However, they impact chromatin dynamics and should be reflected in particular in the motion of a single locus. We present here a method based on polymer models and statistics of single trajectories to extract the force characteristics and in particular when they are generated by the gradient of a quadratic potential well. Using numerical simulations of a Rouse polymer and live cell imaging of the MAT-locus located on the yeast Saccharomyces cerevisiae chromosome III, we recover the amplitude and the distance between the observed and the interacting monomer. To conclude, the confined trajectories we observed in vivo reflect local interaction on chromatin.Is it possible to extract tethering forces applied on chromatin from the statistics of a single locus trajectories imaged  in vivo live single locus trajectories to extract physical forces applied on chromatin. We applied the method to the statistics of the S. cerevisiae MAT-locus motion and recover tethering forces acting on the chromatin. The local confinement of a chromatin locus can either be due to crowding or to local interactions with partners such as the surface of the nuclear membrane, other chromosomes or nuclear bodies that cannot be directly measured. We conclude here that confined trajectories of a single chromatin locus can be generated by local tethering interactions. This approach is applicable to cells under various conditions, such as during double-stranded DNA break repair.Is it possible to recover the local environment, the external and internal forces acting on a polymer from a single locus trajectories? To study this question, we resolve this reverse cell biology problem by developing a method that uses  What can we learn about the local environment, the external and internal forces and the chromatin itself from the motion of a chromatin locus? This motion can be driven by local diffusion and/or forces between monomers of the model polymer –3. Monomv is proportional to a force f applied on the particle plus an additional white noise, summarized asγ is the friction coefficient, D the diffusion coefficient and w is the normalized Wiener process. Thus by averaging over the ensemble of velocity realizations, it is possible to recover the first moment, which is the force field [Rc(t2)−⟨Rc(t2)⟩]⟩ of the tagged monomer c? We shall demonstrate here that the auto-correlation function can be used to recover the spring constant k. Indeed, by decomposing the external potential up, that diagonalizes the Rouse potential p = 0..N−1. The force applied on monomer n couples the modes dynamics . However, when the strength of the coupling term is relatively weak k < κ and N large. Thus the expansion of the auto-correlation function isHow is the force applied to a polymer reflected in the auto-correlation function D is known, it is possible to extract the spring constant k.Thus the auto-correlation function decays exponentially and the exponent of the dominant term is proportional to S. cerevisiae . Thus the locus is restricted to a small region of the nucleus. To extract the possible forces constraining this motion, we analyzed independently the trajectories of the MAT-locus in several cells. As cells are observed in the G1 phase, this analysis assumes that interactions on the chromatin do not change transiently, but rather have reached steady state, compared to the time scale of few minutes of the recording.We now apply the present analysis to the dynamics of a chromatin locus. We monitored the time fluctuations of the chromatin fiber by following a GFP tagged DNA locus in the yeast siae see  for 100skc from the sampled trajectories. The trajectories are acquired in three dimensions. However, due to the precision difference in the XY plane (65nm×65nm) compare to the z axis (300nm), we only used the x− and y− projections to evaluate the constant kc.Within the hypothesis that the chromatin was interacting locally with other nuclear elements, we extracted the overall force resulting from these interactions by applying formula kc  decays as a sum of power laws [C(t) with a power law, but could not obtained a satisfactory approximation, suggesting that the description of the locus motion as a fractional Brownian motion alone is not sufficient. However, we obtain a good fitting of the function C(t) by a sum of two exponentialsτ1 = 45.7 ± 0.005sec,τ2 = 2.4 ± 0.35sec and a1 = 109 ± 5 × 10−3μm2, a2 = 8.38 ± 4.94 × 10−3μm2. This fit suggests that the auto-correlation function for the locus position is well described by a sum of two exponentials, as predicted by formula To test that the polymer model we are using, gives a self-consistent framework for interpreting the MAT-locus dynamics, we computed the auto-correlation function for the MAT-locus trajectory ,C(t)=1Nwer laws . Thus weβ− polymer account for the dynamics of a chromatin locus. In that context, it was possible to extract from SPTs, characteristics of the DNA locus, its dynamics, external forces and some properties of the polymer model. At this stage, we cannot determine the nature and physical origin of the anchoring forces. Forces may occur at the centromere, which is anchored to the nuclear membrane in yeast through interaction with the spindle pole body and/or at the telomeres, which are interacting with the nuclear membrane through several pathways [We conclude that polymer models, such as Rouse or pathways . The larkc shown in k—We have shown here how to extract from single locus trajectories, chromatin tethering mediated by interactions with its surrounding environment. The presented method allows recovering an external force applied on chromatin although this one occurs far away from the observed locus. We note that this analysis is valid, although the recorded trajectories are possibly shorter than the relaxation time of the anchored chromatin. However, it is not yet possible to discern the forces from a locus positioned between two different interacting potential wells from the one generated by a single force located far away from the observed locus. In the complex nuclear environment, interactions of different strength can be randomly and transiently distributed along the chromatin. However, the resulting force on a single locus should mostly be generated by the sum of the two nearest interacting forces . The distribution of the spring values β-polymers and we extracted here in vivo interactions of the chromatin with other nuclear element that were reflected in the motion of the MAT-locus. These interactions are responsible for constraining the locus in a small fraction of the nucleus.A refined description of interactions on the chromatin would require monitoring simultaneously several loci. The present approach is also applicable for higher order organized polymer, modeled by kc that would be extracted during an active chromatin motion could be differentiated from the thermal one by projecting the dynamics perpendicular to the direction of motion. Finally, the present approach could also be used to study how chromatin modifications occurring during gene transcription or double stranded DNA repair affect the dynamics of a given locus.The motion of the chromatin is driven by both thermal fluctuations and by active ATP-dependent forces . While oα ade1 leu2-3, leu2-112 lys5 trp1::HisG ura3-52. The strain was obtained through insertion of both a Lac operator array (256 lacOp repeats), a Nup49-mCherry fusion and a non-tetramerizing lac repressor-GFP fusion under the HIS3 promoter into JKM179. To serve as a static reference point in the nucleus, the Spc42 protein was fused to yEGFP. All insertions or deletions were verified by PCR and phenotypic assays.Yeast strains used in this study are all derivatives of the JKM179 strain  which isMicroscopy Images were captured with a ×100 magnification oil-immersion objective  on a Leica DMI 6000B microscope (Leica Microsystems) equipped with a piezoelectric translator , a ORCA-Flash 4.0 camera (Hamamatsu) an illumination system with leds (Lumencore) and rapid imaging software (Metamorph). Wavelengths of the leds used are 475nm , and/or 575nm . Two-minute movies with a stack of 10 optical slices separated by 300nm every 338ms. Each slice was exposed for 30ms for a total of 338ms per stack. All microscopy was done in a temperature-controlled environment set to 25°C. The raw images were deconvolved using the Autoquant software. The movies were then tracking using ImageJ  with theϕ(R) is given by D is the diffusion coefficient and w are the three dimensional white Gaussian noise, with mean zero and variance 1.To simulate the dynamics of the polymer, we used the Euler’s method to discretize the equations intoS1 Text(PDF)Click here for additional data file.


