First isolated in 1947 from a monkey in the Zika forest in Uganda, and from mosquitoes in the same forest the following year, Zika virus has gained international attention due to concerns for infection in pregnant women potentially causing fetal microcephaly. More than one million people have been infected since the appearance of the virus in Brazil in 2015. Approximately 80% of infected patients are asymptomatic. An association with microcephaly and other birth defects as well as Guillain-Barre Syndrome has led to a World Health Organization declaration of Zika virus as a Public Health Emergency of International Concern in February 2016. Zika virus is a vector-borne disease transmitted primarily by the Aedes aegypti mosquito. Male to female sexual transmission has been reported and there is potential for transmission via blood transfusions. After an incubation period of 2–7 days, symptomatic patients develop rapid onset fever, maculopapular rash, arthralgia, and conjunctivitis, often associated with headache and myalgias. Emergency department (ED) personnel must be prepared to address concerns from patients presenting with symptoms consistent with acute Zika virus infection, especially those who are pregnant or planning travel to Zika-endemic regions, as well as those women planning to become pregnant and their partners. The identify-isolate-inform (3I) tool, originally conceived for initial detection and management of Ebola virus disease patients in the ED, and later adjusted for measles and Middle East Respiratory Syndrome, can be adapted for real-time use for any emerging infectious disease. This paper reports a modification of the 3I tool for initial detection and management of patients under investigation for Zika virus. Following an assessment of epidemiologic risk, including travel to countries with mosquitoes that transmit Zika virus, patients are further investigated if clinically indicated. If after a rapid evaluation, Zika or other arthropod-borne diseases are the only concern, isolation  is unnecessary. Zika is a reportable disease and thus appropriate health authorities must be notified. The modified 3I tool will facilitate rapid analysis and triggering of appropriate actions for patients presenting to the ED at risk for Zika. Following closely after the 2014 Ebola outbreak, due to concerns for rapid spread and a link to birth defects, the World Health Organization (WHO) declared Zika virus a Public Health Emergency of International Concern (PHEIC) on February 1, 2016, making it only the fourth time in history for such a declaration.Constitute a public health risk to other states through the international spread of disease, andPotentially require a coordinated international response.It implies a situation that is –Serious, unusual or unexpected,Carries implications for public health beyond the affected state’s national border, andMay require immediate international action.WHO declared prior PHEICs for the April 2009 H1N1 pandemic, in May 2014 with the resurgence of polio after its near-eradication, and in August 2014 in response to the Ebola outbreak in West Africa.The Zika declaration is unprecedented as it is the first time that a vector-borne disease, i.e. an infection that is not generally transmitted from person-to-person, has been deemed a PHEIC. Vector-borne diseases, including malaria and dengue, account for more than 17% of all infectious diseases and cause more than one million deaths annually.Zika virus is a RNA flavivirus related to dengue, yellow fever, West Nile, and Japanese encephalitis viruses, but not to chikungunya, which is a togavirus. It was first isolated in 1947 from the serum of a febrile sentinel rhesus monkey kept in a cage on a wooden platform in the tree canopy of the Zika forest in Uganda, in which the investigators were studying yellow fever transmission.This paper describes the adaptation of the identify-isolate-inform (3I) tool  as it has been detected in fetuses, amniotic fluid, and semen. These patients, especially if they have been in, or are considering travel to, areas with documented Zika transmission, may present to the ED requesting information and testing.Zika infection is asymptomatic in 80% of cases. When symptomatic, it is typically described as a mild dengue-like illness, manifested by two or more of the following: sudden onset fever, conjunctivitis, arthralgias, and maculopapular rash . MyalgiaZika virus is transmitted by the bite of the Aedes aegypti mosquito and likely also Aedes albopictus. Mosquito bites account for the vast majority of infections, and thus prevention from bites is the principle protective measure. In addition, Zika has been detected for prolonged periods in semen and documented cases of sexual transmission are increasing.Patients should be assessed for pregnancy as well as for neurological symptoms and signs indicating possible GBS. While the situation is rapidly evolving, as of March 2016, laboratory testing is only available through the Centers for Disease Control and Prevention (CDC) and several state and territory health departments. Generally, testing is not recommended for asymptomatic persons, with the exception of pregnant women 2 to 12 weeks after travel to areas with ongoing Zika virus transmission.The differential for Zika virus infection is broad as many diseases present similarly. In patients who have traveled to, or lived in, an area with ongoing Zika transmission within 14 days, chikungunya and dengue viruses, as well as malaria, leptospirosis, and rickettsial infections, are among the other infections that should be considered. Co-infection with dengue and Zika has been reported. It is likely that some travelers to Zika endemic areas will be found to be co-infected with other pathogens acquired from arthropod bites, especially malaria. Distinguishing these infections from one another based on clinical symptoms and signs can be difficult, but malaria is not typically associated with rash or conjunctivitis.Treatment for Zika virus infection is entirely supportive as no specific antiviral therapy is yet available. Aspirin and nonsteroidal anti-inflammatory drugs should be avoided until dengue can be ruled out, to reduce the risk of hemorrhage. As noted, the vast majority of cases (approximately 80%) are asymptomatic; most of the remainder of patients have self-limited illnesses, with symptom resolution within seven days.While under development, as of early 2016, no vaccine against Zika virus exists. Avoidance of travel to Zika-endemic areas or protection against mosquito bites is the best prevention. Mosquitoes that spread Zika bite mostly during the daytime. Preventative measures include the following:Use approved insect repellents (DEET and permethrin are safe and effective for pregnant women when used in accordance with the product label)Use window and door screens, or stay in air-conditioned buildingsWear long-sleeved shirts and long pantsUse a mosquito bed net.For patients with known or suspected Zika virus, prevent spread to others by avoiding mosquito bites . Furthermore, infected men should take measures to avoid sexual transmission of virus to their partners by abstaining, using condoms, and refraining from anal intercourse and fellatio. Only male to female sexual transmission has been reported as of early 2016.Admission criteria for patients who are at risk for Zika virus are similar to those for any other patient. Most patients can be managed conservatively as outpatients; however, those with complications such as GBS may require hospitalization.The identify-isolate-inform (3I) tool, initially developed for Ebola virus disease and subsequently adapted for measles and MERS, can be modified for the ED evaluation and management of patients under investigation for Zika . As mostThe first step in using the Zika 3I tool is to identify patients with a possible Zika exposure within 14 days of symptom onset. This involves taking a travel history and assessing whether the patient was bitten by mosquitoes or has had sex with a patient with Zika virus. If the patient lives in, or has traveled to, areas with ongoing Zika virus transmission within two weeks, assess whether two or more of the following symptoms are present: sudden fever, maculopapular rash, arthralgias, and conjunctivitis. If the epidemiologic and symptom screens are positive, or if a pregnant patient requests testing within 2 to 12 weeks after potential exposure, investigate for the presence of Zika virus by serologic (antibody) testing.While every patient presenting to the ED should be assessed for the potential to transmit disease The second “I” in the 3I tool can be considered to be “investigate” rather than “isolate” if diseases that are contagious from person-to-person are not being considered. “Isolate,” however, remains a useful term to remind the healthcare providers who initially assesses the patient to consider whether isolation precautions are needed, as patients may present with similar symptoms associated with other infectious diseases that are contagious from person to person. In addition, to avert acquiring disease, people should “isolate” from mosquito vectors to prevent infection and, if infected, to prevent potential transmission to others.Patients presenting in high-risk areas of the U.S. should be advised to use precautions such as staying indoors in an air-conditioned environment and wearing long-sleeved shirts and long pants. On March 16, 2016, the U.S. National Center for Atmospheric Research published a public release on potential Zika virus risk estimated for 50 U.S. cities indicating that “summertime weather conditions are favorable for populations of the mosquito along the East Coast as far north as New York City and across the southern tier of the country as far west as Phoenix and Los Angeles.”The final action of the tool is to “inform.” On January 21, 2016, U.S. government regulations authorized CDC to receive case notifications for arboviral illnesses and Zika virus, adding these entities to the list of nationally notifiable infectious diseases. In addition to notifying the hospital infection prevention and control team, emergency physicians should ensure that suspected Zika cases are reported to appropriate health authorities. Patients who do not meet medical criteria for admission should be informed about measures to take to limit the risk of virus transmission, e.g. to avoid sexual activity, mosquito bites, and blood donation.Zika virus disease will likely have substantially less direct impact on ED operations than other emerging infectious diseases such as the 2009 H1N1 influenza epidemic and 2014 Ebola virus disease outbreak. Nevertheless, due to worldwide media attention and an evolving situation, it is important for emergency personnel to be familiar with the basic principles of assessment and management of Zika virus. In particular, women who are pregnant (or considering pregnancy) and have been in (or are considering travel to) areas with ongoing Zika virus transmission, as well as women who have had sex with a male who has travelled to a region of endemic transmission, may present to the ED seeking testing and other advice. CDC issued an unprecedented travel advisory for pregnant womenPatients requiring emergency blood transfusions will need to be counseled about the risks and what measures have been taken to prevent them from contracting Zika virus. Pregnant women are likely to be particularly concerned about receiving blood. Finally, there is the potential for shortages to the blood supply due to a decrease in eligible donors, particularly as Zika virus becomes more widespread over time and during warmer weather.As with all contagious infectious diseases, the question of when to use the public health tools of quarantine and isolation is critical.29Zika virus has a massive economic impact in many sectors, including healthcare. Thus, there may be a shift of resources away from other critical areas of healthcare operations and research. For example, while not approved, in the U.S. alone, President Obama requested 1.8 billion USD in emergency Zika funding.30Zika is an emerging infectious disease that has gained worldwide attention in large part due to its association with fetal microcephaly and rapid global spread. As with any novel infection, it is important not only to identify and treat individual patients, but also to protect healthcare providers and the public health. The identify-isolate-inform (3I) tool is an instrument that can be used real-time on the front lines to rapidly detect and manage patients at risk for Zika virus disease presenting to the ED. Use of the 3I tool will assist emergency physicians in performing rapid and appropriate screening and management and counseling for patients concerned about Zika virus.


