” “for how long would patients be hospitalized” and “for those patients not hospitalized, what outpatient resources would you use: what kinds of tests, procedures or drugs would you recommend, and at what doses or frequencies?” All other interviews were conducted in English.Expert responses from the five physicians in each country were collated to determine the base case approach. Given an AE without a definitive majority (at least three out of five physicians in agreement), agreement between two out of five physicians was used as the default, if the other physician responses differed. Resources with different names but the same indication were considered to constitute physician agreement . Sensitivity analysis calculations were conducted by using any additional or different resources mentioned by clinical experts. In some settings, physicians were reluctant to provide resource use for adverse events they never see in their patients. This was likely because in clinical practice, physicians managed AEs at a lower grade, prior to grade 3 or 4 severity. Results note where this was the only response available.Unit costs for required resources identified by clinical experts were collected according to a public payer perspective. All costs were obtained in 2013 local currency and are inflated to 2014 local currency –17. For In Italy, inpatient costs were an average of the six most populous provinces in Italy using the regional tariffs per DRG. The provinces were Piemonte, Lombardia, Veneto, Lazio, Sicilia and Campania. The most recent tariffs were published in 2009 by the Agenzia Nazionale per i Servizi Sanitari Regionali (AgeNaS), and were assumed therefore to be in current use without further tariff inflation . The outIn Spain, outpatient and inpatient costs were obtained from the OBLIKUE database, which collects all healthcare costs available in Spain, both from government databases and from the literature, and designates the best available cost information per indication . As the In Germany, the inpatient costs were derived from the Universitätsklinikum Münster, using the DRG Research Group Medizinisches Management search tool . The outFrench inpatient and drug costs were found in public price lists maintained by the Agence Technique de l’information sur l’Hospitalisation (ATIH) . OutpatiIn the Netherlands, the Tariefapplicatie from the Dutch Healthcare Authority (NZa) was used to identify inpatient and outpatient costs . This soIn the UK, inpatient, short stay, and outpatient costs came from the National Health Service (NHS) National Schedule of Referenced Costs, which reflect the average actual costs to the hospital to provide the activity, and is the preferred source for payer perspective costs in the UK . The cosFor Australia, inpatient costs were found in the Australian Government Department of Health and Ageing Medicare Benefits Schedule Book . OutpatiFor Canada, inpatient costs were from the Ontario Case Costing Initiative (OCCI)—OCCI costing analysis tool . For cosTo calculate outpatient and inpatient treatment of each AE, unit costs were multiplied by default resource utilization suggested by the clinical experts. A sensitivity analysis was conducted by applying the unit costs to non-base case resource utilization patterns in order to explore the range suggested by the interviews.Forty-five publications on the pre-determined therapies were derived from the literature search. From these studies, a high-quality phase 3 study reporting grade 3 or 4 AEs for each line of therapy was chosen. But if the line of therapy was unclear or if a particular line of therapy had only phase 2 results published, phase 2 results were accepted; this was the case for first line ipilimumab and second line IL-2 and vemurafenib. Details of selected papers are reported in Table A total of 30 (grade 3 or 4) AEs were identified from these publications, excluding toxicities that would not receive active management. Following the pilot interviews, resource utilization patterns indicated that some AEs would require a similar management approach or magnitude of treatment. As a result, 17 clusters were created from the list of AEs and the toxicity considered to be the most clinically significant was selected as a proxy for the others; total toxicity management costs were only estimated for the proxy toxicity. The full list of AEs by their groupings is presented in Table The most common grade 3 and 4 AEs included neutropenia, vomiting and anemia for chemotherapy agents. Vemurafenib was most commonly associated with cutaneous squamous cell carcinoma (CSCC)/keratocanthroma, rash and elevated liver enzymes and dabrafenib was more commonly associated with CSCC and pyrexia (fever). Hypertension and rash were most often related to trametinib. Common ipilimumab AEs were immune-related diarrhea/colitis, dyspnea, anemia, vomiting, and hypophysitis less frequently.Base case outpatient costs are in Table Results vary by country, reflecting variation in unit costing across country borders, but trends appeared indicating that hospitalization, outpatient procedures, and certain high-cost medications lead to expensive AE management. Anemia required high-cost outpatient care across all countries except for Germany due to use of erythropoietin and/or blood transfusions. These costs range from €936 (Netherlands) to €1443 (Spain), £730 (UK), CAN$370 (Canada), and AUS$890 . Aside from anemia, other costly outpatient AEs included CSCC in the Netherlands (€1063), Germany (€406) and UK (£720), febrile/afebrile neutropenia for Italy and Spain (€497–755), peripheral neuropathy for Spain (€1289) and immune related diarrhea for Australia (AUS$1121). In the Netherlands and Germany, the necessary outpatient excision of CSCC drove the higher costs for that treatment, while febrile/afebrile neutropenia expenditure was related to use of granulocyte colony-stimulating factors (GCSF). Specialist consultations resulted in the high cost for peripheral neuropathy in Spain while the cost for immunosuppressant treatment contributed to the high cost of immune-related diarrhea in Australia. See Table Hospitalizations were more costly in general, but the most expensive AEs to treat in the inpatient setting include hypophysitis in Spain , Canada (CAN$9735), and Australia (AUS$7231), dyspnea in Germany (€9077), febrile neutropenia in the UK £4444), elevated liver enzymes in France (€6913), fever in Italy (€3433), and peripheral neuropathy in Netherlands (€6977). Febrile neutropenia was in the top three most expensive hospitalizations in multiple countries, as well, including Germany, Spain, Netherlands, and UK . All Euro-zone hospitalizations exceeded €1000 per episode, while the lowest costs were approximately £1300 in UK, AUS$1900 in Australia, and CAN$3200 in Canada. Detailed results can be found in Table 444, elevAs with base case results, country variation in sensitivity analysis results exists as well. Due to cost-conserving