It is important for health policy and expenditure projections to understand the relationship between age, death and expenditure on health care (HC). Research has shown that older age groups incur lower hospital costs than previously anticipated and that remaining time to death (TTD) was a much stronger indicator for expenditure than age. How health behaviour or risk factors impact on HC utilisation and costs at the end of life is relatively unknown. Smoking and Body Mass Index (BMI) have featured most prominently and mixed findings exist as to the exact nature of this association.This paper considers the relationship between TTD, age and expenditure for inpatient care in the last 12 quarters of life; and introduces measures of health status and risks. A longitudinal dataset covering 35 years is utilised, including baseline survey data linked to hospital and death records. The effect of age, TTD and health indicators on expenditure for inpatient care is estimated using a two-part model.As individuals approach death costs increase. This effect is highly significant (p<0.01) from the last until the 8th quarter before death and influenced by age. Statistically significant effects on costs were found for: smoking status, systolic blood pressure and lung function (FEV1). On average, smokers incurred lower quarterly costs in their last 12 quarters of life than non-smokers (~7%). Participants’ BMI at baseline did show a negative association with probability of HC utilisation however this effect disappeared when costs were estimated.Health risk measures obtained at baseline provide a good indication of individuals’ probability of needing medical attention later in life and incurring costs, despite the small size of the effect. Utilising a linked dataset, where such measures are available can add substantially to our ability to explain the relationship between TTD and costs. There is general agreement in the academic literature and amongst policy makers that population ageing alone does not drive healthcare expenditure (HCE) to the extent previously anticipated. Research has found a number of other factors aside from population ageing that explain HCE. One frequently researched factor is remaining time to death (TTD), and studies have concluded that the association between age and HCE actually reflects a stronger relationship between the remaining TTD and HCE, such that TTD is argued to be a better predictor of acute care costs than population ageing per se –8. A limHealth behaviours or risks have been found to be important predictors of lifetime medical costs. Smoking has long been a focus in the literature and a number of studies have analysed HC costs related to smoking, but have found conflicting results; ranging from findings that show a positive association between smoking and lifetime medical expenditure –13 to thMany previous analyses of the TTD-ageing-HCE relationship have only included individuals for which a date of death could be observed (decedents); participants without an observed date of death (survivors) are typically right censored and their remaining TTD is unknown. Consequently, any HC costs observed at the end of the study are not necessarily costs incurred at the end of life. This introduces bias and the exclusion of survivors may lead to an overestimation of costs. Such bias is avoided in this paper as survival analysis is employed to predict remaining lifetime for survivors and adjust their observed time before death accordingly .Presented below is a novel but robust analysis of unbiased expenditure for hospital care, controlling for both age and remaining TTD, and health behaviours and health status. The analysis utilises a longitudinal dataset from Scotland where baseline survey data from the 1970s have been linked to subsequent hospital admissions and death records. The breadth of the Scottish data means that explanatory variables, previously not been considered can be included, such as health risk and health status measures at baseline and socio-economic status, thus allowing an examination of the importance of these factors and their impact on expenditure later in life.http://www.gla.ac.uk/researchinstitutes/healthwellbeing/research/publichealth/midspan/.This study uses longitudinal data based on a large cohort study undertaken in the 1970s in the West of Scotland. The Renfrew/Paisley study, one of the Midspan studies, covers a period of 35 years ,17. The Permission was given by the Privacy Advisory Committee of the Information Services Division (ISD) to use linked SMR01 data. Ethical approval for this study had been sought and was granted by the University of Glasgow’s ‘Medical Faculty Ethics Committee’(FM01709). All patient records were anonymised prior to the analysis being carried out and no individual patient could be identified.Linkage was established between the cohort and SMR01 and the National Records of Scotland (NRS), with individuals followed up in terms of hospital use and death until December 2007. SMR01 has episode-based patient records that relate to all acute inpatient and day cases. Care episodes that are excluded from SMR01 are obstetric and psychiatric specialties. Geriatric long stay episodes were part of SMR01 until 1997 but due to this inconsistency and the nature of the care episodes they have been excluded from the analysis. To estimate hospital utilisation multiple inpatient episodes that form an entire hospital stay were summarised into continuous inpatient stays (CIS).To reduce measurement error, the SMR01 data were checked for data entry anomalies such as duplicate, overlapping and nested episodes, which were discarded from further analysis; in the cohort sample of 15,252 members there were 99,028 CIS. The data were manipulated into 3-monthly quarters for the analytical approach, and after taking account of missing information for the dependent and independent variables, the cohort sample size was 14,860. Costing of hospital episodes was undertaken following the procedure for Healthcare Resource Group (HRG) costing  using thThe questionnaire included questions on smoking behaviour, exercise, home address, bronchitis and angina and clinical data were collected on height, weight, respiratory function (Forced Expiratory Volume (FEV) in 1 second; % predicted FEV1  through expected FEV1 value, where the expected value is derived from a healthy subset with similar characteristics)), systolic and diastolic blood pressure and cholesterol. Exercise was defined from a question on time spent each day walking to and from work. Smoking status was defined as currently smoking one or more cigarettes/pipes/cigars per day. Data that were subsequently derived include the age at screening and the Carstairs deprivation category based on postcode of residence at screening. The index was developed in 1991 based on the 1981 census as a measure of area deprivation. Category 1 represents the most affluent postcode sectors and category 7 the most deprived ones [At the end of the study period, death was not observed for all sample members . Regression analysis was undertaken to estimate the hazard of dying for surviving individuals assuming a Gompertz distribution of the hazard of dying . Time unA dummy variable indicating poor % predicted FEV1 (<70%);A dummy variable indicating high systolic blood pressure (SBP) (>140 mmHg)A dummy variable indicating high cholesterol levels (> = 6.2mmol/L)A dummy variable indicating a high BMI/obesity (>25)A dummy variable representing a measure of the time individuals spent each day walking to and from work as a proxy for physical activity (> = 10 minutes)A dummy variable representing current smoking status .Regression models were employed to estimate the probability of utilising acute inpatient HC services (as measured by CIS) and related costs conditional on positive utilisation for the last 12 quarters of life. The initial exploration of the data showed that costs increased markedly in the last two quarters of life. Exploratory regression analysis determined when TTD became an insignificant predictor for costs. It was therefore decided to analyse the last three years of life, measured in quarters (i.e. 12). The following explanatory variables measuring health status at baseline were included in the regression:TTD was included as a series of 12 quarter dummy variables. The quarter furthest away from death (12th quarter) served as the reference category. Age at death (in five year age bands) was measured in seven categories with the youngest age group serving as the reference category. Interactions between TTD in quarters and age at death categories were included to capture any combined effect of ageing and TTD on costs. Gender was included to account for any differences in costs due to gender-specific differences in morbidities with males serving as the reference category. The Carstairs deprivation score was included to account for differences in costs due to socio-economic status. The period of admission in which each quarter before death lies, was included to account for advances in medical technology over time, with the most historic period serving as the reference category.i = robust standard errors.A two-part model was developed with the first part using a probit link and a binomial distribution to estimate the probability of utilising hospital care in any given quarter before death conditional on a set of regressors X .Pr clustered on patient identifier. Predicted probabilities of positive HC utilisation, obtained from the first part of the model were multiplied by cost estimates from the second part and average cost estimates were obtained .Sample characteristics are presented in No significant differences between survivors and decedents were found for two of the health risk and health status measures. The share of the population with a healthy cholesterol level (<6.2mmol/L) in both groups was about 54%. Another non-significant difference between groups was observed for the proxy measure that was used to capture physical activity, i.e. the number of minutes spent each day walking to and from work. In both groups, about 74% spent ten minutes or more walking to or from work each day.Regression results (hazard ratios) for the survival analysis are shown in In The age effects relate to the 12th quarter before death. The inclusion of interaction terms allowed age to have a different effect on the probability of being admitted to hospital in each quarter before death. An increased probability of accessing hospital care was observed for individuals aged 70 and older compared to the youngest age group (<65 years). Coefficients for interaction terms between age and TTD, whereby the reference category for age is varied, are shown graphically in The coefficient estimates for health status baseline measures revealed that: smokers had a significantly higher probability of being admitted to hospital compared with non-smokers; individuals with a BMI below 25 were less likely to access hospital care than people with a BMI above 25; individuals with a normal SBP (< = 140mmHg) were significantly more likely to access hospital care than people with an SBP of over 140mmHg; and individuals with a cholesterol level below 6.2mmol/L were found to be significantly more likely to access hospital services than people with a level over 6.2mmol/L . No statistically significant association was found between the probability of being admitted to hospital and the % predicted FEV1, and walking to and from work for more than ten minutes per day. Individuals living in areas with deprivation categories 3, 4 and 5 were significantly more likely to be admitted to hospital compared to individuals from the most affluent areas (category 1). However, this significant effect could not be observed for the two most deprived areas (categories 6 and 7).Regression results for the second part of the model, estimating costs given positive utilisation see , are preOn average, male individuals incurred significantly lower costs than females (~15%). The effect that the socioeconomic status had on costs, given positive utilisation was very small. Interestingly, it also did not seem to have a statistically significant association with costs. The statistically significant effect of deprivation on the probability of hospitalisation that was observed above was found to disappear. Smokers were found to incur lower costs on average than non-smokers (~7%). The effect of BMI was found to be very small and not statistically significant. A SBP of below 140mmHg at baseline led to a significant reduction in costs by about 5%, and a % predicted FEV below 70% led to a significant increase in costs (~8%). No significant effect was observed for the health status indicator of walking to and from work for more than ten minutes a day.This paper provides an estimate of the independent effect that TTD and age have on expenditure for acute inpatient care. This was done using a longitudinal survey based dataset (Renfrew/Paisley study) linked to acute inpatient records (SMR01) that included previously excluded factors; namely measures of health status and health risks while also controlling for socio-economic status.Overall, TTD, age at death and the interactions between these two were found to be significant predictors for costs in the last 12 quarters of life thus confirming findings from other national research ; 22 for This paper also sought to investigate how health status and health risk measures that were obtained at baseline influenced hospital costs later in life. Although the size of the effect that these variables had on the probability of being admitted to hospital was small, statistical significance could be observed for four of the measures: smoking status, BMI, SBP, and cholesterol at baseline. Notably for SBP and the cholesterol level the converse of what was expected was found: individuals with healthy readings were observed to have a higher probability of accessing hospital care. This might be explained by the ‘worried well’ seeking medical attention earlier and perhaps more frequently. This is confirmed when considering the second part of the model which estimated costs given positive utilisation; the significant effects on the probability of accessing hospital care did not filter through to significantly higher costs being incurred. People from more deprived areas are more likely to reach hospital in the last 12 quarters before death than people from less deprived areas. This might be a reflection of people from more affluent areas being more likely to die at home or in nursing homes compared to people from more deprived areas who might be more likely to die in hospital. This seems to be confirmed when looking at the lack of difference in costs incurred between socio-economic groups, once hospitalised.The time during which this study was undertaken might provide further explanation as to some of the results. An inverse association between the cholesterol level and the socio-economic status was found in previous research –24, i.e.The strength of this study is expressed through the