A multitude of resistance deployment strategies have been proposed to tackle the evolutionary potential of pathogens to overcome plant resistance. In particular, many landscape‐based strategies rely on the deployment of resistant and susceptible cultivars in an agricultural landscape as a mosaic. However, the design of such strategies is not easy as strategies targeting epidemiological or evolutionary outcomes may not be the same. Using a stochastic spatially explicit model, we studied the impact of landscape organization  and key pathogen life‐history traits on three measures of disease control. Our results show that short‐term epidemiological dynamics are optimized when landscapes are planted with a high proportion of the resistant cultivar in low aggregation. Importantly, the exact opposite situation is optimal for resistance durability. Finally, well‐mixed landscapes  are optimal for long‐term evolutionary equilibrium (defined here as the level of long‐term pathogen adaptation). This work offers a perspective on the potential for contrasting effects of landscape organization on different goals of disease management and highlights the role of pathogen life history. However, such cultivars may rapidly become ineffective owing to the well‐documented ability of pathogens to evolve and overcome plant resistance genes  or spatially , or combinations of some or all of these cultural practices Brown, . The effIn the few simulation models developed to investigate the case of agricultural systems, most describe agricultural landscapes of susceptible and resistant cultivars allocated to different fields as a mosaic. In this way, the impact of several landscape characteristics  on disease control was evaluated in different pathosystems. One general result that emerges from many spatially explicit but purely demographic models . The landscape is composed of fields where a susceptible crop cultivar and a resistant crop cultivar are sown in controlled proportions and degree of spatial aggregation. The crop is present all year‐round although plant cover is reduced during the off‐season. For a given simulation, the spatial structure of the landscape is assumed to remain the same across years.The two crop cultivars impose selection pressure on the pathogen population through a single life‐history trait, the efficacy of infection. Thus, pathogen genotypes are characterized by their associated ability to infect the two host cultivars. We assumed that the resistant cultivar consists of a pyramid of both qualitative and quantitative resistances. Thus, at the beginning of a simulation, the pathogen population is only adapted to the susceptible cultivar and cannot attack the resistant cultivar. However, new pathogen genotypes can progressively arise through mutation, resulting in the emergence of genotypes associated with gradually increasing infection efficacy on the resistant cultivar. These new pathogen genotypes are only partially adapted to the resistant cultivar because of the presence of quantitative resistance. In addition, they are penalized by decreasing infection efficacy on the susceptible cultivar to account for life‐history trade‐offs, as has been documented for several rust pathogens  and the other genotypes correspond to those associated with gradually increasing infection efficacy on the resistant cultivar. These genotypes are considered as generalists with different degrees of specialization as they can infect both cultivars, at least to some extent. The model then describes the dynamics of the number of foliar sites in each of the following states and for each field i : healthy sites (Si), latent sites infected by pathogens of genotype p , infectious sites infected by pathogens of genotype p  and removed sites infected by pathogens of genotype p . Spores produced by infectious sites correspond to the propagule state.We consider a Susceptible‐Exposed‐Infectious‐Removed (SEIR) model with foliar sites as individuals . The host population is composed of two genotypes (corresponding to the susceptible and resistant cultivars) and the pathogen population of Epidemics were simulated over 50 years, each composed of 12 months of 30 days. This duration was chosen to ensure detection of the long‐term impact of the deployment strategy on disease epidemics and pathogen evolution. The cropping season formed the first 120 days of the year, whereas the off‐season was represented by the remaining 240 days of the year. Below, we describe each sequential step of the model as it occurs during a cropping season. The full description is given in the Appendix p = 1). Epidemics are initiated by assuming that plants in susceptible fields are randomly infected with a probability of .01 (infected plants at t = 0 are at an infectious stage).For each simulation, the pathogen population is initially composed of the specialist of the susceptible cultivar , we assume that the pathogen population evolves gradually: a new genotype arises from closely related genotypes by mutation with small gains or losses in infection efficacy by setting mp(p‐1) = mp(p+1) = 