approaches to treatment, low ranges did not differ importantly from base case results across the countries, but more expensive options were suggested for management of certain AEs, with some notable trends, such as higher cost medications suggested in lieu of base case treatment. Cost of treating febrile or afebrile neutropenia could rise based on the use of GCSF during treatment in Italy, Germany, France, and the UK, with associated excess costs of ~€900, ~€1400, ~€3800, and ~£240, respectively. In Canada, use of antibiotics against febrile neutropenia could inflate costs by approximately CAN$250. Use of erythropoietin products to treat anemia also resulted in higher costs in Italy, France, Spain, and the Netherlands (over €1000 more). The use of mycophenolate in Australia could increase the cost of treating rash and palmar plantar hyperkeratosis by as much as AUS$1000, as well.Adding specialist consultations, facility stays, or procedures raised the total costs per AE as well. Additional consultations increased the cost to treat neutropenia and immune-related diarrhea in Canada by over CAN$1000 per episode. Short facility stays in the UK led to higher costs of managing grade 4 diarrhea and neutropenia of approximately £550-750. Transfusion procedures (thrombocyte or leukocyte) were costly additions to treatment for hematological toxicities in the Netherlands and France .Managing AEs resulting from metastatic melanoma treatment can incur significant costs, even accounting for variation in country-level or individual differences in treatment approach. Payer-perspective costs of total resources administered or recommended by a medical oncologist or dermatologist , ranged from the cost of an excess physician visit to high hospitalization costs. This is a conservative estimate of the resources used to treat toxicities related to metastatic melanoma treatments; if other specialists are consulted to manage specific events, for instance, additional resources may be required that this study does not capture.Although differences existed across countries in itemized costs and treatment approaches, as well as in extent of additional specialist consultation , some AE management cost trends were apparent. In the outpatient setting, anemia was one of the most costly AEs to treat in most countries examined in this study. CSCC and immune-related diarrhea were also costly in several countries. In the inpatient setting, hypophysitis, elevated liver enzymes, peripheral neuropathy, dyspnea, diarrhea, CSCC, and febrile neutropenia incurred higher costs relative to other AEs related to melanoma treatments. AEs such as headache and rash were among the least costly hospitalizations. These trends reflect the higher unit costs associated with hospitalization, certain expensive medications, and procedures. Some of the common AEs associated with ipilimumab  and vemurafenib/dabrafenib (CSCC and elevated liver enzymes) are among the most expensive AEs.A recent Italian retrospective cohort study investigated the cost of unresectable stage III or stage IV melanoma, providing some support for our findings in the Italian setting . AlthougThis study was designed to demonstrate that managing each AE associated with melanoma treatment can be costly; specific values presented in this study for multiple country settings can be used in future analyses, including modeling to assess the overall burden of managing AEs over the course of therapy. Although not covered in this study, a recent US retrospective database analysis investigated the overall AE costs associated with several different melanoma therapies . The authors found that hemic and lymphatic disorders and effects incurred the highest costs across therapies. Adjusted mean costs for AEs were highest for dacarbazine and lowest for vemurafenib among the treatments considered .This study does have some limitations, however. The study does not include pembrolizumab or nivolumab as these products were not approved at the time of study completion; however, AE profiles for these drugs do not require significant additions to the already comprehensive list assessed in this analysis. Moreover, although clinical trials may not capture the set of real-world AEs, this source was considered the most comprehensive and consistent source across therapies under consideration and thus used to determine the list considered in this study. Also, the resource use estimates in this analysis may not represent the full spectrum of clinical practice patterns or duration of required management across all patients. This study depended on physician interviews to derive resource use information, and five clinical experts may not represent a nation’s practice patterns as a whole. However, their responses serve to indicate some approaches to management, and the use of clinician agreement for a base case estimate with sensitivity ranges to explore variation creates a picture of the magnitude of financial impact in each country. Additionally, only resources that would be used from the medical oncologist or dermatologist’s perspective were included. These estimates may not include additional resources a specialist requires to continue management of an AE, leading to potential underestimation of the total costs. However, higher costs would only further support the suggestion of sizeable AE-related cost burden. Regarding the use of specific data sources, the cost estimates used in Spain included both government costs and literature-based values, as derived from the OBLIKUE database. Given the limited nature of cost information in Spain, the OBLIKUE database compiles the best available data sources, and therefore the costs used in this study constitute the best representation of the public payer perspective in Spain. Finally, this study does not explore any indirect costs associated with advanced or metastatic melanoma, as this analysis took the payer perspective in each country. Therefore, costs identified here likely underestimate the true burden of melanoma in these countries.In light of these limitations, the findings from this analysis should be interpreted with caution; results can be considered an indication of the extent of the economic issue, as well as an initial understanding of the AE-specific costs per AE that payers may face. As one of the only close examinations into the size of global impact per AE, this initial financial picture can help inform future evaluations of the clinical and cost-effectiveness of treating patients with metastatic melanoma while serving as an impetus for further study. As information continues to accrue for the newest therapies in the coming months and years, opportunities will exist to complement this effort via future analysis of real-world resource use and costs related to treating toxicities as a result of metastatic melanoma therapy.Supplementary material 1 (DOCX 107 kb)Below is the link to the electronic supplementary material.