comprehensive linked data with very low attrition rates and minimum missing observations. However like many empirical analyses using longitudinal cohort data it also has limitations. Study participants may have had hospital admissions outside Scotland, i.e. the rest of the UK or even Europe, which are not recorded in the linked Renfrew/Paisley-SMR01 data. Admission quarters have been used to assign costs to a specific quarter before death. This, while used in previous research, does push costs away from death which could lead to an underestimation of costs in the quarters closest to death. As older people tend to be closer to death on average, this may also affect the distribution of costs in the last quarters of life by age category. Further limitations arise from the fact that repeated measurements on health status indicators over the follow-up period were not available and the analyses rely on the assumption that participants had constant health risks throughout. However it does highlight that even baseline health measures taken some decades ago have an impact on future hospital utilisation and costs. Another limitation arises from the fact that only costs for the acute inpatient care sector could be analysed. Although accounting for the main share (close to 60%) of overall HC costs in Scotland , it doesDespite these limitations, this study is novel as it provides, for the first time in Scotland, a comprehensive analysis of inpatient care costs, accounting for age, remaining TTD as well as health status and health risk measure. It underpins previous research such as studies on the issue of rationing HC by age; two decades ago Williams put forward the ‘fair innings’ argument, which argued that resources should be devoted to those that would benefit most, i.e. more should be done to enable younger people to survive than should be done to enable older people to survive . This paFurther contributions to the wider literature include suggestions regarding the relationship between deprivation and costs, although further research to confirm this is required. The analysis identified interesting results for the effect that deprivation category had on the probability of being admitted to hospital and quarterly costs. Once hospitalised, the overall deprivation effect is no longer significant, so while these individuals might be more likely to be admitted to hospital, they do not consequently incur higher costs. Access to HC services and a higher risk of mortality for less affluent areas could serve as an explanation here  however


This article presents a tool to calculate health care costs attributable to overweight in a comparable and standardized way. The purpose is to describe the methodological principles of the tool and to put it into use by calculating and comparing the costs attributable to overweight for The Netherlands, Germany and Czech Republic. The tool uses a top-down and prevalence-based approach, consisting of five steps. Step one identifies overweight-related diseases and age- and gender-specific relative risks. Included diseases are ischemic heart disease, stroke, hypertension, type 2 diabetes mellitus, colorectal cancer, postmenopausal breast cancer, endometrial cancer, kidney cancer and osteoarthritis. Step two consists of collecting data on the age- and gender-specific prevalence of these diseases. Step three uses the population-attributable prevalence to determine the part of the prevalence of these diseases that is attributable to overweight. Step four calculates the health care costs associated with these diseases. Step five calculates the costs of these diseases that are attributable to overweight. Overweight is responsible for 20–26 % of the direct costs of included diseases, with sensitivity analyses varying this percentage between 15–31 %. Percentage of costs attributable to obesity and preobesity is about the same. Diseases with the highest percentage of costs due to overweight are diabetes, endometrial cancer and osteoarthritis. Disease costs attributable to overweight as a percentage of total health care expenditures range from 2 to 4 %. Data are consistent for all three countries, resulting in roughly a quarter of costs of included diseases being attributable to overweight.The online version of this article (doi:10.1007/s10198-014-0655-8) contains supplementary material, which is available to authorized users. In 2008/2009, the prevalence of overweight in 19 European Union member states varied between 51 and 69 % for men and between 37 and 57 % for women . These pd data are assumed to be available from surveys or morbidity registers, or they can be calculated (by the OBCOST tool) from incidence data and disease-specific mortality using an incidence-prevalence-mortality (IPM) model . The method uses a top-down approach for estimating the cost of illness, which consists of four stages [d. In order to assure international comparability of cost of illness estimations, the use of a standardized health accounting framework is required. The OECD System of Health Accounts (SHA) is recommended, restricting costs to curative care [Step four calculates the health care costs associated with the included diseases [cr stages , 16. Firive care .d value for each disease. The tool provides results in both absolute costs of included diseases that are attributable to overweight and percentage of costs of included diseases that are attributable to overweight.Finally, the fifth step calculates the costs attributable to overweight with the following formula:In order to perform calculations for NL, GE and CZR, data from these three countries were collected for population numbers, BMI prevalence, mortality of diseases, disease prevalence/incidence and costs of diseases. If possible, collected data were age and gender specific (5-year age groups were used). Data were not always complete for all age categories. Since the OBCOST software does not work when one or more cells are incomplete, estimations were made for missing data. Detailed information on the collected data and assumptions made is provided in Online Resource 3.The most important assumptions are presented here in short. First, GE reported their population numbers of the eldest ages as an aggregate group (90+). This number was divided over the last three original age categories  according to the WHO world standard population distribution . Second,Sensitivity analyses were performed in several ways. First, the sensitivity of the tool for missing disease data was estimated by including estimations of these missing disease data based on the data in the other countries in sensitivity calculations. The missing diseases categories were estimated by averaging data for the missing disease categories from the other two countries. Second, sensitivity of the tool to changes in disease costs was estimated by including pharmaceutical costs for hypertension and diabetes in cost data for CZR. These pharmaceutical costs were not included in the cost of illness for CZR, but were provided separately based on the Anatomical Therapeutic Chemical (ATC) classification system. Third, sensitivity analyses were performed with both of the previously described situations at the same time. Fourth, the sensitivity of the tool to variations in BMI prevalence was estimated, since studies have shown that self-reported BMI tends to differ from measured BMI by overestimating BMI at the lower end of the BMI scale (BMI < 22) and underestimating BMI at the higher end of the BMI scale (BMI > 28) , 20. TheAs could be expected, the proportion of males (49 %) and females 51 %) in the population is the same for all three countries. Table  % in theThe summarized disease data in Table The percentages of costs attributable to overweight calculated here are percentages of total costs of included diseases, as opposed to percentages of total annual health care expenditures. Estimation of the percentage of total health care costs that are attributable to overweight  results in percentages of 2.3 % for NL, 2.1 % for CZR and 3.7 % for GE.Table The estimated percentage of total costs of nine obesity-related diseases attributable to overweight ranges from 20 to 26 % in this analysis. Main contributors to this high percentage are diabetes, endometrial cancer and osteoarthritis. The present results are based on data provided by different countries with their own ways of collecting and registering data. This has some consequences for the quality and comparability of the data entered into the tool.Data for the eldest age groups were often not available. Estimations were made by applying the rate of the last available age group to all successive age groups. Since the eldest age groups (85+) are very specific groups, these assumptions are very precarious and can possibly bias the results. Due to the assumptions made, exclusion of these eldest age groups from this analysis had little effect on the percentage of total disease costs attributable to overweight. In general, the eldest age groups have a larger disease burden compared with the younger age groups. However, much of this disease burden is due to non-overweight-related diseases, so a relatively smaller share of the disease burden in the eldest age groups is attributable to overweight. Therefore, the percentage of disease costs attributable to overweight in the eldest age groups can be expected to be relatively low and pull down the percentage of total disease costs attributable to overweight. However, due to the small size and relatively low absolute costs of the eldest age groups , this influence might not be significant.Due to variations and uncertainties regarding the included diseases, cost data and BMI data, various sensitivity analyses were performed that yielded only small effects. Since including estimations of missing data did not change the percentages of total disease costs attributable to overweight, slight deviations in the ICD-10 codes for which disease data were provided can be expected to have negligible effects as well. Inclusion of pharmaceutical costs for CZR did result in a percentage change, suggesting that differences in cost data can influence cost estimates. Cost data are preferably based on the functional classification of the SHA  in orderThis study uses a method in which current disease prevalence data are used to estimate past incidence rates, which in turn are used to calculate current prevalence rates in a hypothetical population without overweight. However, since BMI prevalence rates have changed over time , the metSome methodological issues are discussed according to their potential influence as opposed to previous calculations. First, the results of this method will depend on the number and type of diseases that are included in the calculations; this will influence both absolute costs and percentage of costs attributable to overweight. Generally, when more diseases are included, estimated absolute costs attributable to overweight will increase. The estimated percentage of costs attributable to overweight is strongly dependent on the PAP of the included diseases. When diseases have a low PAP (such as stroke), a relatively small share of the costs of these diseases will be attributable to overweight. Therefore, these diseases will lower the percentage of total costs of included diseases attributable to overweight. On the other hand, diseases with a high PAP (such as diabetes) will result in an increase of this percentage.Second, the percentages of costs attributable to overweight calculated by the OBCOST tool are percentages of total costs of included diseases, as opposed to percentages of total annual health care expenditures. Estimation of the percentage of total health care costs that are attributable to overweight  results in percentages of 2.3 % for NL, 2.1 % for CZR and 37 % for GE. This is in line with results found in previous studies, where percentages were found ranging from 2.1 % of total health care costs in Germany to 4.8 % of total health care spending in the US –11.Third, by using PAPs, the method assumes that overweight-related diseases are mutually exclusive. However, overweight people often have multiple attributable diseases at a time, and often interactions exist between these conditions. The dynamics of these interactions are not reflected in the relative risks used for the PAPs. When health care provision for multiple diseases becomes more efficient , this wFurthermore, when interpreting the results of this study it should be kept in mind that the presented costs consist of direct  costs attributable to overweight. They do not take into account indirect costs such as production losses due to morbidity, mortality or informal care. Inclusion of indirect costs can lead to much higher estimates of total costs attributable to overweight. A review by Trogdon et al. (2008)  found abSince the OBCOST tool uses data that are supposed to be generally available for many countries, the tool can be used by any country where BMI data are available and where health care expenditure data are available coded by disease. When this is not the case, estimations can be made by using foreign data. The standardized method of cost calculations will lead to more comparable estimates of costs of overweight between countries. However, due to the flexible nature of the tool, expansion of its functionality is supported should the user find this necessary. For instance, one can include the nonmedical costs of disease or extend the selection of diseases by adding other diseases known to be associated with overweight .Information about costs attributable to overweight is important for establishing a case of preventive action . By usinThis tool can be relevant for several purposes. In the first place, it can serve to increase knowledge as a long-term monitoring instrument in order to keep track of changes in the annual economic burden of overweight. Furthermore, the tool could also be developed for other risk factors of disease. This way, the tool can be used to monitor and compare between costs attributable to different risk factors. This kind of information could be helpful in determining which risk factors or diseases are most costly and how to distribute preventive resources.The present study described a standardized methodology for calculating health care cost attributable to overweight. This methodology increases comparability between countries. Results show that for the three countries included, roughly a quarter of the costs of included diseases are attributable to overweight. Information about the costs of overweight increases political awareness and emphasizes the need for preventive action.Online Resources provide background information on the OBCOST tool and its methodology (Online Resource 1), an overview of relative risks entered into the tool (Online Resource 2) and detailed information on collected data and assumptions made (Online Resource 3).Supplementary material 1 (DOCX 102 kb)Supplementary material 2 (DOCX 37 kb)Supplementary material 3 (DOCX 23 kb)Below is the link to the electronic supplementary material.