Talpa europaea) from Belgium was determined. The 3 genomic segments have a total length of 11,979 nucleotides, and nucleotide identities to other Nova viruses are between 80 and 89%.The complete genome sequence of Nova virus, a divergent hantavirus, originating from the kidney tissue of a European mole ( Bunyaviridae family, hantaviruses are enveloped negative-sense single-stranded RNA viruses with a trisegmented genome  in Zala, Hungary . Total RNA was extracted from kidney homogenate with the RNeasy minikit (Qiagen), and viral RNA was amplified using the OneStep RT-PCR kit (Qiagen). Primers for conserved regions were designed based upon alignments of available Nova virus sequences or other insectivore-borne hantaviruses when insufficient sequence data were present. PCR amplicons were purified using ExoSAP-IT (Affymetrix) and sequenced with the BigDye Terminator v3.1 cycle sequencing kit (Life Technologies) on an Applied Biosystems 3130xl Genetic Analyzer.Kidney tissue was obtained from a wild-caught European mole in 2013 in Namur, Belgium, and transferred to RNAThe genome of this strain, designated NVAV BE/Namur/TE/2013/1, has a full length of 11,979 nucleotides with a G+C content of 35.65%. Complete genomic characterization reveals a 1,826-nt S segment (encoding a 428-amino-acid (aa) nucleocapsid protein, but lacking a secondary open reading frame for the NSs protein), a 3,590-nt M segment , and a 6,563-nt L segment . Nucleotide similarity to other Nova viruses ranged from 80 to 89%, 82 to 86%, and 82 to 88% for S, M, and L segments, respectively. Amino acid similarity to other Nova viruses ranged between 97 and 98% (nucleocapsid), 98% (glycoprotein precursor), and 96 to 97% (polymerase). Phylogenetic analyses of full-length S, M, and L segments demonstrated a host-specific clustering with other Nova viruses. Nova virus exhibited a close phylogenetic relationship with shrew- and bat-borne hantaviruses rather than other mole-borne hantaviruses, providing arguments for a complex hantavirus evolution likely involving host switching events.KT004445, KT004446, and KT004447.The complete genome of Nova virus strain BE/Namur/TE/2013/1 has been deposited in GenBank under the accession numbers


Oryctolagus cuniculus) which have a devastating impact on the native Australian environment. RHDV causes regular, short disease outbreaks, but little is known about how the virus persists and survives between epidemics. We examined the initial spread of RHDV to show that even upon its initial spread, the virus circulated continuously on a regional scale rather than persisting at a local population level and that Australian rabbit populations are highly interconnected by virus-carrying flying vectors. Sequencing data obtained from a single rabbit population showed that the viruses that caused an epidemic each year seldom bore close genetic resemblance to those present in previous years. Together, these data suggest that RHDV survives in the Australian environment through its ability to spread amongst rabbit subpopulations. This is consistent with modelling results that indicated that in a large interconnected rabbit meta-population, RHDV should maintain high virulence, cause short, strong disease outbreaks but show low persistence in any given subpopulation. This new epidemiological framework is important for understanding virus–host co-evolution and future disease management options of pest species to secure Australia's remaining natural biodiversity.In Australia, the rabbit haemorrhagic disease virus (RHDV) has been used since 1996 to reduce numbers of introduced European rabbits ( Oryctolagus cuniculus. The causative virus (RHDV), a very small (approximately 7.4 kb) positive-sense single-stranded RNA virus belonging to the genus Lagovirus in the family Caliciviridae  as well as for commercial rabbit producers. In contrast, it has been used as a biological control agent in Australia where introduced wild European rabbits are a severe pest of agriculture and the environment , known since 1984 following outbreaks in domestic rabbits in China, is an acute disease affecting European rabbits Rabbit haemorrhagic disease was introduced into high security quarantine in Australia and, after several years of laboratory testing to ascertain its specificity for European rabbits, experiments commenced in quarantine compounds on Wardang Island 4 km off the coast of South Australia. The virus' escape to the mainland during testing of its field efficacy was embarrassing for the institutions involved, but it quickly became obvious that the virus could spread over long distances and was highly effective in reducing Australia's rabbit populations. Nation-wide, rabbit numbers fell by 60% and declines were even higher in arid areas where no other methods of controlling rabbits are economically feasible  and show how virus diversity has increased over time  following the introduction of the single RHDV variant in Australia  the observed rate of initial spread, (ii) the rate of spread relative to the distribution and distance between recorded rabbit populations and (iii) the seasonal distribution of disease outbreaks on a regional scale. Building upon the results of Kovaliski et al. , who hadWe considered that epidemiological processes could not be determined by simple virus–host interactions alone. Connectivity between rabbit populations (in terms of virus spread), for example, is clearly dependent on insect vectors, most probably flies, and not on rabbit movement between territories. If vector-based connectivity between rabbit populations is strong, virus variants would not necessarily compete within rabbit subpopulations, but rather on a wider regional scale. Natural selection would favour virus variants with the capacity to spread ahead of others into new rabbit subpopulations, where a previous variant had died out, and quickly infect all susceptible rabbits.We see the results from these field observations, molecular and epidemiological studies as valuable for testing the various scenarios proposed earlier by Fouchet et al.  to anticThe initial spread of RHD from Wardang Island to other parts of Australia was monitored using reports of disease outbreaks confirmed by the detection of virus in samples of dead rabbits obtained from each outbreak site. A database was set up and is maintained by Biosecurity South Australia to manage information on dates of collection of dead rabbits, verification of the presence of RHDV through laboratory tests, recording of the exact location of outbreaks (latitude and longitude) to facilitate mapping and measurement of rates of spread  as vectors of myxomatosis, data from experimental sites for rabbit control studies, state fauna surveys and results from the Rabbit Scan project . The Rabbit Scan project is a website for community ad hoc reporting of sites where rabbits are problematic. Using those data, we were able to consider in detail (i) the spatial distances between rabbit populations in which initial RHD outbreaks occurred and the minimum rate at which the virus first spread (km day−1) through the region; (ii) the proximity of known rabbit populations to each other relative to rates of virus spread; and (iii) the occurrence of RHD outbreaks throughout the year within the region selected. It has to be noted that the map reflects only the minimum density of rabbit populations. It is thus conservative; there are many more rabbit populations than those recorded with accurate coordinates.To construct a map of all presently known rabbit populations in the region, we combined information available in the Biosecurity SA database, with Australia-wide records collated by Dr D. Berman associated with release of Spanish rabbit fleas , which is located 50 km north of Adelaide, South Australia. The rabbit population is 3–4 km distant from other rabbit populations, with only a few scattered rabbit warrens in between  as instructed in the manufacturer's protocol.Total RNA was extracted using approximately 30 mg of liver, marrow or spleen (whichever was available). Tissue was placed in a 2-mL tube containing 500 μL QIAzol lysis reagent . The tissue was disrupted in a Precellys homogenizer  using Ø1.4 mm ceramic beads at 5000 rpm for 2 × 10 s. RNA isolation followed the QIAzol lysis buffer protocol (Qiagen). Total RNA was dissolved in 87.5 μL RNAse-free water, cleaned-up using RNeasy spin columns (Qiagen) and finally eluted in 60 μL of RNAse-free water. We measured concentration and purity of the RNA using a Nanodrop 1000 . For each sample, we constructed two independent first-strand cDNA libraries using up to 5 μg of total RNA, Oligo-dTN/dS ratio when compared to the whole capsid (data not shown), and thus, it was logical to concentrate on this most variable section of virus genome.Virus presence was confirmed by polymerase chain reaction (PCR). We amplified a 504-bp fragment (nucleotide position 872–1376 of the RHD virus capsid protein gene) using forward primer rhdNS1f (5′-CGTTTGCCGACATTGACCA-3′) starting at capsid position 872 and reverse primer rhdNS1r (5′-GTGTTCTTACCCACAGGTGC-3′) which ends at capsid position 1357. This fragment covers most of the P2 region (aa 287–449) which forms an external loop of the virus capsid and contains the less conservative regions V1–V6 that are thought to be important for antibody interaction . The thermal profile consisted of an initial denaturation at 94°C for 1 min, 35 cycles of 15 s at 94°C, 30 s at 55°C, 1 min at 72°C with a final extension period at 72°C for 10 min in a T Gradient  or T Professional Thermocycler (Biometra). Positive samples were sequenced bidirectionally using BigDye®; Thermo Fisher Scientific Terminator v.3.1 Cycle Sequencing Kit  and analysed on ABI PRISM 3110xl Automated Genetic Analyser (Applied Biosystems®).Amplifications were run in a final volume of 25 μL including 10–100 ng cDNA, 15 pmol of each primer, 0.8 m−1). The values obtained were then considered together with maps of the distribution of all known rabbit populations throughout the study region to ask whether or not RHDV could readily move from one rabbit subpopulation to another.Database information was extracted on the number of RHDV outbreaks recorded each month from the time RHDV escaped off Wardang Island in October 1995 until December 1996 to determine whether there was credible evidence that the virus could potentially survive by circulating continuously through a wide regional landscape. Database information on dates and locations of RHD outbreaks during these early phases of spread was examined using ESRI®Arc Map 10  to determine how quickly RHDV had spread from Wardang Island to more distant rabbit populations  isolated from rabbits found dead at Turretfield. We used the general time reversible model allowing for a proportion of invariant sites and a gamma distribution of among-site variation with five categories. Bootstrapping based on 1000 resampled replicate ML trees (utilizing NNI branch-swapping) was used to estimate the support for individual nodes. Alignment