The presence of CRISPR spacers was associated with a ∼50% reduction in single-cell coinfection in a marine bacteria, despite the absence of exact spacer matches in any active infection. Collectively, these results suggest the environment bacteria inhabit and the interactions among surrounding viruses are two factors consistently shaping viral coinfection patterns. These findings highlight the role of virus–virus interactions in coinfection with implications for phage therapy, microbiome dynamics, and viral infection treatments.Infection of more than one virus in a host, coinfection, is common across taxa and environments. Viral coinfection can enable genetic exchange, alter the dynamics of infections, and change the course of viral evolution. Yet, a systematic test of the factors explaining variation in viral coinfection across different taxa and environments awaits completion. Here I employ three microbial data sets of virus–host interactions covering cross-infectivity, culture coinfection, and single-cell coinfection  to provide a broad, comprehensive picture of the ecological and biological factors shaping viral coinfection. I found evidence that ecology and virus–virus interactions are recurrent factors shaping coinfection patterns. Host ecology was a consistent and strong predictor of coinfection across all three data sets: cross-infectivity, culture coinfection, and single-cell coinfection. Host phylogeny or taxonomy was a less consistent predictor, being weak or absent in the cross-infectivity and single-cell coinfection models, yet it was the strongest predictor in the culture coinfection model. Virus–virus interactions strongly affected coinfection. In the largest test of superinfection exclusion to date, prophage sequences reduced culture coinfection by other prophages, with a weaker effect on extrachromosomal virus coinfection. At the single-cell level, prophage sequences eliminated coinfection. Virus–virus interactions also  Viruses outnumber hosts by a significant margin . In thisRecent studies of bacteriophages have highlighted just how widespread coinfection is and have started to shed light on the ecology of viral coinfection. For example, although studies of phage host range have shown that some bacterial hosts can be infected by more than one type of phage for some time , mountinThe aforementioned studies, among others, establish that viral coinfection is a frequent occurrence in bacterial and archaeal hosts, but systematic tests of the factors explaining variation in viral coinfection across different taxa and environments are lacking. However, the literature suggests four factors that are likely to play a role: host ecology, host taxonomy or phylogeny, host defense mechanisms, and virus–virus interactions. The relevance and importance of these are likely to vary for cross-infectivity, culture coinfection, and single-cell coinfection.Cross-infectivity has been examined in studies of phage host-range, which have provided some insight into the factors affecting potential coinfection. In a single bacterial species there can be wide variation in phage host range , and thuinsitu protective effects of CRISPR on cells, which should now be possible to decipher with single-cell genomics.Culture and single cell coinfection, require cross-infectivity, but also require both the bacteria and infecting viruses to allow simultaneous or sequential infection. Thus, in addition to bacterial phylogeny and ecology, we can expect additional factors shaping coinfection, namely bacterial defense mechanisms and virus–virus interactions. An extensive collection of studies provides evidence that bacterial and viral mechanisms may affect coinfection. Bacteria, understandably reluctant to welcome viruses, possess a collection of mechanisms of defense against viral infection , includiViruses also have mechanisms to mediate infection by other viruses, some of which were identified in early lab studies of bacteriophages . An examHow do ecology, bacterial phylogeny/taxonomy, bacterial defense mechanisms, and virus–virus interactions explain variation in estimates of viral coinfection?What is the relative importance of each of these factors in cross-infectivity, culture coinfection, and single-cell coinfection?Do prophage sequences limit viral coinfection?Do ssDNA and dsDNA viruses show evidence of preferential coinfection?Do sequences associated with the CRISPR-Cas bacterial defense mechanism limit coinfection of single cells?Here I employ virus–host interaction data sets to date to provide a broad, comprehensive picture of the ecological and biological factors shaping coinfection. The data sets are each the largest of their kind, providing an opportunity to examine viral coinfection at multiple scales, from cross-infectivity , to culture coinfection  and single-cell coinfection  to answer the following questions:The results of this study suggest that microbial host ecology and virus–virus interactions were consistently important mediators of the frequency and extent of coinfection. Host taxonomy and CRISPR spacers also shaped culture and single-cell coinfection patterns, respectively. Virus–virus interactions served to limit and promote coinfection.I assembled data collectively representing 13,103 viral infections in 6,564 bacterial and archaeal hosts from diverse environments . These dThe first data set on cross-infectivity  is composed of bacteriophage host-range infection matrices documenting the results of experimental attempts at lytic infection in cultured phage and hosts . It compn = 4,964 records) by consulting strain databases  and the primary literature.The second data set on culture coinfection is derived from viral sequence information mined from published microbial genome sequence data on National Center for Biotechnology Information’s (NCBI) databases . Thus, tThe third data set included single-cell amplified genomes, providing information on coinfection and virus–virus interactions within single cells . This gehttp://dx.doi.org/10.6084/m9.figshare.2072929).A list and description of data sources are included in To test the factors that potentially influence coinfection—ecology, host taxonomy/phylogeny, host defense mechanisms, and virus–virus interactions—I conducted regression analyses on each of the three data sets, representing an increasingly fine scale analysis of coinfection from cross-infectivity, to culture coinfection, and finally to single-cell coinfection. The data