Sciurus carolinensis), in which the virus is endemic. However, more recently the virus has emerged to cause epidemics with high mortality in Eurasian red squirrels (S. vulgaris) in Great Britain, which are now threatened. Here we report the genome sequence of SQPV. Comparison with other Poxviridae revealed a core set of poxvirus genes, the phylogeny of which showed SQPV to be in a new Chordopoxvirus subfamily between the Molluscipoxviruses and Parapoxviruses. A number of SQPV genes were related to virulence, including three major histocomaptibility class I homologs, and one CD47 homolog. In addition, a novel potential virulence factor showing homology to mammalian oligoadenylate synthetase (OAS) was identified. This family of proteins normally causes activation of an endoribonuclease (RNaseL) within infected cells. The putative function of this novel SQPV protein was predicted in silico.Squirrelpox virus (SQPV) shows little evidence for morbidity or mortality in North American grey squirrels ( Sciurus vulgaris) populations whilst apparently leaving sympatric North American grey squirrel (Sciurus carolinensis) populations unaffected. The underlying mechanisms responsible for the pathogenicity of SQPV are not understood and hence the reason for the different disease manifestation in the two species cannot be fully investigated. In this paper we document the complete sequence of the SQPV genome , confirming that it is a species in the family Poxviridae and does not partition with any of the currently recognized genera.Poxviruses cause disease in many different taxa and have been described in insects, birds, reptiles and mammals Red squirrels with squirrelpox show typical signs associated with poxvirus infections, with the formation of multifocal extensive ulcerative lesions around the mouth and eyelids It had been postulated that competition for habitat and resources partially explains the replacement of reds by greys Viruses often encode proteins related to the immune system of their host in order to circumvent host immunity and improve viral survival. Poxvirus-encoded virulence factors target many different aspects of host immunity. The majority act to block or subvert the host anti-viral responses, and include proteins that manipulate apoptosis of host cells, inhibit the activity of both natural killer (NK) cells and cytotoxic T-cells and that bind and/or inhibit a multitude of host cytokines The interferon (IFN) response system is targeted in many ways by poxvirus-encoded factors acting before and after IFN production The emergence of SQPV in red squirrels raises important questions about the virus's evolution and it's host interactions. It is clear SQPV has the ability to transfer between host species and has coevolved mechanisms which allow attenuated pathogenesis and virulence within the grey squirrel resulting in asymptomatic infection, but producing a high degree of virulence in the red squirrel host.Poxviridae, and describe novel virulence genes including one predicted to impede the host interferon response via interaction with the OAS system.Here we describe the genomic sequence of SQPV, discuss its phylogenetic relationship with the other The virus isolate used in this study was a from a necropsy sample taken from a red squirrel that had been found dead in the wild as previously described in Pox genomic DNA isolated directly from scabs from an infected squirrel http://staden.sourceforge.net) and gaps closed by assembling Sanger sequences. Protein-coding genes were identified by GLIMMER After assembly the resulting contigs were filtered with BLAST to remove the non-viral sequences. The contigs were viewed in GAP4 , 39 orthologous gene clusters where identified across 19 pox taxa , aligned with MUSCLE Structural homologues were identified using predicted amino acid sequences of relevant ORFs submitted for analysis on the SWISS-MODEL database Poxviridae are dsDNA viruses with linear genomes that range in size from ca. 135 kb to 360 kb. The final assembly for the SQPV genome  was within this range at 148 kb (one contig) with a GC content of 66.69% , differentiates SQPV from the parapoxviruses suggesting the viruses must have diverged before the gene rearrangements and losses occurred in the Parapoxvirinae.The phylogenetic analysis of the virus  shows thin vitro replication), are located at either end of the genome. Our data corrects mistaken annotation in the original description of these flanking regions Chordopoxvirus genomes have a central core of conserved genes. This consists of a minimum (core) set of 88 genes which contains genes essential for virus viability and encode proteins involved in viral DNA replication, transcription and virion assembly molluscum contagiosum virus, SQPV contains a thymidine kinase (TK) gene in the central region of the genome similar to the majority of other poxviruses. The SQPV TK gene, however, is not positionally conserved, in comparison to other poxviruses, and BLAST database searching suggests its closest orthologue is from the rat (Rattus norvegicus) and other rodents. This might suggest that the SQPV TK gene is a relatively recent, independent, acquisition and further analysis of its sequence may help in determining some of the evolutionary history of the virus.Unlike the parapoxviruses and the Molluscum contagiosum virus (MOCV) encodes one MHC class I homolog (MOCV080R gene product) Many viruses have evolved ways to interfere with natural killer (NK) cell activation The SQPV CD47 homolog shares 25–29% amino acid identity with a range of mammalian CD47 molecules and homology to the A38L protein found in Vaccinia virus  and a range of other poxviruses. It shares strong sequence similarity to the immunoglobulin superfamily domain of CD47 in vitroIn mammals, CD47 is widely expressed and its function is complex. It has been implicated in a range of T-cell regulatory functions including both inhibiting The interferon system is activated in response to viral infection and plays an important role in the host defence system. Interferons released into the extracellular space bind interferon receptors on neighboring cells to induce an alert system. The receptors activate the JAK-STAT pathway and induce a range of interferon stimulated genes. Various poxviruses encode proteins that either bind (or are predicted to bind) interleukin-18 (IL-18), an inducer of type II IFN, or are mimics of both type I and type II IFN receptors Double-stranded RNA-activated protein kinase is a key component mediating the antiviral actions of IFN that restricts viral replication by phosphorylating eukaryotic initiation factor-2a-subunit, an initiator of protein synthesis, reducing levels of viral protein n5′A, abbreviated to 2-5A) from ATP. RNase L, a latent endoribonuclease, becomes activated through binding 2-5As and degrades both viral and cellular RNA, including cellular rRNA OAS proteins are activated by dsRNA binding, leading to the OAS-catalysed synthesis of 2′-5′oligoadenylates (ppp(A2′p)in silico analysis, the SQPV-OAS1 gene is not conserved at amino acid residues known to be essential for normal OAS activity, lacking important aspartic triad residues By It is clear from these results that SQPV is both in a novel taxonomic position in the chordopoxviruse subfamily which indicates a possible new genus of poxvirus, and that this virus has a previously undescribed diversity of virulence genes encoded in its genome. It would seem that this virus has adapted to it's host niche using genes commonly associated with pox virulence , but has also acquired novel genes such as multiple MHC molecules, and a way of potentially inhibiting the 2-5A system of the host.The precise advantage and role of the specific virulence genes, and how they contribute to the difference in disease between grey squirrel populations in which it appears non-pathogenic and red squirrel populations in which it is emerging as lethal infection, is still to be determined.Figure S1Structural alignment of A. SQPV_004 (blue) aligned to class I MHC H-2Kk (light yellow) and β 2-microblobulin (red) with a peptide (green) in the peptide binding groove (based on PDB entry DOI:10.2210/pdb1zt7/pdb). Structural alignment of B. SQPV_027 (blue) aligned to class I MHC-like HFE (yellow) and β 2-microblobulin (red) (based on PDB entry DOI:10.2210/pdb1a6z/pdb) Structural alignment of C. SQPV_064 (blue) aligned to class I MHClike HFE (yellow) and β2-microblobulin (red) (based on PDB entry DOI:10.2210/pdb1a6z/pdb).(PDF)Click here for additional data file.Figure S2A. Predicted structure of SQPV_132 (VACV_Cop A38L) (Blue) with the extracellular domain of of CD47 (light yellow) and SIRPα (green) (based on PDB entry 10.2210/pdb2jjs/pdb). B. Alignment of SQPV_132 with CD47 antigen isoform 3 precursor [Homo sapiens] (NM_001025079.1. The cysteine residues that form a conserved disulphide bond between β sheets (PDF)Click here for additional data file.Table S1Gene table function and vaccinia virus homology. The table shows the location and annotation of the predicted SQPV genes plus the predicted ortholog in other species. The “Nearest Poxvirus GeneID” column the ID of the gene with the top blast hit. The Product column gives the annotation of the predicted product based on the best blast hit.(XLSX)Click here for additional data file.