and molecular evolutionary and phylogenetic analyses were conducted using MEGA version 6  suggesting that it either had spread about 60 km across the sea or that it had taken a much longer route along the coastline. By 17 December 1995, it had reached Tailem Bend on the Murray River about 210 km east of Wardang Island . This must be taken as a minimum rate, because, if RHDV spread from one rabbit population to another, it is unlikely to have spread in a direct line between any two localities. It also implies the virus was transferred by flying insects or birds over 20–100 km of sea and highlighted by the extraordinary arrival of the virus on Spilsby Island.These records indicate a rate of spread of 2–9 km dayThe localities at which infected rabbits were found Fig. by no meVirtually, all recorded populations of wild rabbits in the part of South Australia considered here are within 25 km of other known populations Fig.. This doData so far indicates that RHD outbreaks can occur somewhere on a regional scale throughout the year even if at low, barely detectable levels over the summer months. This potentially allows RHDV to survive at a few localities on a wider regional scale and ensure its availability for spread back into local populations when conditions become favourable again. This is a feasible scenario, given the interconnectivity among rabbit populations, even over large distances, provided by flies or other flying vectors.During our long-term study at Turretfield, rabbits that had died of RHDV were only found during a few weeks in spring and epizootics always started between August and October. These outbreaks were generally of short duration, and no rabbits confirmed to have died from RHDV were found at other times of the year.PCR confirmed the presence of RHDV RNA in 58 of the 63 samples available from dead rabbits collected at Turretfield. There was no evidence that individual rabbits had been infected by more than one virus variant. We identified 24 different RHDV sequences with 2–4 variants per year .Rabbits have a severe impact on the native Australian vegetation and fauna; they are associated with the spread of weeds and introduced plants. It has been estimated that a density threshold of approximately 0.5 rabbits haCalliphora spp, are of particular importance , and rabbit breeding was extended as evidenced by the capture of rabbit kittens in January of both years. However, this does not seem to be an adequate explanation for the wide variability among RHDV variants collected in 1999 because there had been no outbreak of RHD in 1998. Higher variability had also been reported in the first years of spread and a possible explanation for this is that, up to that time, land managers were still making releases of Czech CAPM-V351 RHDV inocula to control their rabbits in Australia  but did not cause an obvious disease outbreak. Since 2006, however, major outbreaks had occurred annually in sharp epizootics.Our results enable us to say that, of the various scenarios proposed by Fouchet et al.  to prediOther recent work with nonpathogenic RCV-A1 in Australia offers a contrasting view, which accords better with the other scenarios put forward by Fouchet et al. . This viOur envisaged model of RHDV circulation within a large meta-population, where virus variants are selected for their capacity to reach and rapidly infect rabbit subpopulations, has extremely important implications for future rabbit management within Australia. Apart from providing one of those rare occasions when epidemiological results from the field have proved useful in selecting an appropriate theoretical model, the combination of field data and theory helps explain why RHDV remains highly pathogenic and continues to cause high mortality in the field despite evidence that rabbits are developing some genetic resistance (Elsworth et al. In planning future rabbit control in Australia, this growing theoretical framework is an important tool when it comes to decision making. The Invasive Animals Cooperative Research Centre is supporting projects in which RHDV variants imported from several countries are being assessed in quarantine to determine whether some may be more effective than current field variants in countering increasing rabbit numbers. While some of these candidate viruses cause high mortality in rabbits selected for resistance to Czech CAPM-V351, it is not a simple case of releasing such viruses without having some supporting theoretical framework to say that they are likely to retain their current virulence and effectively compete with existing field variants of RHDV. Our study provides a framework which should help to explore those future scenarios and judging risks and benefits if new RHDV variants were introduced into Australia.For Europe, these ideas have a different significance because the main aim in Spain, Portugal and France was to reduce RHD impact on wild rabbit populations. One way of deciding whether RHDV is evolving to maintain high virulence, or whether it might be attenuating to less virulent forms, would be to sample RHDV within rabbit subpopulations and compare virus sequences from year to year. According to theoretical expectations, if there is evidence that several virus variants are competing within each rabbit subpopulation, this should result in attenuated, persistent viruses and lesser disease impact in the long run. Whether the interconnectivity through flies as vectors is less important in Europe and consequently results in a difference in selective pressure for virus virulence remains an important question to study.O. cuniculus algirus in Portugal and southern Spain. Pathogens have recently been recognized as an essential and inevitable part of all ecosystems with regulatory functions which can balance the biodiversity of an ecosystem (Lafferty Studies that investigate host–pathogen co-evolutionary processes of infectious diseases under natural conditions are important for many reasons other than the benefits they provide in biological control or because diseases cause problems for threatened wildlife, such as Lafferty . In turnLafferty , and invLafferty . Host–pa


An outbreak of porcine epidemic diarrhea occurred in the summer of 2014 in Ukraine, severely affecting piglets <10 days of age; the mortality rate approached 100%. Full genome sequencing showed the virus to be closely related to strains reported from North America, showing a sequence identity of up to 99.8%. Porcine epidemic diarrhea (PED), caused by PED virus (PEDV) was first recognized in the United Kingdom in 1971 .In May 2014, an outbreak of PED among pigs in the finishing stage of growth was reported in Germany and associated with a PEDV strain very similar to the OH851 variant of PEDV  survived. Disease was less severe in adults, whose appetite returned and diarrhea ceased within 3 days. Abortions occurred immediately following the outbreak in 38% of 1 batch of sows that were at 28–35 days’ gestation; no diagnostic investigation was made to determine the cause. It was not established whether they were a result of the PED outbreak or control methods such as “back-feeding” fecal material to piglets. Abortions did not occur in other groups of sows and have not been reported as a feature of PED outbreaks in North America. The reproductive status of sows at other stages of gestation was not substantially affected by the initial PED outbreak, and no higher return rate was observed. Postpartum sows did not fail to produce milk, but those affected by PEDV had a reduced feed intake and associated reduced milk output. The performance of the pig unit took 20 weeks to return to preoutbreak levels. During that time, a total of 30,000 piglets died, which equates to a loss of 6 weaned piglets per sow per year.The outbreak was controlled by a combination of 2 methods. Lactogenic immunity enhancement was initiated by deliberate reexposure of pregnant sows to infected piglet feces 6 weeks before farrowing. The environmental viral load was reduced by cleaning and disinfection of the area, euthanizing of neonatal piglets, and reduction of transmission by humans and other vectors through enhanced internal biosecurity. http://blast.ncbi.nlm.nih.gov/Blast.cgi) of the 160-nt PCR amplicon revealed the highest similarity (99%) to PEDV strains from the United States and China. PEDV RNA was then subjected to deoxyribonuclease I digestion and converted to cDNA for preparation of sequencing libraries by using a Nextera XT kit . Paired-end sequencing was performed on an Illumina MiSeq. The consensus sequence was obtained by de novo assembly by using the Velvet 1.2.10 algorithm , which indicated the presence of PEDV antigen in the feces. We confirmed these findings at the UK Animal and Plant Health Agency using an in-house PEDV PCR and a commercially available PEDV/transmissible gastroenteritis virus quantitative reverse transcription-PCR kit . A BLAST search (The PEDV Ukraine/Poltava01/2014 strain genome (GenBank accession no. KP403954) is 27823 nt (excluding the 3′ poly A tail). Nucleotide analyses of the full genome of the virus showed the highest similarity to PEDV strains reported in 2013 from the United States; specifically, strains USA/Kansas29/2013 (GenBank accession no. KJ645637.1) and USA/Colorado30/2013 (GenBank accession no. KJ645638.1), with 99.8% nucleotide identity. The nucleotide identity was substantially lower (98.5%) when compared to those of the 2014 strains isolated in Germany, which are similar to another strain isolated in North America, Ohio851/2013 (Mycoplasma hyopneumoniae, Brachyspira hyodysenteriae, mange, toxigenic Pasteurella multocida, porcine reproductive and respiratory syndrome virus, transmissible gastroenteritis virus, Aujeszky’s Disease virus, and classical and African swine fever viruses. Persons on the farm practiced strict biosecurity measures to maintain a specific pathogen free status. No breach in biosecurity could be identified after a review of potential PEDV introduction routes involving pigs or humans or vehicles, equipment, and other fomites; the source of infection remains unknown. However, there were reports of a PED-affected pig farm 1.5 km from this unit, and the potential for unidentified fomite or other transmission from this herd, or another undisclosed infected herd, therefore existed. Windborne transmission has been suggested to explain some outbreaks in the United States (The farm was found to be free from This PED outbreak in Ukraine showed clinical characteristics similar to outbreaks caused by virulent strains of PEDV reported from North America (This outbreak emphasizes the need for decision-makers of countries, pig farms, and allied industries to implement and maintain biosecurity measures to minimize the risk for spread of PEDV to new areas. Early detection of suspected clinical signs on pig farms and, when these occur, prompt testing for PEDV by PCR are also vital (References for outbreaks of porcine epidemic diarrhea virus in 3 countries in the Americas during 2014.