sets represent three distinct phenomena related to coinfection and thus the variables and data in each one are necessarily different. The measures of coinfection used were the following: (1) the amount of phage that could infect each host, (2) the number of extrachromosomal infections, and (3) the number of active viral infections, respectively, for cross-infectivity, culture coinfection, and single-cell coinfection data sets. Virus–virus interactions were measured as the association of these aforementioned ongoing viral infections with the presence of other types of viruses (e.g. prophages) or the association of viruses with different characteristics (e.g. ssDNA vs dsDNA). Ecological factors and bacterial taxonomy were tested in all data sets, but virus–virus interactions for example, were not evaluated in the cross-infectivity data set because they do not apply . http://dx.doi.org/10.6084/m9.figshare.2072929).First, to test the potential influence of these factors on cross-infectivity, I conducted a factorial analysis of variance (ANOVA) on the cross-infectivity data set. The dependent variable was the amount of phage that could infect each host, measured specifically as the proportion of tested phage that could infect each host, because the infection matrices in this data set were derived from different studies testing varying numbers of phages. Thus, as the number of phage that could infect each host (cross-infectivity) increased, the potential for coinfection was greater. The five independent variables tested were the study type/source , bacterial taxon (roughly corresponding to Genus), habitat from which bacteria and phages were isolated, bacterial energy source (photosynthetic or heterotrophic), and bacterial association . Geographic origin and phage taxa were present in original the metadata, but were largely incomplete; therefore, they were not included in the analyses. Model criticism suggested that ANOVA assumptions were reasonably met , despitehttp://dx.doi.org/10.6084/m9.figshare.2072929), and habitat , as opposed to viral sequences integrated into the genome that may or may not be active. Thus, as more extrachromosomal infections are detected in a host culture, culture coinfection increases. The five explanatory variables tested were the number of prophage sequences, ssDNA virus presence, energy source (e.g. heterotrophic/autotrophic), taxonomic rank  that may or may not be active. Thus, as the number of active viral infections detected increase, single cell coinfection is greater. The four explanatory variables tested were phylogenetic cluster , ocean depth, number of prophage sequences, and number of CRISPR spacers. I conducted stepwise model selection with AIC  to arrive at a reduced model that minimized deviance.To obtain more detailed information  on the influence of virus–virus interactions on the frequency and extent of coinfection, I analyzed the effect of prophage sequences in culture and single-cell coinfections, and the influence of ssDNA and dsDNA viruses on culture coinfection.The examinations of prophage infections were entirely sequence-based in the culture coinfection and single-cell coinfection data sets. Because only sequence data is used, the activity of these prophage sequences cannot be confirmed. For instance, the prophage sequences in the original single-cell data set were conservatively termed ‘putative defective prophages’ , becauseTo examine the effect of prophage sequences on coinfection frequency in the single-cell data set, I examined cells with prophage sequences and calculated the proportion of those that also had active infections. To determine the extent of coinfection in cells with prophage sequences, I calculated the average number of active viruses infecting these cells. I examined differences in the frequency of active infection between bacteria with or without prophage sequences using a proportion test and differences in the average amount of current viral infections using a Wilcoxon rank sum test.n = 331), using a binomial test. To provide context for this finding  I also examined the prevalence of dsDNA-only and dsDNA-mixed coinfections and compared the proportion of ssDNA-mixed/ssDNA-total coinfections with the proportion of dsDNA-mixed/dsDNA-total coinfections using a proportion test. To examine whether potential biases in the detectability of ssDNA viruses relative to dsDNA viruses (Inoviridae family) have been proposed to underlie previous patterns of enhanced coinfection between dsDNA and ssDNA viruses, I examined the proportion of coinfections involving ssDNA viruses that had at least one virus of the Inoviridae among ssDNA-only coinfections, ssDNA single infections, and ssDNA–dsDNA coinfections.To examine whether ssDNA and dsDNA viruses exhibited nonrandom patterns of culture coinfection, as found by  viruses  could afn = 127 cells of SUP05 bacteria). I examined cells that harbored CRISPR spacers in the genome and calculated the proportion of those that also had active infections. To determine the extent of coinfection in cells with CRISPR spacers, I calculated the average number of active viruses infecting these cells. I examined differences in the frequency of cells undergoing active infections in cells with or without CRISPR spacers using a proportion test. I examined differences in the average number of current viral infections in cells with or without CRISPR spacers using a Wilcoxon rank sum test.In order to obtain a more detailed picture of the potential role of bacterial defense mechanisms in coinfection, I investigated the effect of CRISPR spacers  on the frequency of cells undergoing active infections in the single cell data set (http://dx.doi.org/10.6084/m9.figshare.2072929).I conducted all statistical analyses in the R statistical programming environment  and genen = 127 cells of SUP05 marine bacteria) and were a statistically significant and strong predictor of coinfection.To test the factors that influence viral coinfection of microbes, I conducted regression analyses on each of the three data sets . The explanatory variables tested in each data set varied slightly , but cann = 499) to infect different bacterial hosts  using experimental infections of cultured microbes in the laboratory .Bacterial energy source  and the type of study  were not selected by AIC in final model. An alternative categorization of habitat that matched the culture coinfection data set  was not a statistically significant predictor and