Apple stem grooving virus (ASGV) as an exemplar virus. We used 15 publicly available transcriptome libraries from three different studies, two mRNA-Seq studies and a small RNA-Seq study.Next-generation sequencing (NGS) provides many possibilities for plant virology research. In this study, we performed integrated analyses using plant transcriptome data for plant virus identification using de novo assembled nearly complete genomes of ASGV isolates Fuji and Cuiguan from apple and pear transcriptomes, respectively, and identified single nucleotide variations (SNVs) of ASGV within the transcriptomes. We demonstrated the application of NGS raw data to confirm viral infections in the plant transcriptomes. In addition, we compared the usability of two de novo assemblers, Trinity and Velvet, for virus identification and genome assembly. A phylogenetic tree revealed that ASGV and Citrus tatter leaf virus (CTLV) are the same virus, which was divided into two clades. Recombination analyses identified six recombination events from 21 viral genomes.We in silico analyses using NGS data provide a successful application of plant transcriptomes to reveal extensive information associated with viral genome assembly, SNVs, phylogenetic relationships, and genetic recombination.Taken together, our The online version of this article (doi:10.1186/s12864-016-2994-6) contains supplementary material, which is available to authorized users. Apple stem grooving virus (ASGV) is a member of the genus Capillovirus in the family Betaflexiviridae [Chenopodium, Cucumber, Nicotiana, Phaseolus, and Vigna species [Apple chlorotic leaf spot virus (ACLSV), Apple mosaic virus (ApMV), ASGV, Apple stem pitting virus (ASPV), and Apple scar skin viroid (ASSVd) [iviridae , 2. ASGViviridae . In addiiviridae , and it  species . ASGV in species ; however species . In many (ASSVd) .Potato virus T, which were conserved among viruses in the families Alphaflexiviridae and Betaflexiviridae [The viral particles of ASGV are flexuous filaments 620–680 nm long and 12 nm wide . ASGV haiviridae .Next-generation sequencing (NGS) produces huge amounts of sequencing data, which facilitate the identification of known and novel viruses and viroids in a wide range of plant species . In addiMoreover, NGS systems are useful for virus–host interaction studies. For instance, sRNA-Seq has been used for virus-derived siRNAs (vsiRNAs) of ASGV from ASGV-infected samples . This stSeveral approaches to detect ASGV have been developed, such as long-distance PCR (LD PCR) to amplify the complete genome of ASGV , multiplSeveral recent studies have demonstrated that many plant transcriptomes contain viral sequences that could be applied to studies associated with virus identification and viral genome assembly , 24.in silico analyses using publicly available transcriptome data for viral genome assembly and identification using ASGV as an exemplar virus. We showed the application of transcriptome data for the analysis of single nucleotide variations (SNVs) on the ASGV genome. Moreover, the two viral genomes obtained were successfully applied in the phylogenetic and recombination analyses of known ASGV genomes.In this study, we conducted Malus domestica) and pear (Pyrus pyrifolia). Due to the clonal propagation of fruit trees, the possibility of virus infection is very high. We screened several apple and pear transcriptomes and selected the transcriptomes infected by ASGV for further study (data not shown). Of the several previously reported apple transcriptomes in response to ASGV infection, we selected two studies: one that performed mRNA sequencing (mRNA-Seq) [Of the known viruses, we selected ASGV, which mostly infects fruit trees, including apple (RNA-Seq)  and one RNA-Seq) . Both sede novo assembled transcriptomes of the two libraries using the Trinity program  [Previous studies have demonstrated that both mRNA-Seq and sRNA-Seq are useful for virus identification , 27. To ous (GD) . The datous (GD) .de novo transcriptome assembly using the Trinity program followed by a blast search to identify viral contigs. However, we obtained only 209 contigs with 425 bp of N50 value, and no ASGV-associated contigs were identified by the blast search. It seems that the Trinity program was not optimal for de novo transcriptome assembly using sRNA data. Thus, we used the Velvet program, which is well known for sRNA transcriptome assembly [The six libraries from ASGV-infected samples were subjected to assembly . The VelPyrus pyrifolia cultivar Cuiguan. The transcriptome was initially assembled by SOAPdenovo2; however, we performed de novo transcriptome assembly again using the Trinity program. A total of 33,858 transcripts were assembled  was derived from a mixture of nine different fruit developmental stages of the Apricot latent virus, Grapevine fleck virus, Rupestris stem pitting associated virus-1, ACLSV, Grapevine Pinot gris virus, and Zucchini yellow mosaic virus with very small numbers of reads were identified. Based on our knowledge, it seemed that the six identified viruses were not likely viruses infecting pears. They might have been sequences that were partially homologous to host genes or other viral genomes. In addition, associations of the six viruses with pears have not been reported. The sequence reads associated with Potato leafroll virus were identified as sequences from the host. Of four identified viruses infecting pears, ASGV was dominant followed by Prunus virus T (PrVT), Apple green crinkle associated virus (AGCAV), and ASPV.In order to identify additional viruses infecting pears, all raw data converted to FASTA format were blasted against the viral reference database. Interestingly, we found many additional viruses infecting pears Table . Of 11 vWe examined SNVs for ASGV isolate Cuiguan within the pear transcriptome after alignment of the raw data on the ASGV isolate Cuiguan Fig. . We founde novo transcriptome assembly, Trinity and Velvet. To find the advantages and disadvantages of the two programs, we compared the number of total contigs and sizes of viral contigs. We first compared the number of contigs of two different mRNA libraries assembled by the two programs  [Capillovirus. From the GenBank, we retrieved all ASGV-associated sequences as well as CTLV-associated sequences. After removing partial sequences, we collected a total of 21 genomes of ASGV and CTLV isolates, including two ASGV isolates in this study. The host ranges of CTLV were mostly from Citrus species as well as Lilium species  . To confdes Fig. . The firWe analyzed recombination events among 21 ASGV isolates. The aligned genome sequences were subjected to the RDP4 program, which includes nine different algorithms for recombination detection. The RDP4 program detected a total of 25 recombination events. Of them, we selected six recombination events that were supported by at least five recombination algorithms Fig. . Recombiin silico [de novo assembler for virus identification and viral genome assembly.The rapid development of NGS is enabling virologists to find viruses from numerous species , 31. NGSn silico , 24. HerThe majority of plant viruses are composed of RNA genomes, and DNA viruses also replicate via an RNA intermediate . Thus, Rde novo assembled from mRNA as well as sRNA data [de novo genome assembly. The number of pear  transcriptomes was about ten times that of apple transcriptomes . The sequence reads associated with ASGV were 7,668 viral reads out of 7,430,428 reads for the apple sample and 4,274 viral reads out of 97,896,223 reads for the pear sample. Although the number of total sequence reads in the apple sample was much smaller than that in the pear sample, the number of sequence reads associated with ASGV was about 1.8 times higher. This result suggests the amount of viral replication in the host might be also an important factor in de novo viral genome assembly. The portion of viral nucleic acids in the sample infected by virus is often low suggesting enrichment of virions prior to NGS [Prunus species followed by 454 pyrosequencing enabled to assemble four complete genomes of Asian prunus virus 1 (APV1), APV2, and APV3 [In this study, we used data from two different library types, including mRNA and sRNA libraries that were single-end sequenced by the HiSeq2000 system. According to many recent studies, viral genomes have been RNA data , 33. In r to NGS . For exaand APV3 . This stde novo genome assembly.In the case of sRNA, two different types were prepared based on size fraction . The libIn addition, sequencing methods are important for virus identification and viral genome assembly. In this study, all transcriptome data were single-end sequenced by HiSeq2000. As compared to single-end sequencing, paired-end sequencing provides sequences from both ends of a fragment and generates high-quality and alignable sequence data. The advantages of paired-end sequencing have been previously reported . Thus, pde novo assembler affects the quality and quantity of virus identification. For instance, mRNA data were very efficiently assembled by Trinity; however, few and low-quality contigs were assembled from the sRNA data by Trinity. Our comparative studies between the two de novo assemblers suggest Trinity and Velvet for de novo assembly of mRNA data and sRNA data, respectively. The obtained viral contigs assembled by Trinity from mRNA data were low in number but long in length, while the viral contigs assembled by Velvet were high in number but short in length. For the de novo assembly of a target virus with high-quality mRNA data, Trinity is ideal. Velvet cannot assemble a nearly complete viral genome, but it assembled many contigs, which enabled us to identify additional viruses, for example, viruses in the pear transcriptomes. Recently, several programs IVA, PRICE, and VICUNA for de novo assembly of RNA virus genome have been developed [de novo assembler might be dependent on researchers and purposes.For virus identification, assembled contigs are frequently used. Therefore, the choice of eveloped –45. The de novo assembly. We did not judge which ASGV genome was the dominant ASGV genome; however, it is highly likely that the de novo-assembled ASGV was a consensus genome sequence of ASGV. The mutation rates of identified ASGV genomes were varied: 1.38 % (90 SNVs) in the Fuji, 1 % (69 SNVs) in the GD, and 0.43 % (28 SNVs) in the Cuiguan. We suppose that several factors—including hosts, viral replication, and environmental cues—might affect the mutation rates. The association of viral mutation rate with other factors will be an interesting subject for further study [It is well known that RNA viruses have a quasispecies nature within the host . Howeverer study .de novo assemblers, and sample conditions for virus identification and genome assembly.Taken together, our study showed the successful application of plant transcriptome data for virus identification, viral genome assembly, and viral mutation rates. In addition, we discussed several factors, including library preparation, NGS systems, Malus x domestica cultivar Fuji (SRP034943), M. x domestica cv. Golden Delicious seedlings, grafted onto MM.109 rootstocks (SRP035543), and Pyrus pyrifolia cultivar Cuiguan (SRP041640).Detailed information for plant materials can be found in the previous studies , 25. In de novo assembly of transcriptomes, we used two different programs, Trinity version 2.0.6 and Velvet version 1.2.10 [De novo transcriptome assembly was performed according to the manuals provided by developers with default parameters.In this study, we used RNA-Seq data from three different projects. The first study employed mRNA-Seq data composed of two libraries derived from ASGV-infected and ASGV-free apple samples . The secn 1.2.10 , 50. De http://www.ncbi.nlm.nih.gov/genome/viruses/) with Evalue 1e-5 as a cutoff. In addition, all raw data were converted to FASTA files using the SRA toolkit and subjected to a MEGABLAST search against the viral reference database with Evalue 1e-5 as a cutoff.For sequence alignment on the reference viral genome, we used Burrows-Wheeler Aligner (BWA) software with default parameters  Standaloin silico approach.The viral contigs identified by the BLAST search were retrieved by the BLASTCMD program in the standalone BLAST system. To assemble ASGV genomes, the identified viral contigs were aligned against the ASGV reference genome (NC_001749.2) using ClustalW implemented in the MEGA6 program . The neade novo-assembled genomes were used. For ASGV isolate GD, the ASGV reference genome sequence was used for alignment. The aligned SAM files by BWA were converted into BAM files by SAMtools [In order to analyze SNVs of ASGV genomes, the raw data were aligned on each identified viral genome using the BWA program with default parameters. In the case of ASGV isolate Fuji and ASGV isolate Cuiguan, the SAMtools . For SNVSAMtools . BCFtoolSAMtools .http://www.ncbi.nlm.nih.gov/genbank/). After eliminating partial sequences, only complete or nearly complete genome sequences for ASGV and CTLV isolates were identified. A total of 21 genome sequences including two isolates in this study were aligned by the ClustalW program with default parameters. After alignment, we deleted unnecessary sequences and poly(A) tails at the 5′ and 3′ regions, respectively. The manually edited aligned sequences were subjected to the construction of a phylogenetic tree using the MEGA6 program. The phylogenetic tree was constructed by the neighbor-joining method with 1,000 bootstrap replicates and Kimura 2-parameter distance.To retrieve the ASGV genome sequences, we first retrieved all sequences related to ASGV from the nucleotide database in GenBank (We used Recombination Detection Program (RDP) version 4.66 . To iden