Aeromonas spp., Pseudomonas spp., Shewanella spp., and Morganella morganii, respectively. For the validation of the probes, 16 reference strains from type culture collections were tested by LDR and FT-RDB hybridization using universal arrays spotted onto membranes. In conclusion, the described methodology could be applied for the rapid, accurate, and cost-effective identification of bacterial species, exhibiting special relevance in food safety and quality.Traditional culturing methods are still commonly applied for bacterial identification in the food control sector, despite being time and labor intensive. Microarray technologies represent an interesting alternative. However, they require higher costs and technical expertise, making them still inappropriate for microbial routine analysis. The present study describes the development of an efficient method for bacterial identification based on flow-through reverse dot-blot (FT-RDB) hybridization on membranes, coupled to the high specific ligation detection reaction (LDR). First, the methodology was optimized by testing different types of ligase enzymes, labeling, and membranes. Furthermore, specific oligonucleotide probes were designed based on the 16S rRNA gene, using the bioinformatic tool Oligonucleotide Retrieving for Molecular Applications (ORMA). Four probes were selected and synthesized, being specific for  Microbial spoilage of food is an area of global concern, causing serious foodborne intoxications and resulting in high economic losses for the food producing sector. The microbiological safety of food has become an increased importance for the consumers, as well as for the food producing industry, and regulations that require monitoring and the enforcement of control systems have been established. Research in the food safety and quality field has been continuously focused on the search for sensitive, accurate, rapid, and cost-effective methods to determine potential microbial risks. Conventional cultivation methods and phenotypical tests are still commonly applied in the food microbiological field but are time consuming and labor intensive. Over the past years, the tools for molecular diagnosis have greatly improved and immunoassays, mass spectrometry, and PCR-based technologies have been introduced for microbial identification. Microarrays are powerful, sensitive, and specific high-throughput technologies that allow an accurate identification based on single target detection and can determine subtle differences in the bacterial genomes. Gene arrays can hybridize multiple DNA targets simultaneously and thus have enormous potential for the detection and identification of bacterial species . Thanks Although the basic principle of the hybridization technique is the discrimination between a perfect match and one single mismatch, this specificity is difficult to reach with classical oligoprobe arrays and cross-reactions can lead to false positive results . This isBased on these previous works , the preThe objective of this study was the development of a fast and cost-effective flow-through hybridization technology, based on the high specific LDR approach coupled to universal macroarrays on membranes. The proposed methodology combines the efficiency and specificity of the microarray technique with the rapidity and convenience of flow-through hybridization, being of special interest for microbial identification in the food safety and quality field. The application of this sensitive method would result in the rapid and early detection of food spoilage bacteria and thus contribute to the accurate analysis and control of microbial risks. in silico against reference databases  for their specificity. At the end four probes were selected to carry out the studies of the present work, being specific for the genera Aeromonas, Pseudomonas, Shewanella, and for the species Morganella morganii, respectively. The sequences of the specific probe pairs and the corresponding zipcode positions are listed in For the probe design, up to fifty 16S rRNA gene sequences of bacterial species exhibiting pathogenic or spoilage activity were downloaded from public databases  and aligned with the bioinformatics program ClustalX 2.1 . ConsensFor the validation of the synthesized probes 16 reference strains were tested . BacteriTotal genomic DNA was extracted by means of the DNeasy tissue minikit  and the 16S rRNA gene was amplified as described elsewhere , using tSelected probes were ordered for synthesis . Each probe pair consisted of a common probe with a phosphate modification at the 5′ end and a unique sequence (complementary zipcode) at the 3′ end and a discriminating probe with the specific nucleotide at the 3′ end and a label for detection at the 5′ end. Three different labels were tested: Cy3, Cy5, and biotin.μM. Afterwards, a probe mix was prepared, containing 1 μM of every oligonucleotide of each probe pair. A probe pair complementary to a synthetic DNA template was also added as a ligation control to the probe mix , ampligase , and taq ligase .The lyophilized probes were resuspended in water, obtaining a concentration of 100  mix see . The totμL were prepared, containing 20 μL of the LDR products, 0.1 μM labeled czipcode 66  were diluted with 0.5 M sodium bicarbonate buffer (pH 8.4) to different concentrations  and were dotted at different volumes  at the given positions on the membranes.The amino-modified zipcode oligonucleotides  were rinsed briefly with 0.1 M HCl and then incubated in freshly prepared 16% EDC (N-(3-dimethylaminopropyl)-N-ethylcarbodiimide hydrochloride; Sigma-Aldrich) for 15 min. After drying at air and dotting the oligonucleotides, any remaining active groups were quenched with 0.1 M NaOH for 10 min. Finally, membranes were rinsed thrice with deionized water and air-dried for storage or immediately used for hybridization.One membrane contained nine subarrays, allowing the hybridization of nine samples simultaneously. Two different types of membranes were tested and required different preparation procedures. Nylon Biodyne C membranes  were used without any pretreatment. After dotting the oligonucleotides, membranes were exposed to UV-light (254 nm) for 30 sec to cross-link the oligonucleotides.The Protran BA 85 nitrocellulose membranes  washing the membrane twice with 0.5 mL Solution A to eliminate the unhybridized PCR products.Flow-through hybridization was performed on an HybriMax device   that worWhen working with biotin-labeled probes, color development was carried out as follows: (4) washing the membrane twice with 0.5 mL 0.1 M sodium citrate, (5) incubation with 0.5 mL streptavidin-horseradish peroxidase (HRP) at 37°C for 5 min, (6) washing the membrane twice with 0.5 mL Solution A to eliminate the uncombined peroxidase, (7) washing the membrane twice with 0.5 mL 0.1 M citric acid buffer (pH 5), and (8) color development with 0.5 mL 3,3,5,5-tetramethylbenzidine (TMB) chromogen.The membranes were imaged and analyzed with the ChemiDoc MP imaging system (BioRad).μm resolution by using a ScanArray Lite laser scanning system  with a green laser for Cy3 dye . All images were analyzed visually and statistically, determining average values and standard deviations of the fluorescence intensity.At the same time, the probes were validated by hybridization of the LDR reaction products to a universal microarray on glass slides, following the methodology described elsewhere . One sli in silico against reference databases  with the aim to verify their specificity. Four of the designed probes were selected for the present studies, for being specific for the bacterial species M. morganii and for Aeromonas, Pseudomonas, and Shewanella genera, which are of special interest in seafood spoilage.For the probe design, we considered DNA sequences of the most important food spoilage and pathogenic bacterial species, using the 16S rRNA gene as target. Consensus sequences were determined for every species and/or genus of interest and discriminating nucleotides were determined with the computer software ORMA. The list of specific fragments of 25–60 bp was reviewed and the potential candidate probes were tested in vitro by hybridization to universal arrays (UA) on glass slides, as described elsewhere [Bacillus cereus ATCC 14893 and Staphylococcus aureus ATCC 9144) were analyzed as negative controls, resulting in no fluorescence signal against any of the tested probes.Before applying the synthesized probes in the flow-through hybridization approach, they were testedlsewhere . The desThe principle of the flow-through hybridization approach is based on the methodology of reverse dot blot (RDB) hybridization in that the targeting molecules flow through the immobilized probes within the membrane fibers. This reduces the time needed for hybridization from hours to minutes and also simplifies the washing steps, since any unbound molecule is removed easily by passing through the membrane.In the present study, we applied for the first time the LDR-based methodology to a flow-through hybridization strategy. First, with the aim of optimizing the protocol, a number of hybridizations were carried out under different conditions only with the positive controls for hybridization (66) and ligation (63). The hybridization control is a labeled synthetic oligonucleotide that contains a DNA sequence that is complementary to the zipcode 66 spotted onto the membrane: a positive signal at this position indicates that the hybridization process is successful. The ligation control includes a synthetic oligonucleotide and a probe pair that is complementary to the DNA sequence of the synthetic template. This probe also contains a DNA sequence that is complementary to the zipcode 63 spotted onto the membrane: a positive signal at this position indicates that the ligation reaction takes place as expected. The scheme of the spotted zipcode oligonucleotides is shown in μM and 0.1 μM ((i) and (ii) in μM gave dots with unacceptable levels of intensity. The best results were obtained with biotin labeling  (data not shown). In addition, the methodology was also successfully applied for mixtures of amplified DNA from reference strains corresponding to different species  exhibits higher sequence variability, allowing the design of specific probes for the successful identification of bacterial foodborne pathogens –23. In ocolitica , 25. HowThe methodology applied in the present study was based on a different microarray technology that combines the high specific ligase detection reaction (LDR) and the multiple, high-throughput DNA analysis of a microarray platform. The LDR approach has the ability to differentiate two DNA sequences by a single nucleotide polymorphism and requires the design of two oligonucleotide probes specific for each target sequence. In the ligase reaction both probes are hybridized to a present template and ligated only in the case of a perfect match in the discriminating position . A furthThe application of LDR coupled to a universal array (UA-LDR) for microbial identification has been described by various authors –29, espe Aeromonas, Pseudomonas, and Shewanella, as well as for the species M. morganii. The probes were successfully tested in vitro with the UA-LDR approach described by Cremonesi et al. [The present study was based on one hand on the results obtained by these authors and, on the other hand, on the design of further probes using the same bioinformatics tool ORMA. Difficulties were observed for some genera, especially inside the Enterobacteriaceae family, for which species differentiation was not possible due to the high sequence similarity of the 16S rRNA gene and the great variety of closely related bacterial species that are potentially present in food products. Finally, four probes were selected and resulted to be specific for the generai et al. , being sAs mentioned before, the UA-based approach can be easily enlarged by further