0.002. The exceptions are pathogen genotypes with the highest infection efficacy on either susceptible or the resistant cultivars—these mutate towards less specialized genotypes with a probability of .004 to ensure their overall mutation rate is equal to that of other genotypes.Infectious sites produce i to field j with probability μij computed from:Spores migrate from field Ai and Aj are the areas of fields i and j, respectively  and is varied to represent 2.5%, 10% and 25% of the landscape length (noting that the simulated landscape is square).Where π(xi(t)), where π(·) is an increasing function of xi(t), the proportion of healthy sites in field i at time t. Indeed, all of the infection sites on an individual plant are not equally accessible to spores, for instance because of the plant physical structure. Then, the possible new infections are distributed among the pathogen genotypes according to their proportion in the set of spores arriving in the field i at time t and following a multinomial distribution.Spores arriving in a field invade a healthy site with probability ep,v(i) the infection efficacy associated with pathogen genotype p on crop cultivar v(i) cultivated in field i.A healthy site receiving a spore (invaded site) becomes infected with probability ep,RC and ep,SC for the resistant and the susceptible cultivars): a gain in infection efficacy on the resistant cultivar has a cost in terms of reduced infection efficacy on the susceptible cultivar (and vice versa). Gain and cost are linked through the relationship . In addition, we consider that only healthy sites (Si) contribute to biomass production . At the end of the cropping season an arbitrary proportion of 99.9% of the sites is removed randomly, regardless of their infection state, and the total number of sites is kept constant during the off‐season .To initiate a cropping season, plant cover is set to 10% of the field acreage. The crop then grows locally until it reaches the carrying capacity of the field, 2.2−1) and mean distance of spore dispersal . For each combination of these five parameters, 20 replicates were simulated . This resulted in a total of 5400 simulations.Simulations were performed using a complete factorial design to explore the five input parameters of interest: cropping ratio , landscape aggregation , strength of the trade‐off between infection efficacies , spore production rate  allowed us to retain the most parsimonious models Table . Secondl3In all, 7.2% of simulations were discarded because the pathogen population went extinct in the initial stages of the simulation as a result of demographic stochasticity during the off‐season (when only a few hosts were available). Pathogen extinction occurred most frequently in mixed landscapes with a high proportion of the resistant cultivar and when life‐history trait parameters were clearly to the disadvantage of the pathogen .In the remaining simulations, the epidemics are characterized by an initial period of immunity of the resistant cultivar Figure . Then, n3.1r = 5) or 40% (r = 10) of the variability in short‐term disease dynamics (Table In the short term (during the phase when the resistant crop cultivar is still immune to disease), the HAD of the susceptible cultivar ranged from 11.7% to 73.2% over all simulations (the median was ~50%). From the GLM analysis, a lower amount of disease in the short term occurred with lower spore production capacity  postintroduction. Increases in pathogen dispersal ability and spore production decreased resistance durability while stronger trade‐offs resulted in more durable resistance (Table 3.3r = 5) and 35% (r = 10) of the variability of long‐term evolutionary equilibrium  and the HAD of the agricultural landscape at the end of the simulation was dependent on the global level of adaptation of the pathogen population. Over all simulations, the HAD of the entire landscape ranged from 17.2% to 62.4% (the median was 26.7%). In 72.6% of simulations, two pathogen genotypes coexisted in the population at the end of the simulation; each of them being more adapted to one of the two cultivars  in response to pathogen demography and evolutionary dynamics. This study is based on a stochastic and spatially explicit SEIR model applied to a foliar fungal disease as typified by cereal rusts. It offers a perspective on the strong differences that may exist between management strategies of resistance deployment accounting for different measures of disease risk, derived either from epidemiological or evolutionary perspectives.The effect of the combination of susceptible and resistant cultivars at field (mixtures) and landscape (mosaic) scales on disease development has been classically studied in the literature Mundt, . It has The optimal spatial structure of the landscape is different when our second criterion (resistance durability) is taken into account. Durability of resistance genes as a function of the deployment strategy has previously been studied using nonspatial models  is optimal for resistance durability. Finally, well‐mixed landscapes  are optimal for