The role of contaminated clothing in the transmission of influenza A virus during an epidemic period was investigated by examining the recovery of infectious influenza virus from experimentally virus-contaminated clothing, which had been subejected to routine wearing and washing for several months or years. The amount of infectious virus recovered from the nine types of clothing decreased with time and was shown to differ widely between clothing samples, when the contaminated clothing samples were maintained in uncovered glass Petri dishes in a safety cabinet under air blowing. These results indicate a dependence of virus transmissibility on the nature of the contaminated clothes. The difference in recovery was shown to have no significant correlation with the thickness or the materials of the clothing; however, a correlation was observed with the residual amount of water in the deposited virus preparation on the test clothing. During the course of studies investigating the effects of natural products on the infectivity of viruses –6, the sOne potential source may be clothing, onto which the droplets containing influenza virus can be deposited. However, viruses are normally inactive outside of the infected cells or bodies. Thus, the viruses may be inactive following deposit on the clothing. By contrast, the viruses may be protected by the clothes from environmentally inactivating conditions. In the latter case, the binding of the virus to the clothes must be reversible in order for transmission to occur. As a result, the efficiency of virus transmission may be strongly affected by the survival of influenza virus on the clothes and by the reversibility of the virus binding to the clothes.et al  was grown in Eagle’s minimum essential medium  containing 5% fetal bovine serum. Influenza virus A/PR/8/34 (H1N1) (obtained from Dr Nakajima) was used throughout the experiments. The viruses were propagated in MDCK cells cultured in MEM supplemented with 0.1% bovine serum albumin (BSA) and acetylated trypsin (4 μg/ml). The viruses were stored at −80°C until required for further use. The amount of infectious virus was measured using a plaque assay on the MDCK cells, as described previously .6–107 plaque-forming units/ml) was placed on the surface of 15 (for triplicate samples) to 16 (for quadruplicate samples) pieces of the test cloths in a glass Petri dish, with a one minute interval. The contaminated cloths were left in the dish without cover in a safety cabinet  under air blowing. The average temperature and humidity in the cabinet was 27°C and 30%, respectively. At the indicated time points after virus deposition, one piece of the contaminated cloth was transferred into a glass test tube, immediately followed by the addition of 1,000 μl ice-cold Dulbecco’s phosphate-buffered saline (PBS) without Ca2+ and Mg2+, but containing 0.1% BSA. Vigorous mixing using a Vortex mixer was performed several times for a few seconds to completely elute the inoculated virus into the PBS from the cloths. All the test cloths were examined in triplicate or quadruplicate. The virus samples were maintained in an ice-water bath until the completion of sampling from all the test cloths for comparison. At the end of sampling, aliquots of these virus samples were serially diluted with ice-cold PBS containing 0.1% BSA, and the amount of infectious virus in the samples was measured. For a negative control without exposure to the test cloths, a 10-μl aliquot of the stock virus preparation was placed directly on the inner surface of the glass test tube with a loose aluminum cap. The sample was kept in the safety cabinet for the indicated period, followed by the addition of PBS containing 0.1% BSA at the indicated time.A 10-μl aliquot of the stock virus preparation . Although the droplets with a smaller size are likely to be dominant , in the As shown in the last column of Four clothing samples , that exhibited marked differences in the recovered virus infectivity at 20 min , were coPreviously, a non-physiological pH was shown to cause the inactivation of certain enveloped viruses ; thus, pIn addition, the observed differences were not found to correlate with the thickness of the material or with the types of materials of the sample cloth . HoweverAn aqueous environment is generally preferable for virus survival; as described above, water loss from the contaminated clothing may be associated with the observed virus recovery and inactivation. In a safety cabinet, rapid airflow maintains the sterility and bioprotective efficacy of the cabinet. This airflow caused the evaporation of water from the sample cloths over time. Time courses of evaporation from the jersey, cardigan, T-shirt and white sweater were evaluated  by examiTransmission of influenza virus is known to occur both directly and indirectly. The present study examined the recovery of influenza virus from experimentally virus-contaminated clothing, in order to understand the role of contaminated clothes in the transmission of influenza A virus during an epidemic period. Although the amount of infectious virus recovered from the contaminated clothes was markedly different between the clothing samples, the difference in recovery correlated with the residual amount of water in the deposited virus preparation on the test cloths. Considering that water from natural droplets would quickly evaporate once the droplet had fallen onto the clothes, indirect transmission of influenza through virus-contaminated clothing may occur, if only under limited circumstances. To understand the role of indirect transmission, further work is required; such as the study of the survival of viruses on contaminated hands and environmental surfaces, such as doorknobs and household furniture.


Experimental infection of Muscovy ducks with V4 strain of Newcastle disease virus was undertaken to determine the response of the ducks to the virus and the possibility of virus transmission to ducks and chickens in village like conditions. Twelve ducks were randomly and equally divided into three groups of control, inoculated and in-contact. Additionally, the chickens were placed into two groups of four animals each, namely in-contact and control. The inoculated and in-contact ducks and in-contact chickens were kept together. The eye drop route was used for inoculation and hemagglutination inhibition (HI) antibodies were measured for assessment of antibody response and cloacal and pharyngeal swabs were used for detection of the virus. The primary antibody response of inoculated ducks was very high and rapid (geometric mean titers [Log base 2] of up to 5.75 ± 0.50). The in-contact ducks showed antibody response with the same pattern but lower titers than the inoculated ducks (geometric mean titers [Log base 2] of up to 3.25 ± 1.70). The in-contact chickens showed a slight increase of HI antibody (geometric mean titers [Log base 2] of up to 2.25 ± 1.25) while the control chickens did not show any increase. The antibody response indicated the transmission of the virus to contact ducks and chickens. A single isolation of virus confirmed the ability of ducks to excrete the virus. It was concluded that the V4 strain of Newcastle disease virus was highly antigenic for ducks, and ducks can transmit it to other ducks and also in-contact chickens. The disease can be fatal for them. By serological tests and virus detection it has been shown that ducks can be infected by NDV.19 Sudharma and Sulochana have recorded that chickens in-contact with ducklings infected with Herts strain of NDV contracted the infection.5 According to Majiyagbe and Nawathe the velogenic NDV pathotype recognized in Nigeria has also been isolated from domestic ducks that have been mixed with the local breed of chicken.Serologically, it has been shown that experimentally infected ducks with velogenic NDV could transmit virus to in-contact free-range chickens.While the most strains isolated from ducks have been lentogenic, the studies on the transmission of NDV from ducks to chickens has been associated mostly with velogenic strains, hence the role of ducks in dissemination of lentogenic strains has remained unclear. The aim of this study was to test the response of ducks to V4 virus (a lentogenic strain of NDV) using antibody response, virus isolation and transmission of virus to in-contact ducks and chickens.Ducks and chickens. Ducks were obtained from a small flock kept at a private farm in Brisbane, Australia. The ducks were young adults and the females came into lay during the experiment. They were randomly divided into three groups: control, inoculated and in-contact groups (four in each). The breeds of the ducks in each group are shown in Virus and inoculation. The inoculum used was suspension of NDV-V4 in phosphate-buffered saline (PBS) with a titer of 107.5 EID 50 per 0.1 mL (50% chicken embryo infective dose). The titer was calculated by the technique of Reed and Muench.20 Strain V4 is a lentogenic strain of NDV which has been used as vaccine in chickens. The intraocular route was used for inoculation.Serological test and studies on antibody response. Blood was collected through brachial vein a week before inoculation and for three consecutive weeks after virus inoculation to study antibody responses. After the collection, blood was allowed to clot and kept in hot room (37 ˚C) for 3 hr. The serum was decanted and then frozen at –20 ˚C until tested. In order to remove natural agglutinins all the sera were treated with chicken red blood cells (RBC). For this purpose, a 10% suspension of RBC in dextrose-veronal-glucose (DVG) was centrifuged 3 min at 250 g. The fluid was sucked off, then the cells were suspended and one drop of the suspended cells were added to each serum sample by a Pasteur pipette, mixed and kept in refrigerator (4 ˚C) for 20 min and then centrifuged at 250 g for 15 sec. The serum was decanted and then tested using a micro hemagglutination inhibition (HI) test.21 Serial two-fold dilution of sera were reacted with 4 hemagglutination (HA) units of NDV-V4 for 20 min at room temperature. Chicken RBC (1% suspension) were added and the test was read after an additional 45 min. The last dilution with complete inhibition of HA was recorded as HI titer in the serum.Studies on transmission. The ducks in test group were exposed to the lentogenic strain of NDV-V4 by eye drop. A quantity of 0.1 mL of the virus suspension was administered into the eye. The frequency of sampling was scheduled to obtain information on the presence and transmission of the virus, and on the development of antibodies. Pharyngeal and cloacal swabs were taken from all birds daily for 14 post-inoculation days and then weekly for 3 weeks. The swabs were placed in ampoules containing 1 mL PSG  and frozen at –70 ˚C until tested. To attempt virus isolation, swabs for the first 14 days were tested individually. Then, samples were pooled, with four birds from each group being amalgamated on each collection day. A volume of 0.20 mL of transport medium was inoculated into each of three 10-day-old embryonated chicken eggs. Eggs were incubated at 37 ˚C. After 72 hr of incubation, they were chilled, opened and the harvested allantoic fluids were tested in a standard HA test.22 Serial two-fold dilutions of sera were used and geometric mean titers were expressed as log index to base 2. Clinical response. No specific clinical signs were observed in the ducks and chickens. In the second week of the experiment, only one duck showed weakness and died after one week. In histopathologic tests, the cause of death was associated with lymphoproliferative disease. No bacteria were isolated.Serological response of the different experimental groups. The mean of antibody titers of different groups are shown in Isolation of virus from swabs. The virus was only isolated once from the pharynx of one of the ducks in the inoculated group, 3 days after exposure.23,24 It has been used as vaccine in different parts of Asia. There have been many studies in assessing the antibody response of chickens to V4, but has not been any previous experience with the response of ducks to this virus. The V4 strain of Newcastle disease virus infects chickens by different routes, spreads rapidly between chickens and is heat resistant.1-13,18, 25 There is little information on the possibility of transmission of the lentogenic V4 strain to other ducks and chickens. Thus, this experiment tried to assess the response of ducks to V4 strain of NDV and also the possibility of its transmission via direct contact to ducks and chickens.There are reports about the potential dissemination of NDV by ducks, based on serological and also isolation of lentogenic, mesogenic and velogenic strains of the virus from domestic and feral ducks.There is no basic data on the levels of expected HI antibodies in ducks. Perusal of the pre-vaccination titers suggests that titer levels of up to 2 may indicate non-specific reactions in duck serum. The responses to vaccination supported this. Vaccine titers were consistently over 2, and individual titers of above 6 were recorded.13 the inoculated ducks in this experiment had a very high antibody response after one week. This supports other reports suggesting that NDV is highly antigenic for ducks.6,18 The HI antibody titers were much higher in ducks than chickens in the present experiment, and higher than the titers usually recorded in chickens. The immune response in the in-contact ducks indicates the transmission of the virus between ducks. The results could have some application when this strain of the virus is considered for vaccination especially in village conditions.In contrast to one report showing failure of immune response in ducks exposed to NDV,3) are protective.26 Potentially protective levels of antibody were not achieved by the third week. This modest response of chickens to primary exposure to the V4 virus was similar to previous studies with the V4 strain of NDV.23 In contrast, the control chickens gave no serological evidence of contact with NDV.In-contact chickens there was slight increase in HI antibody that indicated they had also been infected by the virus, but the amount of antibody in the first week was not protective. According to Allan and Gouph the HI titers of 8 (i.e. 2According to this study, it is suggested that in village conditions where the ducks and chickens are kept together or their chance of contact is highly possible, simultaneous vaccination of chickens and ducks is advisable.