was dropped from the model. Results from this alternative categorization  coinfected , followed by engineered  and environmental  ecosystems .Host energy source  and ssDNA virus presence were not statistically significant predictors as judged by AIC model selection. Details of the full, reduced, and alternative models are provided in the n = 127) directly from their habitat  isolated as single cells ( habitat . Single  habitat . The modhttp://dx.doi.org/10.6084/m9.figshare.2072929).The phylogenetic cluster of the SUP-05 bacterial cells  was not a statistically significant predictor selected using AIC model selection. Details of the full, reduced, and alternative models are provided in the n = 127 host cells), but provides better, single-cell level resolution.Based on the results of the regression analyses showing that prophage sequences limited coinfection, and aiming to test the phenomenon of superinfection exclusion in a large data set, I conducted a more detailed analysis of the influence of prophages on coinfection in microbial cultures. I also conducted a similar analysis using the single cell data set, which is relatively smaller , a modest but statistically distinguishable increase over a 0.5 null , cultures with more than one prophage sequence (32.54%) were 2 times less frequent than those with both prophage sequences and extrachromosomal viruses  were infected by 3.25 ± 1.34 viruses, compared to 2.54 ± 1.02 prophage sequences (n = 575); these quantities showed a statistically significant difference . Bacteria with prophage sequences were undergoing active infection by an average of 0.09 ± 0.30 phages, whereas bacteria without prophages were infected by 1.22 ± 1.05 phages , were more likely to have dsDNA or unclassified viruses (70.69%), than multiple ssDNA infections . These coinfections were >2 times more likely to involve at least one dsDNA virus than none . Coinfected host cultures that contained at least one ssDNA virus represented 6.19% of hosts, in line with, and not statistically different from the aforementioned overall detectability of ssDNA viruses . The most numerous coinfections in the culture coinfection data set were dsDNA-only coinfections . The proportion of ssDNA-mixed/total-ssDNA coinfections (0.71) was higher than the proportion of dsDNA-mixed/dsDNA-total (0.19) coinfections . Of all the ssDNA viruses detected (n = 852) in the data set, a majority belonged to the Inoviridae family (90.96%) with the remainder in the Microviridae (0.70%) or their family was unknown (8.33%). The proportion of hosts infected with Inoviridae was highest in dsDNA–ssDNA coinfections (0.96), higher than hosts with ssDNA-only coinfections (0.90) or than hosts with ssDNA single infections (0.86); the difference in these three proportions was statistically different from zero .The presence of ssDNA viruses in host cultures was not a statistically significant predictor of extrachromosomal infections in the regression analysis of the culture coinfection data set based on  that is n = 127 cells of SUP-05 marine bacterial cells) revealed that the presence of CRISPR spacers had a significant overall effect on coinfection of single cells; therefore I examined the effects of CRISPR spacers more closely. Cells with CRISPR spacers were less likely to have active viral infections than those without spacers : 32.00% of cells with CRISPRs had active viral infections, compared to 80.95% percent of bacteria without CRISPR spacers. Bacterial cells with CRISPR spacers had 0.68 ± 1.07 current phage infections compared to 1.21 ± 1.00 for those without spacers , I found that prophage sequences were predictive of limited coinfection of cultures by other prophages, but less strongly predictive of coinfection by extrachromosomal viruses. Furthermore, in a smaller sample of single cells of SUP-05 marine bacteria (n = 127 cells), prophage sequences completely excluded coinfection . In contrast, I found evidence of increased culture coinfection by ssDNA and dsDNA phages, suggesting mechanisms that may enhance coinfection. At a fine-scale, single-cell data revealed that CRISPR spacers limit coinfection of single cells in a natural environment, despite the absence of exact spacer matches in the infecting viruses, suggesting a potential role for bacterial defense mechanisms. In light of the increasing awareness of the widespread occurrence of viral coinfection, this study provides the foundation for future work on the frequency, mechanisms, and dynamics of viral coinfection and its ecological and evolutionary consequences.The compilation of multiple large-scale data sets of virus–host interactions  allowed a systematic test of the ecological and biological factors that influence viral coinfection rates in microbes across a broad range of taxa and environments. I found evidence for the importance and consistency of host ecology and virus–virus interactions in shaping potential (cross-infectivity), culture, and single-cell coinfection. In contrast, host taxonomy was a less consistent predictor of viral coinfection, being a weak predictor of cross-infectivity and single cell coinfection, but representing the strongest predictor of host culture coinfections. In the most comprehensive test of the phenomenon of superinfection immunity conferred by prophages , but ecological factors were retained as statistically significant predictors in all three models. Moreover, when ecological variables were standardized between the cross-infectivity and culture coinfection data sets, the differences in cross-infectivity and coinfection among ecosystem categories were remarkably similar . The presence of CRISPR spacers reduced the extent of active viral infections, even though these spacers had no exact matches  to any of the infecting viruses identified . Thus, the host factors tested herein should be regarded as starting points for future experimental examinations. For instance, host energy source was not a statistically significant predictor in the cross-infectivity and culture coinfection models, perhaps due to the much smaller sample sizes of autotrophic hosts. However, both data sets yield similar summary statistics, with heterotrophic hosts having 59% higher cross-infectivity and 77% higher culture coinfection . Moreoveand enhancing coinfection. First, host cultures and single cells with prophage sequences showed limited coinfection. In what is effectively the largest test of viral superinfection exclusion , host cultures