probes corresponding to other bacterial species with food spoilage character, thus representing an efficient microarray platform with high potential for the accurate bacterial species identification in the food control sector, where a great variety of bacterial species can be present in one food product and need to be detected at low concentrations.However, besides the fast technology development in the microarray field, the transition to potential commercial applications is very slow . The maiIn this sense, although the throughput of macroarrays is moderate as compared to microarrays, the former represent a cost-effective, rapid, accurate, and efficient alternative that do not require specialized equipment and can be used in any well set-up molecular laboratory but still retain the sensitivity and specificity of the microarray technology. Such oligonucleotide macroarrays are based on the reverse dot-blot (RDB) hybridization technique in which specific oligonucleotide probes are immobilized to membranes and afterwards the labeled target DNA is hybridized to the membranes , 32.The application of a flow-through (FT) device for hybridization represents an improvement of the RDB technique. The FT-RDB approach has been described by various authors for specific oligonucleotide detection and resulted to be the most efficient method for molecular hybridization, combining two advanced techniques: flow-through hybridization and simultaneous multiple detection on a macroarray membrane. An active flow directs the sample DNA towards immobilized probes within the membrane fibers, increasing the diffusivity and local reaction concentration of DNA and allowing the close contact of DNA probes with the immobilized DNA inside the membrane pores. Conventional dot-blot hybridizations need to be incubated for several hours in plastic bags or glass tubes and require large volumes to cover the whole membrane, which decreases the concentration of DNA available for binding to the probes, also decreasing active interactions between complementary strands considerably . The FT-The flow-through hybridization device HybriMax used in the present study has been patented by Tam . BesidesSalmonella spp., Brucella spp., E. coli O157:H7, Clostridium botulinum, B. cereus, Clostridium perfringens, Vibrio parahaemolyticus, Shigella spp., Y. enterocolitica, Vibrio cholerae, L. monocytogenes, and S. aureus) [ L. monocytogenes exhibited cross-hybridization with B. cereus and Bacillus thuringiensis strains, while the probe for S. aureus cross-hybridized with L. monocytogenes strains. Likewise, the probe for B. cereus could not distinguish between B. cereus and B. thuringiensis, due to the high sequence similarity of the 16S and 23S rRNA genes of these two species.The aim of the present study was the use of the FT-RDB hybridization approach for the fast and efficient identification of food pathogens and spoilage bacteria. In another work, the combination of FT and RDB has been applied to the detection of ten intestinal foodborne pathogens ( aureus) . In such E. coli, C. jejuni, Shigella dysenteriae, V. cholerae, V. parahaemolyticus, Proteus vulgaris, B. cereus, L. monocytogenes, C. botulinum, and S. aureus [ C. perfringens and Streptococcus pyogenes based on the 23S rRNA gene. Furthermore, the DNA isolated of the species Salmonella enterica and Y. enterocolitica gave cross-reactions with E. coli in their study [In another study, RDB hybridization on nylon membranes was successfully applied for the detection of 14 foodborne pathogens, using specific probes for the 23S rRNA gene as target sequences for the species. aureus . Howeverir study .In a recent study, 11 foodborne pathogens were detected with biotinylated species-specific primers and hybridization of the amplification products to specific probes on a thin-film biosensor . The assThe assays described in the present work have been carried out taking into account the principles of FT-RDB hybridization described by Xing et al. , but resThe FT-RDB hybridization methodology described in this work is fast and efficient and can be easily implemented in standard microbiological laboratories due to the ease of use and reduced costs as compared to the expensive instrumentation, material, and technical expertise required for microarray platforms. The assay can be applied for food control purposes, obtaining a rapid, accurate, and specific identification of bacterial species with importance in the spoilage and safety of any food product. In the PCR step, bacterial DNA could be amplified directly from a food matrix without previous isolation and purification steps. Thanks to the combination of specific LDR-probes and the flow-through hybridization process, results were obtained in a few hours. Food microbial risk can be efficiently achieved with the FT-RDB approach evaluated in this work, this resulting in a better quality control of foodstuffs and the prevention of foodborne disease.


Phytoviruses are highly prevalent in plants worldwide, including vegetables and fruits. Humans, and more generally animals, are exposed daily to these viruses, among which several are extremely stable. It is currently accepted that a strict separation exists between plant and vertebrate viruses regarding their host range and pathogenicity, and plant viruses are believed to infect only plants. Accordingly, plant viruses are not considered to present potential pathogenicity to humans and other vertebrates. Notwithstanding these beliefs, there are many examples where phytoviruses circulate and propagate in insect vectors. Several issues are raised here that question if plant viruses might further cross the kingdom barrier to cause diseases in humans. Indeed, there is close relatedness between some plant and animal viruses, and almost identical gene repertoires. Moreover, plant viruses can be detected in non-human mammals and humans samples, and there are evidence of immune responses to plant viruses in invertebrates, non-human vertebrates and humans, and of the entry of plant viruses or their genomes into non-human mammal cells and bodies after experimental exposure. Overall, the question raised here is unresolved, and several data prompt the additional extensive study of the interactions between phytoviruses and non-human mammals and humans, and the potential of these viruses to cause diseases in humans. Tobacco mosaic virus (TMV), which causes mosaic disease in tobacco plants, is usually quoted as the first virus discovered. Indeed, in 1892, Ivanovski demonstrated that it passed through a Chamberland filter that retains bacteria .,32.31,32The distribution of plant and animal viruses within the currently defined taxons indicates that some plant and animal viruses belong to same viral families .Reoviridae includes several genera infecting a variety of animals and three genera  infecting both plants and insects [Reoviridae, which includes rotavirus, a major cause of gastroenteritis in humans [Rhabdoviridae, which notably includes rabies virus [Bunyaviridae, which encompasses human pathogens such as Hantaan virus and Toscana virus [Rhabdoviridae, genomic organization indicates that some plant viruses of genera Nucleorhabdovirus and Cytorhabdovirus differ from animal viruses of genera Vesiculovirus, Lyssavirus, Novirhabdovirus and Ephemerovirus only by the presence of an additional gene coding for a movement protein, a protein essential for the systemic spread of the virus in the host plant [Vesicular stomatitis virus (VSV) which belongs to genus Vesiculovirus, can be transmitted to domestic swine through infected black-flies (Simulium vittatum) [Indeed, family  insects ,35. In an humans , family es virus , and famna virus ,39,40. Wst plant ,18,37,41ittatum) . NotablyCowpea mosaic virus (CPMV), which is a member of family Secoviridae, shares structural features and genetic organization with animal picornaviruses such as polioviruses, coxsackie viruses and Theiler’s murine encephalomyelitis virus (TMEV), which supports the hypothesis that these plant and animal viruses might derive from a common ancestor [Caulimoviridae, which consists of the only plant viruses with a double-stranded (ds) DNA genome, as well as animal viruses that belong to the family Hepadnaviridae, which includes hepatitis B virus [Beet necrotic yellow vein virus, a plant virus that belongs to the genus Benyvirus, based on phylogeny of RNA polymerases [Virgaviridae were detected in the genome of a mosquito, Aedes aegypti; a fly, Drosophila rhopaloa; and a bee, Bombyx terrestris. The EVE detected in Aedes aegypti was similar to the tobamovirus genome, which is very surprising as no insect vector is known for tobamoviruses. These insects might have possibly interacted with these viruses during nectar feeding on infected plants. Moreover, Dolja and Koonin noted that there are multiple related groups of viruses in plant and animals and that the genes involved in viral genome replication and expression are conserved between plant and animal viruses, whereas genes implicated in virus-host interactions are not [Geminiviridae and Nanoviridae) and in animal viruses (families Parvoviridae and Circoviridae) [et al. suggested that a plant nanovirus switched hosts to infect a vertebrate and then recombined with a vertebrate-infecting picorna-like virus [hepatitis delta virus (HDV), which is a human pathogen that needs the envelope protein of hepatitis B virus for its replication, displays considerable similarities with viroids [In addition, ancestor ,44. In a B virus . Similarymerases . Recentlymerases . Interes are not . These a are not . For ins are not ,48,49. Iviridae) ,51. Alsoke virus ,53. This viroids . Viroids viroids . Regardi viroids ,56. This viroids , and it  viroids ,58. Inde viroids . These aTomato bushy stunt virus (TBSV), a tombusvirus, is an important pathogen of tomatoes and other plants, and it was observed to reach a concentration of approximately 200 mg per kg of infected leaves in experimental hosts such as Nicotiana clevelandii [Pepper mild mottle virus (PMMoV) RNA in 57% of 28 pepper-based food items and demonstrated that the PMMoV in these food items was still able to induce plant infection [7 viral copies/mL in Tabasco sauce [10RNA copies/cigarette and 3.8 log10RNA copies/mL of smokers’ saliva [Non-human animals and humans that eat fruits and leafy vegetables are exposed to phytoviruses. Thus, plant viruses are most likely highly prevalent in wild and cultivated plants, including fruits and vegetables . For exavelandii . Moreovevelandii . Some phnfection . The virco sauce . Hence, co sauce . Smokingco sauce ,65,66,67co sauce . Mean TM’ saliva .10copies/mL [Melon necrotic spot virus have been detected in irrigation systems in Slovenia and Spain, respectively [Cucumber green mottle mosaic virus (CGMMV) was recovered from the Yamuna River in India, and the recovered viruses were infectious to host plants [Finally, plant viruses are also detected in the environment including in the soil, water, and clouds ,71,72. Topies/mL . In addiopies/mL , ToMV anectively ,81, and t plants . This caThe natural presence of plant viruses in non-human mammals has been reported in several studies , and theLuteoviridae and Sobemovirus [Nanoviridae, Geminiviridae, and Alphaflexiviridae [Partitiviridae, Tymoviridae and Secoviridae were also observed in bat and rodent stools [10copies/mg [Rice yellow mottle virus can be transmitted by cows, donkeys and grass rats [Subterranean clover mottle virus by sheep  of 304 adults [versus 13% of PMMoV-negative patients (p= 0.042), and abdominal pain were reported in 39% of PMMoV-positive versus 7% of PMMoV-negative patients (p = 0.008), and pruritus was also independently correlated with PMMoV RNA detection in feces in multivariate analysis. Nevertheless, no causal relationship was demonstrated between PMMoV presence and clinical symptoms; especially, abdominal pain may be related to the