Bovine viral diarrhea virus (BVDV) is a common pathogen of cattle herds that causes economic losses due to reproductive disorders in breeding cattle and increased morbidity and mortality amongst infected calves. Our objective was to evaluate the impact of BVDV spread on the productivity of a beef cow-calf herd using a stochastic model in discrete time that accounted for (1) the difference in transmission rates when animals are housed indoors versus grazing on pasture, (2) the external risk of disease introductions through fenceline contact with neighboring herds and the purchase of infected cattle, and (3) the risk of individual pregnant cattle generating persistently infected (PI) calves based on their stage in gestation. The model predicted the highest losses from BVDV during the first 3 years after disease was introduced into a naive herd. During the endemic phase, the impact of BVDV on the yearly herd productivity was much lower due to herd immunity. However, cumulative losses over 10 years in an endemic situation greatly surpassed the losses that occurred during the acute phase. A sensitivity analysis of key model parameters revealed that herd size, the duration of breeding, grazing, and selling periods, renewal rate of breeding females, and the level of numerical productivity expected by the farmer had a significant influence on the predicted losses. This model provides a valuable framework for evaluating the impact of BVDV and the efficacy of different control strategies in beef cow-calf herds. Bovine viral diarrhea virus (BVDV) affects most industrialized cattle farming systems by inducing reproductive disorders  in breeding cattle and by lowering herd productivity through increased culling, morbidity, and mortality . IntroduModelling is a pertinent approach to predict pathogen spread and persistence in a herd and to evaluate its impact on herd dynamics and productivity for a large range of management scenarios . Many ofOur objective was to evaluate the impact of BVDV spread on the productivity of a beef cow-calf herd across a large range of management scenarios. A stochastic epidemiological model was proposed that accounted for different transmission rates between separately managed production groups during the outdoor grazing period versus a homogeneous population structure during the indoor period. The model also incorporated an external risk of BVDV introduction through fenceline contacts with neighboring herds as well as through animal purchases. A sensitivity analysis was conducted to determine the relative importance of management factors such as herd size, the length of breeding, grazing, and selling periods, the replacement rate of breeding females, and level of numerical productivity expected by the farmer. Comparisons of production losses in acute outbreaks and endemic situations were also performed.A stochastic compartmental model in discrete time was developed to simulate the spread of BVDV. A time interval of 7 days was chosen as the longest as possible to properly represent transiently-infected animals in the infection process. The model was fully implemented in C++, allowing the model to be run rapidly.The farming system modelled and used for simulations in this work was based on the characteristics of beef cow-calf herds in Bourgogne, one of the main beef production regions in France. In this area, animals are indoors during the winter and most of them are outdoors from spring to autumn Figure . Purchasε , heifers under the age of two kept for renewal, bred heifers, cows from the first pregnancy diagnosis until fattening decision, cows from fattening decision until culling, and bulls. The herd dynamics relies on specific dates when animals change groups Figure ; Table 1ρsex ,  and . The proportion of animals sold at each period was fixed at the beginning of each simulation.The number of bulls present in the herd was assumed to be constant. The model assigns one bull per 20 bred heifers or cows. Each year, on November 1st, a bull was randomly selected for replacement.S), transiently-infected (T), recovered, i.e. immune (R), protected by maternal antibodies (M) or persistently infected . The M to S and T to R transitions depend on transition rates ϕMS and ϕTR  in the herd.Animals were classified into mutually exclusive BVDV health states Figure : susceptTR Table . Since mf is given byDuring the indoor winter period, the risk of infection is assumed to be equally distributed among all animals. In that case, the transmission rate NP and NT are the total numbers of PI and T animals in the herd, respectively; N the herd size, and βP and βT the transmission rates per day associated with the PI and T animals, respectively  plus remaining bulls. We assumed this latter group to be homogeneously mixed. Virus transmission outdoors was due to transmission within each group (with the same formulation as indoors) and between pairs of groups  gave birth to calves protected by maternal antibodies (M) acquired via colostrum. Susceptible (S) or PI (P) cows gave birth to calves in the same state. If mothers were infected during pregnancy, consequences differed depending on the stage of the pregnancy at the time of infection. As females are individually monitored during pregnancy, their stages are known precisely. The pregnancy period was divided into three stages: early pregnancy , mid pregnancy , and late pregnancy . Infection during the first stage led to either embryonic or fetal death with probability αRa (Table M). Different consequences of infection during mid-pregnancy are possible [αRb . In case of abortion, a delay of 60 days is applied between the end of infection and abortion. The female is then unavailable for breeding for 20 days. If abortion occurs not too late, i.e. at least 20 days before the end of the breeding period, the female may return to the pregnancy state using the algorithm explained in the previous section.Immune cows  and τij the daily transition rate between compartments i and j. The transition ΔNij is then calculated aswhere Ni represents the number of individuals present in compartment i. In the case of multiple transfers, we used multinomials instead [where  instead . The traFemales in the group of young heifers stayed two years in that group. For this group, all compartments were doubled to differentiate one-year and two-year-old heifers. At the beginning of the reproduction period, only two-year-old heifers were either selected for breeding and transferred in the bred heifer group, or sold.μP,bi and μCa,bi, respectively .Purchased animals  can be of any health state in the model (st October), corresponding to week 0 . For each scenario considered thereafter, 3000 repetitions were performed.A simulation year started after weaning , and the prevalence of PI and T animals and of immune dams carrying a PI fetus (state RP). Prevalence of PI and T animals represents the proportion of PI and T animals in the whole herd while prevalence of immune dams carrying a PI fetus is restricted to breeding females only. Outputs related to herd productivity are the number of losses , purchases , weaned calves, sales of grassers and young heifers, and sales of empty and fattened females. To evaluate the impact of BVDV on herd productivity, we subtracted the contribution of the reference year (the last year before the first BVDV introduction) from these last outputs, considering only the relative change with and without BVDV circulating. Losses and purchases were also evaluated per bred female to remove the direct impact of herd size on such outputs.For all outputs except virus persistence, we calculated the annual median value with an 80% credible interval (P10-P90) for each year after BVDV introduction by selecting only repetitions in which the virus was still present in the herd at the end of the year, i.e. at weaning. Virus persistence was calculated weekly.Pb = 0).To test the effect of our assumptions regarding the structure of herds during the indoor and outdoor periods, two options were compared with the reference scenario: (1) no structure is considered, assuming all animals are homogeneously mixed as indoors; (2) three groups are considered as in the reference case but with no contact between them  and outdoor (pasture_dur) periods, renewal rate of breeding females , herd size (size), and period of selling (sell_period);parameters related to the herd management: level of numerical productivity expected by the farmer μP,bi), mortality of PI animals (μP), transition rates (ϕMS) and (ϕTR), transmission rates  and mid-pregnancy (αRb), probability of giving birth to a PI calf for a dam infected during mid-pregnancy (ηP), and type of virus introduction (intro_week).parameters related to the infection dynamics: mortality at birth of PI animals . The selected input parameters were the following:ε for which the variation was ± 5% to remain within a plausible range, and for ηP which cannot be above 1). For other parameters , we tested for plausible values. Three selling periods are possible in the field, animals (including PI) being kept longer or shorter accordingly. We tested for selling all of the sold animals at each of these 3 periods. Durations of the breeding and the pasture periods varied by ± 2 and 3 weeks, respectively. To cover the variety of herd sizes in the Bourgogne region, we tested three numbers of females kept for breeding: 42 (10 heifers - 32 cows), 83 , and 125 (30 heifers - 95 cows). Finally, BVDV introduction may occur through different ways in a naive beef cattle herd. In addition to the birth of a single PI , we tested the case of multiple births of PI calves , and the case where a PI replacement calf is purchased during the outdoor period (week 40). Other purchased animals could also be infected and therefore introduce BVDV in the herd. However, pregnant females are purchased at the start of the building period, thus when most of the females are in late gestation. Introducing a transiently infected female will barely have any effect. Introducing an immune dam carrying a PI fetus will have the same influence as introducing a PI calf at birth. Introducing a PI pregnant female is quite rare. Lastly introducing a PI bull could have a large effect but only if introduced directly in a group of bred females among which some are already pregnant, which also corresponds to the period of purchase of replacement calves. Therefore, we chose to present here the most probable cases that are the birth of PI calves and the purchase of a PI replacement calf.Three values were tested per parameter Table . For conpn scenarios when n parameters with p levels are considered. Using a fractional design  enabled us to considerably reduce the number of scenarios and is appropriate when sensitivity indices for principal effects and first-order interactions only are estimated. Two thousand one-hundred and eighty-seven scenarios were run for each analysis.Since herd size and the type of virus introduction in the herd were expected to largely impact model outputs, 9 (3 herd sizes × 3 types of introduction) sensitivity analyses were carried out to evaluate the effect of other model parameters. We used a fractional factorial design to sample parameter values . A factolosses  and of the prevalence of T (prevT) and PI (prevP) animals, and