long‐term evolutionary equilibrium of pathogen populations.Pisum sativum L.). In our model, temporal heterogeneity was considered through seasonality in host density. Indeed, for biotrophic pathogens, host harvest can represent a severe bottleneck, potentially eliminating rare adaptive mutations. We assumed that, during the off‐season, host genotypes remain the same as during the cropping season and that individual plants are homogenously distributed within a field. These assumptions limit the effect of spatial structure on both off‐season eco‐evolutionary dynamics (Tack & Laine, Nevertheless, these conclusions must be nuanced by the theoretical context of this study. The target of this work was to specifically explore the short‐ to long‐term impacts of spatial heterogeneity on resistance durability and disease control. Thus, we considered that the landscape organization remained the same for the whole simulation run. In practice, the same resistance genes may be introgressed into many commercial cultivars and continuously used for long periods of time although particular cultivars containing these resistance genes may turn over frequently Johnson, . HoweverFuture investigations using our modelling approach may include consideration of additional temporal heterogeneity in the landscape as well as the simulation of more complex resistance deployment strategies, which are likely to be better approaches to deploy plant resistance (Fabre et al.,  Click here for additional data file.


Electronic portal imaging devices (EPIDs) are potentially valuable tools for linear accelerator quality assurance and for measuring and analyzing geometric variations in radiation treatment delivery. Geometric analysis is more robust if referenced against an absolute position such as the isocenter (collimator axis of rotation), allowing the observer to discriminate between various setup errors and jaw or multileaf collimator (MLC) calibration errors. Unfortunately, mechanical instabilities in EPIDs make such analysis difficult. In the present work, we describe how MLC interleaf radiation leakage, hidden in the background of portal images, can be extracted and analyzed to find the field isocenter perpendicular to leaf travel direction. The signal from the interleaf radiation leakage is extracted to provide a precise and accurate determination of the isocenter location in the direction perpendicular to MLC leaf travel. In the direction of leaf travel, the minimization of residuals between planned and measured leaf positions is used to determine the isocenter. This method assumes that leaf positioning errors are randomly distributed. The validity of the method for determining the angular deviation between EPID image grid lines and collimator angle and for determining the known isocenter position is experimentally established.PACs numbers: 87.53.Oq, 87.53.Xd, 87.57.NK The efficacy of the EPID for this application is limited by poor image quality,2  can be used to enhance contrast by allowing various features in the portal image to be extracted and identified for positional analysis. Image noise reduction is often achieved through frame averagingAlgorithms such as adaptive histogram equalization enhancement,5 ca(–) Starting from a portal image, machine errors and setup errors can be distinguished only if an absolute reference point such as the intersection of the collimator axis of rotation with the image plane can be determined on each image. Many EPIDs are deployed only when images are required, and the EPIDs are susceptible to mechanical instability such that their position, with respect to the beam axis, can vary significantly with gantry angle and from day to day.Many portal image applications and various clinical studies attempt to differentiate between the geometric effects of organ or target motion, patient setup error, and machine alignment errors .–13 S The simplest method is to use small high‐Z markers (BBs) mounted in an acrylic tray and placed in the shielding tray holder of the linear accelerator (LINAC). This approach is inappropriate for our work, because the BBs often obscure the patient's fiducial markers, making it difficult to extract anatomic information from the EPID images, and, less importantly, the BBs cause minor dosimetric anomalies.For our Siemens KD Mevatron , we have measured variations greater than 10 mm for the point (pixel) at which the collimator axis of rotation intersects the image plane of the TI EPID. The magnitude and direction of this variation depends on gantry angle, but is not reproducible from deployment to deployment. Several methods of correcting for image placement resulting from this instability have been reported in the literature.,14 THere, we present a method for accurately determining the intersection of the collimator axis of rotation with the EPID image plane, making geometric treatment analysis more robust. The approach is based on an inherent property of MLCs. Adjacent leaves of MLCs have a small gap between them, allowing a measurable amount of radiation (up to 2%) to be transmitted.26.5×24.8 