with prophage sequences exhibited limited coinfection by other prophages, but not as much by extrachromosomal viruses. Although this focused analysis showed that prophage sequences limited extrachromosomal infection less strongly than additional prophage infections, the regression analysis suggested that they did have an effect: a statistically significant ∼21% reduction in extrachromosomal infections for each additional prophage detected. As these were culture coinfections and not necessarily single-cell coinfections, these results are consistent with a single-cell study of Salmonella cultures showing that lysogens can activate cell subpopulations to be transiently immune from viral infection  and those that are easy to grow in pure culture. The single cell data set is limited to 127 cells of just one host type (SUP05 marine bacteria) isolated in a particular environment, as opposed to the 5,492 hosts in the culture coinfection data set. This limitation prohibits taxonomic generalizations about the effects on prophage sequences on single cells, but extends laboratory findings to a natural environment. Additionally, both of these data sets use sequences to detect the presence of prophages, which means the activity of these prophage sequences cannot be confirmed, as with any study using sequence data alone. Along these lines, in the original single cell coinfection data set  percentage than contained in ssDNA-only coinfections or ssDNA single infections. Notwithstanding, caution is warranted due to known biases against the detectability of ssDNA compared to dsDNA viruses in high-throughput sequencing library preparation (Second, another virus–virus interaction examined in this study appeared to increase the chance of coinfection. While prophage sequences strongly limited coinfection in single cells, data set . This anoviridae , might boviridae . Filamenoviridae  often cooviridae , allowinparation . In thisparation . HoweverCollectively, these results highlight the importance of virus–virus interactions as part of the suite of evolved viral strategies to mediate frequent interactions with other viruses, from limiting to promoting coinfection, depending on the evolutionary and ecological context .In sum, the results of this study suggest microbial host ecology and virus–virus interactions are important drivers of the widespread phenomenon of viral coinfection. An important implication is that virus–virus interactions will constitute an important selective pressure on viral evolution. The importance of virus–virus interactions may have been underappreciated because of an overestimation of the importance of superinfection exclusion . ParadoxThese results have clear implications, not only for the study of viral ecology in general, but for practical biomedical and agricultural applications of phages and bacteria/archaea. Phage therapy is often predicated on the high host specificity of phages, but intentional coinfection could be an important part of the arsenal as part of combined or cocktail phage therapy. This study also suggests that viral coinfection in the microbiome should be examined, as part of the influence of the virome on the larger microbiome . FinallyVirus Evolution online.Supplementary DataClick here for additional data file.


Poxviruses constitute one of the largest and most complex animal virus families known. The notorious smallpox disease has been eradicated and the virus contained, but its simian sister, monkeypox is an emerging, untreatable infectious disease, killing 1 to 10 % of its human victims. In the case of poxviruses, the emergence of monkeypox outbreaks in humans and the need to monitor potential malicious release of smallpox virus requires development of methods for rapid virus identification. Whole-genome sequencing (WGS) is an emergent technology with increasing application to the diagnosis of diseases and the identification of outbreak pathogens. But “finishing” such a genome is a laborious and time-consuming process, not easily automated. To date the large, complete poxvirus genomes have not been studied comprehensively in terms of applying WGS techniques and evaluating genome assembly algorithms.To explore the limitations to finishing a poxvirus genome from short reads, we first analyze the repetitive regions in a monkeypox genome and evaluate genome assembly on the simulated reads. We also report on procedures and insights relevant to the assembly  of genomes. Finally, we propose a neural network method  to “finish” the process by closing gaps remaining after conventional assembly, as the final stage in a protocol to elucidate clinical poxvirus genomic sequences.The protocol may prove useful in any clinical viral isolate (regardless if a reference-strain sequence is available) and especially useful in genomes confounded by many global and local repetitive sequences embedded in them. This work highlights the feasibility of finishing real, complex genomes by systematically analyzing genetic characteristics, thus remedying existing assembly shortcomings with a neural network method. Such finished sequences may enable clinicians to track genetic distance between viral isolates that provides a powerful epidemiological tool. The orthopoxvirus variola virus  was wiped out of a human history in the 1970s, thanks to the success of vaccination. Smallpox eradication counts as one of the greatest triumphs of modern medicine. Before the eradication, smallpox caused from 30 to 35 % case-fatality rates (CFRs). It is a highly contagious and strictly human disease, which caused an estimated 300–500 million deaths during the 20th century alone cth shortest path); w = 0 (w records a weight on an edge).Excite the initial neuron. c = 0  for the excited neuron(s).Record the weight as w.Track current excitation state of each neuron.  and set c = c + 1;4;If c = k; then go to If terminal neuron arrived.2.Else set all outgoing edges’ weights of the excited neurons minus w, and then go to Excite downstream neuron(s).4. [end]Here we present two simple examples to show how to convert the gap-filling task into KSP problem. Let’s start with (what we believe to be) an accurate, genomic sequence of monkeypox virus . From it, we have selected a 55 nucleotide region , and using them to build an analogous graph  is the correct sequence is not trivial. We recommend setting k = 20 for initial gap filling (based on our observations of finishing the clinical data in Table As implied in Fig.