consumption of pepper or pepper-based food items, which was not assessed in our study. Nonetheless, taken together, these findings support the hypothesis that PMMoV may not be only a transient inhabitant of the human gut but, alternatively, its presence may be non-neutral. In another study, PMMoV was found by qRT-PCR in 19/20 healthy human fecal samples at titers up to 7.0 log10viral RNA copies/mg [In 1982, TBSV, a h sewage ,79. Moreh sewage ,100. Thuh sewage   pooled cervicovaginal secretions from women from Ukraine presenting with colpitis (6 of 11), uterus fibroids (5 of 14), or uterus cervical erosion (5 of 16) [Acanthocystis turfacea chlorella virus 1 (ATCV-1), a phycodnavirus that infects green algae [Finally, two recent studies described the presence of algae viruses in humans. Stepanova 5 of 16) . More sten algae . MoreoveFrankliniella occidentalis, its major insect vector [Potato Virus Y developed antibodies to the virus [Plant viruses were found to trigger immune responses in invertebrates, vertebrates and humans, which might only reflect a history of exposure to foreign proteins and does not necessarily imply a role of the plant virus in pathogenesis. TSWV infection has been shown to induce a strong immune response in t vector . In addit vector , mice inhe virus , and we he virus . In anothe virus . Finallyhe virus , and anthe virus .In vitro analyses have demonstrated that TMV RNA can be translated in Xenopus laevis oocytes [et al. demonstrated that CPMV binds to the vimentin surface protein and enters mammalian cells, including endothelial, circulating cells and HeLa cells [Porcine reproductive and respiratory syndrome virus also uses surface vimentin for entry in host cells [Flock house virus (FHV), an insect virus first isolated from the grass grub Costelytra zealandica that belongs to the family Nodaviridae, whose members include viruses infecting various invertebrates and vertebrates, has been reported to replicate in plant, insect, yeast and mammalian (baby hamster kidney) cells, and it has been hypothesized that the host components required for FHV replication could be widely conserved [Frankliniella occidentalis encoded putative transcription factors in two human cell lines, human epithelial carcinoma (HeLa) cells and diploid fibroblasts, made these cells permissive for the replication of TWSV [Experimental data are available on the entry of plant viruses or their genomes into non-human mammal cells and bodies, although this was obtained through artificial means and did not lead to viral propagation in absence of an exogenous host factor. Indeed, viral replication implies the formation and release of a viral progeny, following virus entry in the target host cells.  oocytes  (Figure  oocytes . In addi oocytes . MoreoveLa cells . The aniLa cells , and mamst cells . Thus, Cst cells . RNA of onserved ,118,119.onserved  and treaonserved . CPMV waonserved , as the  of TWSV .Previous findings suggest that the border between plants and mammals including humans may be less strict for plant viruses than currently described . Mankind


Decision making often requires simultaneously learning about and combining evidence from various sources of information. However, when making inferences from these sources, humans show systematic biases that are often attributed to heuristics or limitations in cognitive processes. Here we use a combination of experimental and modelling approaches to reveal neural substrates of probabilistic inference and corresponding biases. We find systematic deviations from normative accounts of inference when alternative options are not equally rewarding; subjects' choice behaviour is biased towards the more rewarding option, whereas their inferences about individual cues show the opposite bias. Moreover, inference bias about combinations of cues depends on the number of cues. Using a biophysically plausible model, we link these biases to synaptic plasticity mechanisms modulated by reward expectation and attention. We demonstrate that inference relies on direct estimation of posteriors, not on combination of likelihoods and prior. Our work reveals novel mechanisms underlying cognitive biases and contributions of interactions between reward-dependent learning, decision making and attention to high-level reasoning. Humans are often biased in estimating the precise influence of probabilistic events on their decisions. Here, Khorsand and colleagues report a behavioural task that produces these biases in inference and describe a biophysically-plausible model that captures these behavioural deviations from optimal decision making. Naturalistic decision making and judgment often require simultaneously learning about and combining evidence from various sources of information. Learning the evidence provided by each source is a non-trivial task when multiple sources are presented at once and feedback is binary . For example, examining patients requires a doctor to attend to a variety of symptoms and utilize previous correct/incorrect diagnoses to make proper decisions. Such naturalistic decision making and reasoning situations can be simulated in the laboratory environment using various probabilistic inferences and learning tasks12245786910111213Interestingly, humans often exhibit systematic biases in their judgment as measured by deviations from normative accounts of inference . Although these biases have been attributed to heuristics or limitations in cognitive processesMoreover, an important and often neglected aspect of decision making and probabilistic inference is the influence of existing information on what is processed and subsequently updated when reward feedback is received. For example, during a flu epidemic, a doctor may first try to identify a few symptoms that strongly predict a correct diagnosis. For future patients, however, she would not only focus on those symptoms, but also attribute the resulting diagnosis success/failure to those symptoms, rather than the unattended ones. Existing information could influence what should be processed via attentional selection, which in turn could influence future learning and decision processes. Therefore, interactions between reward-dependent learning, decision-making and attentional processes could be crucial for probabilistic inference and could contribute to cognitive biases. Currently, little is known about how these processes interact17Here we used a combination of experimental and modelling approaches to reveal neural mechanisms underlying probabilistic inference and related biases. First, we used a novel experimental paradigm to precisely measure cognitive biases during both choice and inference. The experiment involved predicting reward on two alternative options based on simultaneously presented cues and subsequently receiving reward feedback. This decision task was followed by an inference task where subjects provided their estimates of the predictive power of individual cues or combinations of cues. Second, we developed a biophysically plausible model of probabilistic inference to simulate subjects' behaviour during our experiment. We find that when alternative options are not equally rewarding, choice behaviour of individual subjects is biased towards the more rewarding option, whereas their inferences about individual cues show the opposite bias. Moreover, inferences about combinations of cues show a bias that depends on the number of cues, contradicting any normative accounts of inference. Our results indicate that inference relies on direct estimation of posteriors and not on combination of likelihoods and prior. We show that reward-dependent learning, decision-making and attentional processes occur concurrently and interact dynamically to determine probabilistic inference and corresponding cognitive biases.P(B), equal to 0.25 and 0.75, respectively. During each trial of the estimation session, the subject estimated the probability that the red or blue target would be rewarded given a shape or a combination of shapes (two or four shapes). Importantly, there was no feedback during the estimation session, so subjects relied solely on what they had learned during the choice session when providing estimates.To measure cognitive biases during both choice and inference, we used a novel experimental paradigm in which human subjects simultaneously learned about and combined information from multiple cues through reward feedback, and subsequently provided estimates about the predictive power of individual cues or combinations of cues. Specifically, each subject performed a choice session followed by an estimation session. During each trial of the choice session, the subject was presented with four shapes (selected from a set of 4 shapes with the possibility of repetition) and chose between two alternative options (red or blue targets) to receive reward points . SubjectP(B)=0.25 and the blue target when P(B)=0.75. We quantified this bias by measuring the indifference point of the psychometric function =0.25 and negative for the subject with P(B)=0.75.Even though four shapes were presented on each trial, most subjects performed the task well, as shown by their choice behaviour, and learned the evidence provided by individual shapes (see Methods section for inclusion criteria). Estimation of posteriors for individual shapes matched the order of evidence carried by those shapes in 30 out of 37 subjects, and the remaining 7 subjects missed the order only between 1 pair of shapes. P(B)=0.25 and rightward shift for ePF for P(B)=0.75). Nevertheless, estimated posteriors were smaller  than the evidence (shown by grey squares in P(B)=0.25 =0.75), indicating that what was learned about individual shapes was influenced by the prior probability but not as strongly as prescribed by Bayes theorem. The subjective weight of evidence (SWOE) for individual shapes, which quantifies the influence of each shape on choice (see Methods section), showed a similar bias towards the less rewarding option =0.25; μchoice=−0.58±0.64 for P(B)=0.75). At the same time, one-shape estimates were biased towards the less rewarding option =0.25; μ1-shape=0.76±0.27 for P(B)=0.75). Similar to example subjects =0.25; μ2-shape=0.29±0.51 for P(B)=0.75). Moreover, the biases for two-shape estimates towards the less rewarding option were larger than those for four-shape estimates =0.25; μ4-shape=−0.37±0.63 for P(B)=0.75). Interestingly, four-shape estimates were biased towards the more rewarding option , and this bias was slightly less than the bias in choice behaviour; however, the difference between those biases was not statistically significant =0.25 and the red target for P(B)=0.75 . This phenomenon has been reported before116We further examined one-shape estimates corresponding to the extent that individual shapes predicted the reward on a given option, which revealed an important aspect of our data . OverallP=1 × 10−6 for S3 and P=6 × 10−10 for S4, N=37; P(B)=0.75 =0.25), S1 and S2 were presented more often when the blue  target was assigned with reward, whereas S3 and S4 were presented more often when the red  target was assigned with reward (see Methods section). The overestimation of both S3 and S4 towards the less rewarding option was robust (present in 32 out of 37 subjects) and large (m=0.23±0.17), and was similar in strength for these shapes =0.25 and smaller than 0.5 for P(B)=0.75; one-sided sign test P=0.04, N=37; Our data revealed two novel aspects of base-rate neglect, in addition to demonstrating this effect in individual subjects. First, there was a significant overestimation towards the less rewarding option only for two out of four shapes . However, we found a gradual increase in σ from one-shape to two-shape estimates  for 32 out of 37 subjects  for 33 out of 37 subjects  and in four-shape estimates were statistically indistinguishable across all subjects  as the number of shapes used for estimation