cm projected to the plane of the isocenter and a square pixel dimension of 0.518 mm per side. The Siemens MLC consists of two opposed banks (X1 and X2) of 29 leaves each, defining the collimated field in one direction. The central 27 leaves of each bank project to exactly 1 cm width in the plane of the isocenter; the two outer leaves of each bank project to a width of 6.5 cm. A signature of the MLC is the interleaf leakage, which in the Siemens LINAC is about 1.5% of the central‐axis open‐field dose. The machining tolerance of the MLC leaves is very tight, resulting in a highly accurate and reproducible spacing of 10.0±0.1 mm of the interleaf leakage pattern at the isocenter. The positional accuracy of the MLC in its direction of travel has a specification of ±2 mm at isocenter, which is a principal cause for uncertainty in using field‐matching techniques to determine beam isocenter. In the direction perpendicular to leaf travel, the field edge is defined by solid tungsten jaws with a specified positional accuracy of ±2 mm.The present study was based on a Siemens Digital Mevatron LINAC equipped with a 58‐leaf double‐focused MLC  and a Beamview TI electronic portal imager. The LINAC produces X‐ray photons with nominal energies of 6 MV and 18 MV. The imager consists of a gadolinium oxysulfide screen viewed through a mirror by a video camera and lens system placed at 90 degrees. The imager has a field of view of The open field portal (treated field)The area shielded only by the MLC leavesThe area shielded by the solid jawsFor the present work, we segmented the portal image into three zones as shown in Fig. Two segments, one from each of the regions shielded only by the two MLC banks, were selected for analysis. The length of the selected region was limited in the Y direction by the field size defined by the solid jaws. The width of the region was selected to be about 5 cm. The region was always at least 0.5 cm from the open field. These two regions were analyzed for interleaf leakage. Recently it has been used for image processing—particularly for pattern recognition—because of its ability to transform line patterns in the image domain into dot patterns in the Radon domain. A domain of possible line parameters can be easily determined.The Radon transform has been widely used in image reconstruction from projections for imaging modalities such as computed tomography and magnetic resonance.17 Recf along direction θ) is defined asA Radon transform  is the image intensity at location x,y, and r=xcosθ+ysinθ is the perpendicular distance from the image origin (image center) to the straight line along which the ray‐integral is evaluated. Equation f along a particular angle θ.where δ is the Dirac delta function, Equation When appropriately applied, the Radon transform is able to identify and extract weak lines embedded in very noisy images. In the present work, we used this property to extract interleaf radiation leakage from the background noise in the portion of clinical portal images shielded only by the MLC leaves. Note that, the lower the contrast and the noisier the image, the longer the integration line must be for accurate analysis. to substantially improve the contrast of the interleaf leakage.Fig. ,5 togθ(r) of the image f at 0 degrees  and 90 degrees (perpendicular) to the direction of motion of the MLC leaves.Fig. gθ(r) can be described as a stationary signal, modulated by the interleaf radiation leakage, with a period that is the width of a single leaf along the line of integration at angle θ. The signal contrast of interleaf radiation leakage can be defined asThe Radon transform can be considered the sum of all intensity values along the integration path. Thus, when the path of integration is parallel and coincident with the leakage radiation, the signal (sum) will be significant. When the integration path is parallel, but not coincident, with the leakage radiation or when the integration path is perpendicular to the leakage radiation, the signal will be weak. The expression sθmax and sθmin are the maximum and minimum values of the signal gθ(r) at angle θ. Thus, the signal contrast as a function of integration angle θ can be used as a means of determining leaf (and hence collimator) orientation in a portal image such that C(θ) is a maximum when the integration angle θ is parallel to the leaf orientation θleaf in the image. Then, if the collimator angle θcol is well known, the relative rotation of the image chain with respect to the collimator rotation will be Δθ wherewhere Using a single image frame to analyze the interleaf leakage behind a single‐leaf bankUsing a single image frame to analyze the interleaf leakage behind both leaf banksUsing a composite image made from six frames to analyze the interleaf leakage behind both leaf banksWe tested the Radon transform method of determining collimator rotation with respect to the EPID grid for five relative angles . The angle between the collimator rotation and the EPID grid was determined independently using the projected image of a machined grid mounted in the collimator's