Dear Editor,The recent report on Zika virus infection is very interesting and ther


The history of virology starts with a plant virus, tobacco mosaic virus, being identified as a novel type of infectious agent in 1898 , others are more difficult to work with. Luckily for virologists, many plant viruses have a wide host range and can infect well-characterized model plants, in particular Arabidopsis thaliana, with a genome sequence elucidated two decades ago and extensive genetic resources . An explosion of recent studies has highlighted the complexity of these small genomes and the multifunctional characteristics of plant virus proteins. Well-studied examples of multi-functional viral proteins could include the potyvirus HC-Pro protein, the caulimovirus P6 protein, the cucumovirus 2b protein and the tombusvirus p33 protein. HC-Pro is a protease, an aphid transmission factor and a suppressor of RNA silencing  and they play distinct, yet sometimes redundant roles in the antiviral response  virus. The next phase of plant virology research promises to be exciting as we obtain a better appreciation of the overall impact of viruses not only in model plants, but also in agricultural crops and complex ecosystems.The author confirms being the sole contributor of this work and approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Drawing on the model on financial planning for retirement (FPR), the aim of this work is to explore how parental economic socialization both directly and indirectly affects FPR through the mediation of financial literacy, financial planning decisions and financial management. Data from a sample of 280 participants aged between 45 and 63 years were used. The results show that parental economic socialization directly and indirectly influences FPR. Moreover, parental economic behavior acts as a positive model for the development of financial literacy and skills and for decisions about FPR. All the variables increased the explained variance of FPR. Lastly, we discuss the process by which parental economic socialization is positively related to financial literacy and skills that impact on FPR, indicating some implications and future lines of research. We currently live in a time of great economic changes, and the need to save for retirement is determined by the rise in the cost of living in the future and by the augmentation of the older population  and the mediating role of financial literacy, decisions about FPR and financial management, based on the model on FPR of Hershey et al. . DespiteThe first dimension includes the abilities, knowledge and skills that contribute to differences in people’s cognitive and intellectual capacity to effectively plan and save for retirement. The second dimension includes the motivational forces and the psychological and emotional factors that determine the likelihood that an individual will begin planning and will sustain the activity over time. Finally, certain external influences, including environmental facilitators and constraints, influence effective financial tasking.In the present study, financial literacy was considered as belonging to the capacity dimension, decisions about FPR are within disposition dimension, financial management belongs to the opportunity dimension and parental economic socialization is a distal antecedent of FPR because, as pointed out by some authors, financial lessons learned in childhood could have an impact 20 or 40 years later Dan, . TherefoSavings have been and continue to be the main starting point to describe financial behavior and, normally, people accumulate their savings during their professional careers to have economic resources during retirement. Many recent studies have focused on FPR . Only five organizations finally took part in the study. During May–July of 2017, 355 current employees aged 45 years or older received the questionnaire, a letter explaining the purpose of the study and the data collection procedure, and an envelope to return the survey directly to the Research group. Those employees who agreed to participate completed the questionnaires individually and outside of their workplaces. We eventually collected 302 completed questionnaires, yielding a response rate of 85%. Questionnaires missing more than 25% of the data were excluded, yielding a final sample of 280 Spanish participants.SD = 5.06). The most predominant professional categories were employee (62.5%) and intermediate manager (22.5%). Mean job tenure in the company was 19 years (SD = 10). Concerning education, 40.7% of the sample had university studies, and 30.7% up to high school. Moreover, 29.3% worked in the health setting, and 22.5% in the technological setting. Regarding job status, 89.3% worked full time, and 2.9% were part-time employed. With regard to retirement, 55% thought they could retire at age 65 years and 17% at 67 years.The Ethical Committee of the three authors’ University Bio-ethical Committee of the National Distance Education University (UNED) approved the Project at May 2016. This study was carried out in accordance with the recommendations of Declaration of Helsinki revised in Fortaleza , followed and approved by the Bio-ethical Committee of the National Distance Education University (UNED). All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Bio-ethical Committee of the National Distance Education University (UNED). Confidentiality was maintaining by data being de-identified prior to data analysis by the deletion of identifiable material. Regarding gender, 59.3% of the sample was female . The participants’ mean age was 53.08 years .The decisions about FPR were measured by means of the Financial Preparation subscale included in the “Decision to Prepare for Retirement Scale” , but the number of dependants and estimated age of retirement correlated negatively but non significantly with FPR. FPR was significantly and positively correlated with parental economic socialization , financial literacy , decisions about FPR , and financial management .The means, standard deviations and correlations of all the variables in this study are shown in Table M = 3.33, SD = 0.78), the lowest score in financial literacy , and FPR obtained a mean of 2.76 (SD = 0.84).With regard to the descriptive statistics of the target variables, the highest score was observed in parental economic socialization , which provides support for Hypothesis 1. However, the values of R2 were not higher than 0.10.We analyzed the relation of parental economic socialization with FPR, both directly and considering the variables financial literacy, decisions about FPR and financial management as mediators. To test the first hypothesis of the study, linear regression analysis was conducted, entering the control variables in the first step , and parental economic socialization was entered in the second step (Model 2). The first step was nonsignificant, but in the second step, parental economic socialization predicted FPR positively and significantly , decisions about FPR , and financial management . Likewise, with a 95% confidence level, the confidence intervals (CI) did not include the value 0 , so the indirect effect was significant. Therefore, the results confirmed that financial literacy, decisions about FPR and financial management mediated the relation between parental economic socialization and FPR, which confirms Hypotheses 2–4 . This coincides with the idea that what we learn from our parents and what they teach us about economic issues and savings has an impact on the decision to plan the financial aspect of retirement, and this decision is the first step of planning for retirement. Thus, Koposko and Hershey  find thaFourth, it should not be forgotten that various works indicate that the family is not the only influence in financial decisions, but instead they are also influenced by comparisons with peers . In a similar vein, it has been observed that financial management is related to people’s financial attitudes  on FPR because, although parents are the first source of influence, other models of financial behavior may influence throughout adolescence and adulthood, and it would be appropriate to study the interaction between all of them, as outlined above.In the same vein, future research would consider that parental economic socialization should interact with emotions or other individual features  and is usually the “bread winner or head of household” and decide more on family economic issues. Or, on the contrary, it is already mothers (with their double role as housewives and paid workers) who are increasingly influencing this. Due to gender differences, perhaps mothers tend to be less charged and therefore less influential on these issues, as an anonymous reviewer suggested.Additionally, for future research, a qualitative sample could be used based on some in-depth interviews or discussion groups for children/youth or adults. These techniques would allow us to obtain opinions on the values, attitudes and expectations  of these groups of people on important subjects that are discussed here: savings/spending, consumption and consumerism, need for preparation and training for retirement economic security) from childhood to improve intergenerational relations, need for savings for reasons of leisure, health, future care, etc. Especially due to the fact that the family or the State can no longer cover this service anymore  or at the population level (e.g., economic crisis, changes in the system of pensions; Han et al., To sum up, this work supports earlier studies that show that parental financial behavior impacts on children’s FPR, and it is a positive model for the development of financial literacy and skills and for decisions about FPR. Thereby, the findings support the idea that FPR can and must start at youth because we have the tools for this purpose, and delaying FPR could reduce the likelihood of success. Hence, this initial learning, complemented with others developed during adulthood, is of great relevance for FPR.FP, IJ and GT made substantial contributions to the conception or design of the work; drafting the work or revising it critically for important intellectual content; final approval of the version to be published; agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Raphanus sativus) to wild (Raphanus raphanistrum) radish populations. Both radish species were grown together in relatively dry (no rain), relatively wet (double rain), or control soil moisture conditions in Ohio, USA. We measured wild and crop radish life-history, phenology and pollinator visitation patterns. To quantify hybridization rates from crop-to-wild species, we used a simply inherited morphological marker to detect F1 hybrid progeny. Although crop-to-wild hybridization did not respond to watering treatments, the abundance of hybrid offspring was higher in fruits produced late in the period of phenological overlap, when both species had roughly equal numbers of open flowers. Therefore, the timing of fruit production and its relationship to flowering overlap may be more important to hybrid zone formation in Raphanus spp. than soil moisture or pollen vector movements.Since plant mating choices are flexible and responsive to the environment, rates of spontaneous hybridization may vary across ecological clines. Developing a robust and predictive framework for rates of plant gene flow requires assessing the role of environmental sensitivity on plant reproductive traits, relative abundance, and pollen vectors. Therefore, across a soil moisture gradient, we quantified pollinator movement, life-history trait variation, and unidirectional hybridization rates from crop ( As climate change transforms environments , it can 2 has increased the earth’s surface temperature )rviflora ) or larganistrum ) more frmoisture , 46. ThuR. raphanistrum and its cultivated relative (R. sativus) to investigate the effects of environmental variation  on hybridization rates, as well as how these rates may be affected by local ecological context . R. raphanistrum is a well-established model system in studies of plant evolution and ecology that has been used to evaluate the ecological consequences of crop-to-wild gene flow , 48R. raw e.g., , 49). We. WeR. raWhat is the hybridization rate between crops and their wild relative, and under which environmental conditions is the hybridization rate maximized?Does soil moisture affect hybridization rate by altering plant size or age at reproduction, floral synchrony, or pollinator movement between parental species?A basic understanding of how the environment, specifically soil moisture, is likely to impact hybridization rates is imperative to assess potential ecological consequences of climatic variation and global change. We discuss the potential implications of climate change for interspecific hybridization with a special focus on the introgression of crop alleles into weed populations.R. sativus) is an annual, cosmopolitan root vegetable. Wild radish, or jointed charlock (R. raphanistrum L.), is a closely related species, differentiated from the cultivar by a chromosomal translocation and a suite of morphological characters )R. sativus [R. raphanistrum [Pollinators often discriminate among flowers within and between species based on floral morphology, which can be highly sensitive to both abiotic and biotic variation . Since anistrum . We hypoR. sativus) and its wild, weedy relative (R. raphanistrum), but generally, risk assessments have only considered physical and phenological separation, as well as genetic compatibilities [R. sativus have been shown to range from 14–100% and generally decreased with increasing distance, exhibiting a typical leptokurtic distribution [Many factors could influence rates of hybridization between cultivated radish Click here for additional data file.S1 FigLeast squares mean values (±SD) across plots within a watering treatment are represented by black dots.(DOCX)Click here for additional data file.