increased.Finally, the precise nature of our experimental design allows us to quantify the stochasticity in choice behaviour and estimation, measured by the P(B)=0.5), we did not observe any systematic biases in choice behaviour or in the estimation , two-shape  and four-shape estimates , σ for one-shape and two-shapes estimates and for one-shape and four-shape estimates were strongly correlated  to make decisions and to give estimates (see the last Results section for more proof). Instead, we argue that what is learned about each cue also contains information about the prior as these two pieces of information are not and cannot be separated during learning.We hypothesized that the cognitive biases observed during probabilistic inference are strongly influenced by interactions between reward-dependent learning, decision-making and attentional processes. To link these interactions to observed cognitive biases, we extended our previous model of probabilistic inferenceThe model simulated subjects' behaviour during both choice and estimation sessions and exhibited patterns of biases similar to those shown by our subjects, as demonstrated by the model's behaviour using one set of parameters replicating the average behaviour , and theSimilar to the experimental data, the model also showed a progression of estimation biases, starting from a bias towards the less rewarding option for one-shape estimates to a bias towards the more rewarding option for four-shape estimates . Finallyμchoice values in To demonstrate why all the three aforementioned components are necessary to capture the experimental data  and to gain further insights into the model's behaviour, we show next the results of simulating the experiment using our previous modelMoreover, as the number of shapes increased, the model's ePF shifted towards the more rewarding option . The shiDespite the previous model's success in performing the task, the model's behaviour did not match experimental data in three specific ways. First, the model predicted the sensitivity to evidence to be independent of the number of shapes used for estimation . Second,To show ‘pedagogically' how the aforementioned additional components can eliminate the above discrepancies and enable the model to replicate the experimental data, we successively incorporated the following components into the model. First, after considering a concave f-I response function for value-encoding neurons, we found that the sensitivity to evidence decreased as the number of shapes increased . A concaThird, by incorporating the modulation of DA-dependent plasticity by reward expectation for the chosen option (see Methods section), the model exhibited a larger overestimation for S3 such that it estimated this shape to be predictive of the less rewarding option . This inOverall, these results show that the inclusion of all aforementioned components is crucial for capturing nuances of our experimental observations, and shed light on the contribution of interactions between cognitive processes to probabilistic inference and related contradictory biases. First, choice is biased towards the more rewarding option since selection of that option is more frequently accompanied by reward; therefore, cue–outcomes associations stored at plastic synapses are biased towards that option. That is, what is learned about each shape contains a piece of prior information . In essence, what is learned about a given shape is inherently a mixture of the prior probability and the likelihood provided by that shape, and this information is combined using the sum of the output currents through corresponding synapses when more than one shape is presentedBriefly, the heuristic model assumes that subjects perform the probabilistic inference task by assigning a probability of predicting reward on red (or blue) for each shape, and using the average of the assigned probabilities for the presented shapes to make a decision or give an estimate. The normative model assumes that subjects separately learn the likelihood  associated with individual shapes, as well as the prior probability for reward on red and blue. To make decisions or to give estimates, subjects optimally combine the prior probability and likelihoods from presented shapes using Bayes theorem. Finally, the reduced circuit model assumes that what is learned about each shape is an estimation of posteriors , which is combined (in log space) to make decisions or provide estimates when more than one shape is presented.P(B)=0.75 (bottom panels), using the three models. As expected, the heuristic model exhibited a strong decrease in the sensitivity to evidence  as the number of shapes used for estimation increased =16.6, 15.3 and 8.6 for the normative, heuristic and reduced circuit model, respectively). also see .The normative model predicted biases that changed with the number of shapes; however, the direction of these changes was the opposite of experimental observations. More specifically, two-shape and four-shape estimates were biased towards the less rewarding option, similar to one-shape estimates. This happens because in the normative model, the prior probability is similarly combined with evidence independent of the number of shapes used for estimation. Importantly, the discrepancy between this model's predicted biases and subjects' biases increased with the number of shapes . In contCognitive biases are often attributed to heuristics or limitations in cognitive processeset al.The base-rate effect has been shown before116120The fundamental outcome of the synaptic plasticity rule of our model is that evidence learned about individual cues is contaminated by the prior probability. This predicts that biases in estimated posteriors systematically depend on the number of cues used for estimation (which was observed in our experiment), contradicting any notion of a normative calculation of posteriors. Note that we did not add a separate set of synapses to our model to learn the prior probability because such a component would equally bias choice and estimation towards the more rewarding option. Overall, our results do not suggest that the brain fails to track the prior probability and instead, indicate that the computation of posteriors does not involve combining prior and likelihoods.Furthermore, the model assumes that information stored in plastic synapses can be accessed to provide explicit estimations about the predictive power of individual cues and/or their combination. This assumption is supported by the strong correlation between the SWOEs extracted from choice behaviour and one-shape, two-shape and four-shape estimates , as wellPrevious works using the weather prediction task have revealed important aspects of human learning and memory2345678101112668Even though the contribution of attention to cognitive biases has not been extensively explored28We showed that modulation of DA-dependent synaptic plasticity by reward expectation is crucial for capturing the patterns of overestimation and base-rate neglect. Although this assumption resembles the reward prediction error (RPE), it only applies to the DA signal when the reward is present ; that is, when outcomes are better than expected. This assumption is not only supported experimentally33The biophysically inspired nature of our model allows us to make further behavioural and neural predictions that can be tested in future experiments. First, the model suggests that the prior probability cannot be separated from evidence when both have to be learned through reward feedback, and, therefore, neurons or areas representing the reward value of individual shapes could show a modulation by the prior probability as well. This modulation can be measured and tested by varying the prior without changing the evidence associated with individual shapes. Second, sensory neurons representing shapes could show attentional modulation, while the evidence for shapes is being learned. Importantly, the degree of such modulation is correlated with the amount of shift in the bias between two-shape and four-shape estimates. Third, the model predicts that overestimation of individual shapes towards the less rewarding option should correlate with the influence of reward expectation on the learning rates, which can be measured using reward expectation as a regressor for neural response. In addition, one could use data from human subjects and look for within-subject correlation between measures of dopaminergic activity  and the amount of overestimation for individual shapes. Finally, the decision-making circuit relies on the difference between the inputs to its selective populations  to determine a choice, and the reaction times are inversely proportional to this differential input. This means that during the choice session, decisions are faster for trials on which the evidence provided by a combination of shapes is farther from zero. Moreover, the model assumes that estimation relies on the same sets of synapses used for making decisions. Therefore, one prediction would be that reaction times for one-shape estimates for S3 and S4 is inversely correlated with the deviation of the estimation from 0.5, which could be tested, considering the large variability in subjects' estimations.Altogether, our results suggest that probabilistic inference is performed by directly estimating posteriors and not by combining likelihoods and prior, resulting in deviations from the normative accounts of inference . Moreover, these cognitive biases are influenced by interactions between reward-dependent learning, decision-making and attentional processes that dynamically determine probabilistic inference.P(B), equal to 0.75. The remaining 18 subjects performed the experiment with P(B)=0.25. For the control experiment, we recruited 12 subjects  and ran the experiment with equal probabilities of reward on the red and blue targets =0.5). All subjects were naive to the experiment and the goal of our study.A total of 60  Dartmouth college undergraduate students participated in this experiment. Eleven subjects were excluded from data presented here for the following reasons: five subjects for not learning the task properly, defined as having the stochasticity during choice or estimation session three s.d. larger than the mean of all subjects; six subjects for giving three (out of four) or more inconsistent estimates for blue and red targets during one-shape estimation . The consistency criterion during one-shape estimation was used as a test for trusting all estimates, since for two-shape and four-shape estimates either the blue or red target was probed for each combination of shapes. The results from 37  subjects who performed the main experiment are reported here, 19 of whom performed the experiment with the prior probability of the blue target being rewarded, P(B)=0.5), followed by 18 estimation trials. During the choice session of the training phase, various combinations of two shapes (chosen randomly from a set of four shapes with replacement) were presented together on each trial. During the estimation session of the training phase, subjects provided their estimates about the predictive power of individual shapes and two-shape combinations of shapes.After receiving and signing the consent form, subjects received instructions and then played a few mock trials to learn about the two sessions of the experiment (choice and estimation), as well as different types of trials, timing of trials and how to submit their responses using a keyboard. This was followed by a training phase consisting of 120 choice trials in which subjects learned about the predictive power of 4 randomly assigned shapes  followed by 53 estimation trials.P(B)=0.25 or P(B)=0.75).The subjects were informed that their payment in the experiment depended on the number of reward bars they collected, as well as how closely their estimates resembled the actual values. Moreover, the