accessory holder. The grid lines were defined with respect to the solid Y‐jaw of the collimator to better than 0.1 degree. A precision level was used to define the collimator rotation at gantry 90 degrees, accurate to better than 0.01 degree. Images were acquired with 10–monitor unit (MU) exposures at 18 MV. For each case, the interleaf radiation contrast signal was determined as a function of projection angle θ. The method was tested for thee different cases:g0(r) parallel to the MLC leaves produces a high‐contrast signal, with peaks (yipeak) at locations where the interleaf leakage is maximum. This signal will have a period of distance d, the projection width of an individual leaf. Assuming very high manufacturing tolerance, the Y component of beam isocenter yiso should be exactly in the middle of the 15th leaf for a 58‐leaf Siemens MLC. Locations for yipeak are determined byThe Radon transform analysis of the interleaf leakage can be used to accurately determine the Y‐component of the intersection of the beam axis with the portal imager. The algorithm is described in this way: The Radon transform i is the leaf index. A least‐squares fit of the measured yipeak values to Equation yiso.where yiso is predetermined from the interleaf radiation leakage, a 5‐mm‐wide strip centered on each leaf i is segmented from the portal image. For each extracted strip i, the actual measured leaf edges  in the left and right banks are independently determined through local thresholding. The expected edge position xi,je of leaf i in bank j can be determined asThe position of the isocenter in the orthogonal direction can be determined by several methods. The most accurate and simplest is to rotate the collimator by 90 degrees and repeat the preceding method. But rotation is not always practical, particularly in the clinical environment. An alternative method uses local thresholding: Given that anddi,1p and di,2p in two banks are assigned during planning. If the nature of the MLC positioning error is random, then xiso can be determined by minimizing the variance for the selected leaves, wherewhere the leaf positions The advantage of local thresholding is that it eliminates the error that arises when global thresholding is applied to images that have intensity gradients across the field, because global thresholding uses a single threshold value to determine the beam edge . But, because of the intensity gradients, this global value may not be valid locally. The intensity gradients arise from patient attenuation and, in the case of our Siemens TI imager, from spatial variation in imager response.yiso was determined directly from the Radon transform as described earlier; xiso was determined from local thresholding and residual minimization as described earlier. The reference “true” isocenter position was determined under collimator rotation as the centre of the locus of projections of a 0.5‐mm steel sphere embedded in a plastic plate placed in the collimator accessory holder. For comparison purposes, the isocenter position was also calculated by the method of moments and the normalized cross‐correlation method.We tested the ability of our method to determine the exact position of the intersection of the collimator rotation axis with the image plane for 22 different MLC‐shaped fields. All images were exposed to 10 MUs of 18‐MV photon beams. Four images were taken at each of the nominal oblique gantry angles  and six images were taken at gantry 0 degrees. The position of f moments,9 anC(θ), where the angle between the collimator and the EPID grid was known a priori to be 2.0±0.1 degrees. Table 0.12±0.18 degrees, 0.12±0.11 degrees, and 0.08±0.11 degrees by analyzing the interleaf radiation leakage under both banks from a single frame, from double frames, or from a six‐consecutive‐frame average, respectively.Fig. Table In our experience, the mechanical instability of our portal imagers has acted as a barrier to fully exploiting portal images for LINAC quality assurance and for accurate distinction between patient setup errors and MLC leaf and jaw calibration errors. In the present work, we investigated the feasibility of using interleaf radiation leakage to accurately determine collimator rotation. We used the interleaf leakage to accurately estimate the position of the beam isocenter perpendicular to leaf travel in the EPID image and minimization of residuals, aided by local thresholding, to estimate the isocenter in the leaf travel direction. We showed that the Radon transform can successfully extract position and orientation of interleaf leakage from the electronic portal image and that this information can be used to accurately determine the collimator rotation.Electronic portal imagers could be ideal devices for filmless quantity assurance of treatment machines, and particularly for MLC and collimator calibration; but they are not being used for this purpose in most radiotherapy practices. A recent reportThe efficacy of portal imagers as a tool for image‐guided radiotherapy is also limited by their performance—particularly their lack of