of immune dams carrying a PI fetus (prevRP), in an infected herd.We analyzed aggregated outputs calculated as the mean values over the first 5 years after BVDV introduction of k, a linear regression model (ANOVA) was run with all model parameters: kij … = μ + f + ϵ, with μ a constant, f the relation between factors , and ϵ the residual. The total sum of squares then writes: i and j), with i and to the first-order interaction between factors i and j for output k, respectively. The contribution of factor i to variations in output k is R2.For each output Kext). As no information was available on observed within-herd prevalence of BVDV infection in infected herds, we assumed a risk of purchasing infected animals on our best knowledge  and assuming two levels of regional prevalence of infected herds: weak (10%) and strong (50%). For fenceline contacts, we evaluated three levels of external risk: nil (Kext = 0), weak (Kext = 0.0025), and strong (Kext = 0.01). Each case was tested for each of the three herd sizes and type of initial virus introduction which is expected to impact the acute phase.Five years after BVDV first introduction, if the virus is still present in the herd then an endemic state has been reached. To evaluate the impact of BVDV spread in such an endemic situation, cumulated outputs over 10 years (year 6 to 15 after virus introduction) were calculated to enable a comparison between the endemic situation and the acute one (based on the first 3 years). Only repetitions for which virus was present at least one week were included. Moreover, cumulated outputs were normalized to account for the proportion of time the herd truly was infected. Outputs thus were multiplied by the ratio of the number of weeks the virus was present in the herd over the total duration in weeks of the acute and the endemic periods, respectively. The comparison between the acute and the endemic periods was initially evaluated without allowing virus reintroduction through purchases of infected animals or fenceline contacts with infected neighboring herds during the outdoor period. As these factors can significantly influence disease persistence, we also simulated BVDV reintroduction in the herd accounting for a probability of purchasing infected animals  and for a probability of fenceline contacts with neighboring infected herds during the outdoor period  or through the purchase of a PI replacement calf (week 40) gave rise to virus persistence in almost all of the repetitions for 1 to 3 years, whereas the birth of a single PI (week 27) was followed quickly by a 10% drop in persistence . The prevalence of transiently infected animals was the highest during the year of virus introduction (year 1) when 3 PI were successively introduced. In the other scenarios, it was the highest the second year. In medium herds, after the birth of a single PI calf, the prevalence on year 2 of T, PI and dams carrying a PI fetus in 80% of the repetitions ranged from 1.6 to 2.0%, 1.0 to 3.6%, and 2.8 to 6.6%, respectively .The predicted virus persistence slightly increased when assuming a homogeneous mixing compared with a heterogeneous one, especially for large herds and for risky types of virus introduction , purchases, and sales of empty and fattened females were slightly higher during the acute phase when assuming a homogeneous mixing outdoors, regardless of herd size and type of virus introduction.Herd management and infection characteristics both influenced losses over the first five years of infection, as well as the prevalence of T animals, PI animals, and dams carrying a PI fetus Figure . The typLosses varied among scenarios of the sensitivity analysis in the ranges 8-15, 13-30, and 18-42 animals in small, medium, and large herds, respectively. The prevalence of T animals varied among scenarios between 0.8 and 1.6%, the prevalence of PI animals varied between 0.9 and 3.0%, and the prevalence of dams carrying a PI fetus varied from 1.4 and 4.8%, irrespective of herd size.pasture_dur) and the choice of the selling period (sell_period) for parameters related to herd management, and by the abortion rate due to infection in early pregnancy (αRa) and the transmission rate by PI animals (P) for parameters related to infection characteristics  and of PI mortality (μP).Losses variations were mainly explained by pasture duration , the transient infection duration (ϕTR), and slightly by pasture duration (pasture_dur), the transmission rate by PI animals (βP), and PI mortality (μP). The variations in the prevalence of PI animals , and in some cases pasture_dur, as well as by the probability of given birth to a PI calf for dams infected in mid-pregnancy (ηP), μP, and in some cases βP. The variations in the prevalence of dams carrying a PI fetus  corresponded to BVDV reintroductions during the endemic phase once every 6-7 years for the low level, and once every 2-3 years for the high level  between the indoor period, during which a homogeneous contact structure is assumed, and the outdoor period, during which groups are formed and raised on different pastures. Moreover, we accounted for a risk of continuous virus introduction in the herd through animal purchases and contacts with neighboring infected herds. This enabled the model to represent a large range of possible situations, from a single introduction into a naive herd to endemic situations potentially maintained by an external risk of virus reintroduction. Lastly, the model was flexible enough to represent beef herds of different sizes and management types, as illustrated by the different types of herds observed in Bourgogne, one of the main beef cattle farming regions in France. Such a model was pertinent to investigate the impact of BVDV spread on the productivity of a herd for a large range of scenarios.To precisely represent the infection process, several modelling choices had to be made. The model was stochastic to enable an estimation of the probability of virus persistence. The variability in model outputs if the virus persisted was not very large. However, the prevalence in PI animals and in immune dams carrying a PI fetus in infected herds was low and therefore better estimated using a stochastic model. A discrete time step of 1 week was used as the longest permitting to precisely estimate the morbidity related to transiently infected animals (transient infection being quite short). We chose to implement a combination of compartmental and individual-based models - instead of using a fully individual-based approach as used in ,35 – witThe simulation results show that failing to account for the separation of cattle into different management groups on pasture could lead to overestimations of the predicted disease prevalence and persistence in affected herds. In our study, we assumed that there were three management groups and that these management groups remained fixed over the entire grazing period. However, in the real world, herd structure is often dictated by complex management constraints such as herd size, pasture availability, and labor resources. Furthermore, the management groups of bred females with bulls may be reformed several times during the grazing period, which may lead towards more homogeneous mixing dynamics. There is a need for further research into the effects of herd structure on BVDV spread using empirically derived data.When introduced into a naive herd, BVDV spread was shown to have a large impact on herd productivity, especially the first 3 years after the initial introduction of the virus, during which yearly losses may be up to 6 times higher than in subsequent years when herd immunity has developed. The impact expressed per bred female was slightly lower in larger herds. Moreover, in the absence of control measures, BVDV may persist for years, which is currently observed in some regions ,36,37. WThe sensitivity analysis shows that herd management  and the infection dynamics  were very influential processes on at least some of the model outputs , regardless of herd size and type of virus introduction. As for dairy herds , it suggAmong the available strategies to control BVDV spread in beef herds, vaccination is one of the main current options. According to our findings, female vaccination before breeding seems to be a valuable strategy to limit losses due to BVDV spread and persistence -43. Vacc


To the Editor: In late June 2012, a previously healthy, 49-year-old woman was admitted to the emergency department of Trikala General Hospital in Trikala, Greece, with confusion and delirium. A few hours before admission, she had had a grand mal seizure; she had experienced gastroenteritis with fever (38°C) 5 days earlier. On admission, she was intubated and transferred to the intensive care unit, where she underwent mechanical ventilation and sedation. 3, 92% neutrophils) and slightly elevated serum lactate dehydrogenase level (240 U/L). Brain imaging showed edema , 22 km west of Trikala in the Thessaly region. She had not traveled abroad or to other area of Greece. Results of blood and cerebrospinal fluid (CSF) laboratory testing were unremarkable except slight leukocytosis  species (Because West Nile virus (WNV) infections emerged in 2010 in Greece and outbreaks have recurred , and Cyprus virus . The first sample yielded negative results, but the follow-up sample showed IgM and IgG against TOSV and SFNV (both belonging to SFNV serocomplex). Neutralization testing to differentiate TOSV and SFNV was not performed because PCR and sequencing confirmed the TOSV infection.Bunyaviridae) are endemic in Mediterranean countries, and at least 3 serotypes are associated with disease in humans: TOSV, SFNV, and SFSV. Among these, TOSV is associated with neurotropism, a major cause of meningitis and encephalitis in the Mediterranean region (Sandfly-borne phleboviruses (family Phlebotomus neglectus (31.2%), followed by P. similis (25.1%) and P. tobbi (15.3%) (P. perfiliewi and P. papatasi (known vectors of TOSV) accounted for 83.4% and 3.93%, respectively, of the sandflies collected  showed that the sandfly species with the widest distribution was For this patient, TOSV was detected by using phlebovirus generic primers. The TOSV sequence found in Greece differs greatly from other TOSV sequences, even from the genetically closer Croatian TOSV sequence (15%). To avoid false-negative results, the high genetic diversity among TOSV strains must be taken into consideration when using TOSV-specific primers.In conclusion, a novel variant of TOSV has been detected in Greece. Further studies are needed to obtain a whole-genome sequence of the Greek TOSV strain and to identify the vector(s) of the virus. TOSV is a highly variable neurotropic phlebovirus, a characteristic that must be taken into account by laboratory scientists. Clinicians should be aware of the possibility of phlebovirus infections in Mediterranean countries and should include these viruses in the differential diagnosis of febrile illnesses observed during the warm seasons, especially in patients who exhibit neurologic symptoms.Computed tomography scan image of the brain of a 49-year-old female patient at admission to the emergency department of Trikala General Hospital, Trikala, Greece, June 2012. A Toscana virus strain was later detected in the patient.