subjects were told that presented shapes on each trial predicted reward on the red or blue targets, and the rule used for assigning reward did not depend on the location of the shapes. Subjects were randomly assigned to experimental conditions with an unequal prior probability =0.5 and P(B)=0.75, and S4 to S1 for P(B)=0.25. To determine which shapes would be presented on each trial of the experiment, we first randomly assigned reward to one of the two options according to the prior probability, P(B) . Subsequently, depending on the assigned reward on a given trial, the computer program used the logLRs to determine which of four shapes should be present. This procedure was repeated two (for training) or four times (for the main experiment) to assign all shapes on each trial. Note that in the case of unequal prior, four shapes were not presented equally often.where During each trial of the choice session, subjects were presented with two (during the training phase) or four shapes (during the main experiment) and chose between the two colour targets by pressing the letters ‘F' or ‘J' to select the left or right target, respectively. A circle appeared around the chosen target, followed by the presentation of reward feedback (correct/incorrect) and an update of the reward bar that showed recent accumulation of reward points . When thDuring each trial of the estimation session, subjects estimated the probability that the red or blue target would be rewarded given a shape or a combination of shapes . This seAfter completion of the experiment, the subjects were asked to write down the overall probability that the blue target was rewarded during the training as well as during the main experiment. All experimental procedures were approved by the Committee for the Protection of Human Participants of Dartmouth College, and a written consent was obtained from each subject before participating in the experiment.To quantify each subject's behaviour during the choice session, we computed the PF by calculating the probability that the blue target was selected for a given combination of shapes as a function of the log posterior odds for that combination. Using Bayes theorem, the log posterior odds for reward being on blue versus red is equal tok shapes, was presented on a given trial. We fit this PF with a sigmoid function to extract μ  that measures the subject's bias, as well as σ that measures stochasticity in the subject's choice behaviour :where PB is the probability of choosing the blue target. Note that based on μ=0 and very small values of σ. To quantify behaviour in the estimation session, we computed the ePF that show the relationship between estimations provided by the subject and the actual log posterior odds for each shape or combination of shapes. A fitting procedure similar to one used for the PF was applied for estimating μ and σ for the ePF.where Moreover, we used a logistic regression model to extract the influence of individual shapes on choice behaviour:Ni is the number of appearances of shape i in a given combination. We call the regression coefficients (qi's) from this fit the SWOE for individual shapes. Note that we did not include a bias term here because there is a fixed number of shapes on each trial, and therefore, an additional bias term would cause degeneracy in fitting . Extracted SWOEs from subjects showed that subjects learned most of the information after around 60 trials , and an inhibitory pool of neurons. As we have shown before1438The decision-making circuit receives inputs from neurons encoding action values . This ciB given a combination of shapes t, 1/σD quantifies the sensitivity of the decision network to the difference in its inputs, ciB(t) represents the average strength of synapses  between neurons encoding shape i to neurons encoding the value of the blue target, and the sum is over all shapes presented in that combination (the current evoked by a repeated shape is multiplied by the number of repetition). Importantly, the convergence from cue-encoding neurons enables value-encoding neurons to combine information from various cues presented on a given trial. Moreover, to give estimates, the overall output from the value-encoding neurons evoked by the presentation of a shape or combination of shapes is passed through an equation similar to σE.where (pign), but this mechanism could be generalized to include the possibility of ignoring any shapes with a probability that depends on the predictive power of that shape. Finally, the reward circuit signals the presence or absence of reward at the end of each trial. We assumed that the absence of reward is signalled with no DA release , whereas the presence of reward is signalled by DA release modulated by the expectation of reward on the chosen target (see Learning rule).The attention circuit also receives inputs from value-encoding neurons that are selective to action and shape . We assuciB and ciR .The inputs to the decision circuit are determined by the activity of sensory neural pools encoding the presented shapes, and by the strength of plastic synapses from these populations onto value-encoding populations. Similar to our previous work1438At the end of each trial, plastic synapses were modified according to a stochastic, reward-dependent, Hebbian learning rule. First, Hebbian plasticity required a high level of activity in both pre- and post-synaptic neurons so only synapses from active cue-encoding neurons onto the value-encoding neurons selective for the chosen target were modified. Neurons encoding the reward value of a given shape are assumed active only if that shape was registered/attended. We assumed value-encoding neurons selective to shape and action behave similarly. Second, depending on the outcome (reward or no reward) on a given trial, plastic synapses were either potentiated or depressed. However, the potentiation rate depends on the concentration of DA, which itself depends on reward expectation (see below). In the absence of reward, DA neurons fire at a very low rate (DA concentration is very low) and therefore, modulation of the depression rate by reward expectation is not meaningful. Third, these synaptic modifications occur stochastically.Specifically, at the end of an unrewarded trial where the blue target was selected, synaptic strengths  for all registered/attended shapes onto value-encoding neurons selective for this target were updated as (assuming there is a large number of synapses to consider synaptic strengths as continuous variables)q− is the depression rate. On rewarded trials, synaptic strengths are updated as followswhere q+ is the potentiation rate, and qp and qd) do not depend on the number of repetitions of a presented shape, but qualitatively similar results were obtained if the learning rates monotonically increase with this number.where The effect of reward expectation on the potentiation rate (via changes in DA release) is computed as:1/σr determines the sensitivity of DA release to reward expectations. The local average of reward on the blue target (and similarly for the red target) is updated as:where qr was set to 0.1 in all simulations. Synapses onto value-encoding neurons selective for both action and shape undergo a similar learning rule, but with larger learning rates. The faster learning allows the attention circuit to selectively process individual shapes early in the experiment.Because the chance level of reward for binary choices is 0.5, this value is used as a baseline in qp<2.5qd:qp=; qd=; σD=; σE=. We limited the range to the above values to avoid extreme biases towards the more rewarding option during the choice session, which occurs when the learning rates are greater than 0.12, or when qp and qdare comparable in magnitude. For simulations of the original model with the additional concave f-I response function for the value-encoding neurons  from the following values: qp=; qd=; σD=; σE=; pign=; and σr=.For simulations shown in  neurons , we usedectation , we sampThe heuristic model assumes that subjects perform probabilistic inference by assigning a probability of predicting reward on the blue (or red) target for each shape and updating these probabilities using reward feedback. Specifically, subjects use the average of the assigned probabilities for the presented shapes to estimate the predictive power of a combination of shapes. To make a choice, we assumed that the average of reward probabilities assigned to the presented shapes are mapped to a sigmoid function to allow a wide range of behaviour piB is the probability of reward on blue if shape i is presented alone, and σD is a model parameter. The assigned probabilities are updated after receiving feedback on every trial using equations similar to ciB) are replaced with reward probabilities (piB). Similar to our neural circuit model, we assumed that the potentiation rate could be modulated by reward expectation based on qr at 0.1 and sampled all combinations of parameters  from the following values: qp=; qd=; σD=; σr=.where To test the basic assumptions of our model regarding what information is learned about individual shapes and how information from multiple shapes is combined to make decisions and give estimates, we fit the experimental data using three models: the heuristic, normative, and reduced circuit model. More specifically, we used one-shape estimates given by individual subjects to predict their two-shape and four-shape estimates. We used this fitting method because it is parameter-free and avoids any assumptions about learning in the normative model.First, the heuristic model assumes that subjects perform probabilistic inference tasks by assigning a probability of predicting reward on the blue (or red) target for each shape, and using the average of the assigned probabilities for the presented shapes to give an estimate. To fit the data using the heuristic model, we predicted two-shape and four-shape estimates for each subject by simply averaging one-shape estimates for the presented shapes on a given trial (including repetition).Second, the normative model assumes that subjects separately learn the likelihood  associated with individual shapes, as well as the prior probability for reward on red and blue. Moreover, to make decisions or give estimates, subjects optimally combine the prior probability and likelihoods from presented shapes using Bayes theorem. Although it is unclear how such learning could be performed in the brain (or even by a Bayesian learner) and whether these computations can be done without any bias, we wanted to test predictions of this model regarding changes in estimation as the number of shapes used for estimation increased. To calculate predictions for the normative model, we first used the prior probability and one-shape estimates provided by a given subject to extract the logLR associated with each shape for that subject. Following this, two-shape and four-shape estimates were predicted by summating the log prior odds and logLRs of the presented shapes to obtain the log posterior odds, and then converting the resulting log posterior odds to posteriors.Finally, we used the main assumption of our model to arrive at the reduced circuit model. That is, what is learned about each shape is a mixture of prior and evidence and this information is used to provide estimates about a combination of shapes. To compute predictions for the reduced circuit model, we first calculated the log posterior odds for individual shapes using one-shape estimates for a given subject. The two-shape and four-shape estimates were calculated by adding log posterior odds for shapes presented in a given combination, which were then converted to posteriors.How to cite this article: Soltani, A. et al. Neural substrates of cognitive biases during probabilistic inference. Nat. Commun. 7:11393 doi: 10.1038/ncomms11393 (2016).Supplementary Figures 1-7 and Supplementary Note 1