mechanical stability and reproducibility (orientation and translation) from field to field and fraction to fraction, and their low image contrast and the geometric distortion inherit in some video‐based systems.We demonstrated a method that can overcome the issue of mechanical instability and that can provide an accurate estimate of position . Armed with this information, use of an EPID for routine MLC and jaw quality assurance can be considered. Given a single portal image, information about the location of the collimator axis of rotation makes it possible to distinguish between the geometric errors arising from patient setup, collimator rotation calibration, and the random geometric errors in positioning MLC leaves. We are currently using this approach to analyze EPID images from a large series of prostate patients, accurately quantifying interfraction setup errors, intrafraction patient motion, and treatment‐specific daily errors in MLC calibration.


Happiness in nations is typically measured in surveys using a single question. A common question is: ‘all things considered, how satisfied or dissatisfied are you with your life as-a-whole these days on a scale from 0 to 10?’. The responses typically follow a uni-modal distribution with highest frequencies between 5 and 8. Yet in some nations, the percentage of 10 responses stands out and is higher than the percentage of 9 responses. This is particularly present in Latin America and in the Middle East. In this paper we explore the prevalence of the ‘10-excess’ pattern and check some possible explanations. We conclude that the 10-excess phenomenon is partly due to cultural influence. Happiness became a subject of empirical social science research in the second half of the twentieth century. To date 2015) some 3500 empirical studies have been carried out on happiness, most of which are recorded in the Bibliography of Happiness, part of the World Database of Happiness  characterizes the tendency of some respondents to tick the highest or lowest option. Greenleaf  is surprisingly high in several countries and sometimes, option ‘10’ is ticked more often than option ‘9’. One consequence is that the overall distributions no longer fit the unimodal distribution often observed, in particular in developed nations. An example of such a frequency distribution is presented in Fig. The goal of this paper is to explore the 10 excess phenomenon. More specifically, we want to answer the following questions for the first time: How often does it appear and where? What are the possible reasons behind this response pattern? This is important to know as the 0–10 numerical response scale has become standardized in happiness studies. In that respect, the more we know about the various biases, the more they can be taken into account in future studies. We will first describe in more detail what we mean by ‘happiness’, how it is measured and how we define ‘excess’ Sect. . Next wethedegreetowhichsomeoneevaluatestheoverallqualityofhisorherpresentlife-as-a-wholepositively. In other words, how much one likes the life one lives. Thus defined, happiness is something people have in mind, and happiness in this sense can therefore be measured with questionnaires. As such, happiness is a suitable subject for survey research. The survey question on happiness under consideration in this paper reads as follows: “Taken all together, how satisfied or dissatisfied are you with your life-as-a-whole these days? Please answer by ticking a number between 0 (or 1) and 10, where 0 (or 1) stands for dissatisfied and 10 for satisfied”.In philosophy, the term ‘happiness’ often denotes the good life in a broad sense. In contemporary empirical research, the term is mostly used for subjective satisfaction with life. In this tradition, Veenhoven  defines This kind of question has been replicated in several large scale survey studies, many of which are part of an international survey program, such as the Gallup World Poll, the World Values Survey, the European Social Survey, the Eurobarometer and the Latino Barometro. The observed distribution of responses in each of these studies are gathered have been collated in the collection of ‘Happiness in Nations’ , with at least 10 options, that is, either 1–10 scales, as in Fig. To date 2015) these kinds of questions appear in more than 5000 survey studies in the general public of nations and in 1367 of the surveys the full distribution of responses have been recorded, rather than only the mean and standard deviation. In this paper, we observe the 10 excess responses in the 1367 distributions  have a TON superior to 2 .In this part, we explore possible explanations in order to understand this phenomenon. We first consider technical reasons . We then investigate societal reasons before analyzing possible cultural influences.technical reasons. The phenomenon we observe might occur in particular samples or be caused by subtle differences in survey techniques, such as in the sampling of respondents, the placement of happiness in the questionnaire and the behavior of the interviewer. If so, we can expect that TON differs across surveys in the same country. We checked using the countries where different survey programs had measured happiness on 1–10 or 0–10 numerical scales. To do so, we took the example of Brazil, a country that often presents the 10 excess. We compared different surveys. The results are shown in Table First we will look into Variations can be seen between regions, years, and scales but the 10 excess phenomenon is present in every survey. We also compared the variance in TON within survey programs with variance in TON across survey programs. We compared the LAPOP, What World Thinks and PEW surveys asking the same question on contentment on a 0–10 scale in the years 2000. The LAPOP presents the same survey in the years 2008 and 2010 and their difference are very small, with a variance of 0.05. The variance among the three types of surveys is much more important (0.5), which seems due to the fact that the TON is much higher in the What World Thinks survey (2.37) whereas LAPOP (1.32) and PEW (1.38) are very close.We hypothesized that the 10 excess phenomenon is typical for short response scales and therefore occurs more often on 1–10 scales than on the 0–10. People might be less prone to go to 10 once they have imagined what 0 means versus 1 which is less extreme. This was found to be the case: the 10 excess was rather less present on a 0 to 10 scale (37 %) than on a scale from 1 to 10 (55 %). However, it still represents a high percentage of the distributions in both cases.The 10 excess phenomenon could be more common if the positive end of the rating scale is labeled modestly, using terms such as ‘satisfied’ or ‘happy’, rather than with stronger terms such as ‘completely satisfied’ or ‘extremely happy’. To check this explanation we selected pairs of questions used in the same country in the same period, that differed only in the labeling of the extremes of the numerical response scale. The only match in terms of length of scale, period and measure type are the questionsYet another possibility is that the 10 excess pattern appears more on numerical scales, because the number 10 is open to more interpretations than a word like ‘satisfied’. Ideally this requires a comparison with responses scales with an equal number of verbal response options. Such cases are unfortunately not available; the longest verbal response scales provide only seven options. Therefore we compared means obtained using numerical scales to the mean scores on verbal response scales, which were later transformed to a secondary 0–10 numerical scale. To that end, we selected average values: for numerical scales, we used the average mean score given for an 11-step numeral Life Satisfaction scale (Table 122F) and for verbal scales, the average values given for 4-step scales (Table 111 C). Both sets of data were available in the ‘Happiness in Nations’ collection in the World Database of Happiness.So far we have considered the 10 excess pattern in response to questions on life satisfaction and the Cantril ladder. These two questions are related to a fairly cognitive evaluation of life. Does the same pattern appear in responses to more affective toned questions? We have yet to consider questions that use ‘happiness’ as the keyword, responses to this question are mostly recorded on shorter scales with verbal response options. Data on numerical ratings of ‘happiness’ and ‘mood’ are scarce. Still the European Social Survey includes a question with ‘happinesssocietal factors are responsible. The wealthiest percentile of a nation report to be significantly happier than others and wealth has one of the highest correlations with happiness  to 31.14 .Next, various r et al.  assessedgradingculture. For instance, a studyRatings on the numerical scale of happiness could be influenced by the way school performance is graded in the country. For instance, some educational systems might adopt more extreme grades, others more in the middle for a comparable exam; we call this a The 10 excess pattern observed in responses to questions about happiness can be part of a wider tendency to tick extreme response options. If so, that must manifest it must be manifested in ratings other than happiness, such as in responses to questions about perceived freedom. We checked using the item of perceived freedom in the World Values Survey.artifacts on the 10 excess in the distribution of happiness in nations. The first artifact was to change the frequencies of 9 and 10 responses for the countries presenting a 10 excess. The second artifact was to reduce the 10 scale by combining the responses in the following way: 1–2, 3–4, 5–6, 7–8 and 9–10. The third artifact we applied was the following: we computed the ten over nine (TON) ratio for the 371 distributions on a 1–10 scale that does not present a 10 excess; the average TON was 0.64. Assuming that a unimodal distribution was the norm, we applied this ratio to countries presenting the 10 excess to remove the bias; we then computed a new percentage of 10 respondents by multiplying the number of 9 respondents by 0.64, thus obtaining a modified 10 %, which was lower than in the original data. The sum amounted to lower than 100 %, so we computed a new average with the modified percentage of ten respondents to reach 100 % by multiplying the average by  − (corrected 10)))) so the 10 excess was distributed over all the bars, respectively of their proportional weight.If the 10 excess phenomenon is at least partly due to some cultural effect, scores of average happiness in nations could be inflated in the countries with the 10 excess and lower the correlation with a nation’s characteristics, such as income per head. If that is the case, this provides us with opportunities to check whether a measurement bias is involved and get an idea of the size of this bias. As such we explored the effect of three distinct The question is then whether the means of the corresponding modified distributions in nations correlate better with societal quality than the means of the unmodified distributions. In order to verify this, we examined 97 nations around the year 2000, using the following nation characteristics: buying power per capita, the human development index, government effectiveness and economic freedom. Data was drawn from Veenhoven’s  ‘States When modified averages of happiness was used, the explained variance rose to 71 % in the case of the 9–10 swap, 70 % in the case of the merge and up to 74 % for the TON 0.64 method. So in all cases, there was a gain in explained variance, which is rather substantial in the case of the third method, as the gains of percentage in the high percentage area are hard to achieve. This seems to confirm that some measurement bias is involved in the ‘10 excess’ phenomenon. There could be other possible artifacts, e.g. by squeezing the observed distribution on this 0–10 numerical scale into a reference distribution obtained using a survey question on the same topic, in the same year, with a different response scale . Nonetheless, it seems that some factors are more influential than others. Income inequalities seem rather unsatisfactory in explaining this phenomenon. In this context, the survey techniques, the scaling, the labeling, in brief, all the survey factors seem to play a minor role. Keywords are influential to some extent: in Europe for instance, the phenomenon is less observed on happiness than on life satisfaction. A few hints tell us the grading culture might have an influence, but the grading culture might in turn be a part of a wider response pattern. The large correlations between the 10-excess rate on various subjective questions such as happiness, life satisfaction and perceived freedom seem to indicate that a wider cultural frame is involved, with systematic presence in Latin America and in the Middle East. These findings are in line with other studies (Culpepper and Zimmerman excess or not is of major importance. In this article, we have chosen as a starting point the unimodal distribution mostly observed in developed nations, among others. The excess in the sense used in this article is a deviation vis-à-vis this distribution we chose. This raises a tricky question of whether we should correct it or not. As Stening and Everett (corrections but artifacts, used in order to see what that would imply for commonly used correlations. If we acknowledge the answer is ‘no’, which is the most convenient case, it means that we are leaving aside part of the differences in distribution and that we should at least acknowledge it in our studies. If we acknowledge the answer is ‘yes’, it means we need to choose a norm and correct the samples vis-à-vis that norm. In that case, which norm is the right one? The unimodal distribution of developed nations, the V shaped distribution of Jordan or a completely different distribution? Choosing one norm is convenient but might be rather challenging as there are in the field of cross-national studies rather large clusters that are largely independent (Brulé and Veenhoven The question of whether it is an  Everett  stated fThis study faced several limitations and therefore there are options for future improvement. We think we have highlighted some reasons behind the 10 excess. However, the quantitative contributions to the phenomenon should be determined in future studies. To work further on this issue, more thorough statistical work is required. Moreover, our comparison of grading cultures was limited to only three countries. The grading culture seems to be implicated, but we do not know to what extent and the causality, although it is likely to be a part of a larger cultural response pattern. More systematic data in the field of cross-national comparison of grading would be very helpful for researchers dealing with response patterns. The keyword seems to play a role in Europe, but it would be very interesting to have comparable surveys for all countries, particularly in Latin America and the Middle East. The impact of social desirability has been understudied. The sparse data we have seems to indicate that this might play a role but its actual contribution is opaque as for now.