Facial ancestry can be described as variation that exists in facial features that are shared amongst members of a population due to environmental and genetic effects. Even within Europe, faces vary among subregions and may lead to confounding in genetic association studies if unaccounted for. Genetic studies use genetic principal components (PCs) to describe facial ancestry to circumvent this issue. Yet the phenotypic effect of these genetic PCs on the face has yet to be described, and phenotype-based alternatives compared. In anthropological studies, consensus faces are utilized as they depict a phenotypic, not genetic, ancestry effect. In this study, we explored the effects of regional differences on facial ancestry in 744 Europeans using genetic and anthropological approaches. Both showed similar ancestry effects between subgroups, localized mainly to the forehead, nose, and chin. Consensus faces explained the variation seen in only the first three genetic PCs, differing more in magnitude than shape change. Here we show only minor differences between the two methods and discuss a combined approach as a possible alternative for facial scan correction that is less cohort dependent, more replicable, non-linear, and can be made open access for use across research groups, enhancing future studies in this field. These differences are predominantly evident between continents, however intra-continental variability is also quite distinct. Europe contains genetically divergent populations in close geographic regions, which has been linked to migration patterns and serial founder effects4. Specifically, Southern Europe has been described as being more genetically diverse than other European regions, due to influences from both the Middle East and Africa5. On the other hand, Northern Europe has less diversification as these individuals stemmed from a smaller population of founders4. These genetic differences, alongside climate-related adaptation and selection, are reflected in the shape of the modern human face7. Previous publications have analyzed facial differences between individuals from a handful of European countries and discovered variation predominantly visible in the nose, brow, and lips area10, but a systematic comparison across broad geographic regions within Europe is missing.Although ancestry as a descriptor continues to be a controversial topic in anthropology when describing morphological features, geographic variation is often reflected in the structure of the human face and is an important factor in facial variation studies. Historically separated populations are interbreeding more than ever before, yet geographical distance and barriers still constrain random mating and lead to genomic and phenotypic diversity11. To avoid spurious associations due to ancestry signals, facial GWAS studies frequently reduce cohorts to specific populations (most often Europeans) and correct both genomic and phenotypic data for genetic ancestry. Typically, these studies utilize the first four genetic ancestry principal components (PCs) to correct for European genetic ancestry. However, there is little data on precisely how these genetic PCs are describing and correcting for ancestry within the face. Furthermore, this methodology is highly debated in current literature, with Principal Component Analysis (PCA) being criticized to be highly sensitive to outliers when the data is normalized, which often leads to misalignment such as shrinkage (when new data gravitates to the middle)13. In addition, using genetics to describe facial ancestry  assumes that phenotypic variation can be adequately accounted for by genetic variation alone. This, however, does not take into account that facial variation may be larger than the genetic variation due to phenomena such as epistasis and other post-translational factors14. Only if these modifications correlate to regional genetic variation will they be incorporated, while significant phenotypic variation correlated with nonsignificant genetic PCs or no genetic correlation will be missed.Over the last decade, more than 25 genome-wide association studies (GWAS) have been performed that have mapped genes for facial traits in numerous populations15. While genetic studies prefer to use genetic methods for describing ancestry, anthropological studies rely more heavily on phenotypic variation. In these studies, input data is often separated into ancestral groups and analyzed within these groups, and population differences are described by comparing the geographic mean faces, also known as consensus faces9. These consensus faces reflect the facial features common to a population due to their shared genetic and environmental influences, as well as selective pressures, and as such, their shared ancestry14. As they are not derived from genetic components, these faces describe phenotypic differences that may also account for gene–gene interactions and post-translational modifications. These generalized ancestry depictions focus more on broader ancestry signals, whereas genetic PCs describe an individual’s ancestral position within a space specific to the reference populations used. Although in anthropology the groups are often generalized geographic groups, there remains the possibility to combine both the genetic and anthropological approach; by defining sub-populations based on genetics but describing ancestry within these populations using consensus faces.Yet ancestry must be accounted for, as even within Europe population stratification is evident, showing clear genetic and phenotypic ancestry signals within the continental populationIn this report we explore facial ancestry within Europe inferred from both genetic facial ancestry  and phenotypic facial ancestry (consensus faces), using geometric morphometrics of more than 7000 landmarks. A comparison of the two methods illustrates that genetics do not always match phenotypic separation, yet overall, the differences are minor. We discuss the benefits and disadvantages of both approaches including the possibility of combining both as an alternative method for understanding and correcting for ancestry in future genetic studies.A systematic overview of the methods described below can be found in Fig. 16. These samples were compiled from three independent datasets: Indiana University-Purdue University Indianapolis (IUPUI),Indianapolis, IN, Pennsylvania State University (PSU), University Park, PA, and the 3D Facial Norms cohort (3DFN)17. Each study site obtained approval from an institutional review board. 3DFN dataset: Pittsburgh, PA (University of Pittsburg Human Research Protection Office IRB00000319|PRO09060553 and RB0405013); Seattle Children’s Hospital, Seattle, WA  IRB00000277 and IRB00009311|IRB 12107); University of Texas, Houston, TX ; and University of Iowa, Iowa City, IA (University of Iowa Human Subjects Office IRB00000100|IRB 200912764 and 200710721). Pennsylvania State University dataset  collected at various locations: Urbana-Champaign, IL; New York, NY; Cincinnati, OH; Twinsburg, OH; State College, PA; Austin, TX; and San Antonio, TX, and University of Cincinnati, Cincinnati, OH (University of Cincinnati—Human Research protection Program IRB00000180|IRB 2015-3073);. IUPUI dataset  IRB00000222|IRB-IUB 1409306349): collected in Indianapolis, IN and Twinsburg, OH. The participants of each study provided written informed consent before participation. Individual’s identity was protected by storing data on secured servers and by assigning anonymous identification numbers. Raw data could only be accessed by those with previous ethical approval. All experiments were performed in accordance with relevant IRB guidelines and regulations. Our subset (n = 3814) for analysis excluded any samples that were missing genetic or image data, under the age of 18, or known to have ancestral backgrounds with historical deviations from assortative mating.The samples used for analysis were from the United States dataset of White et al.16. We compiled our subset of non-imputed participant data based on the existing overlap of autosomal single nucleotide polymorphisms (SNPs) . We combined a reference data panel (described below) with our participant data, and this further reduced the total number of shared markers . Complete genetic data overlap of our participant and reference data promotes consistent ancestry estimation parameters for downstream supervised ADMIXTURE analysis.Information on the genotypic data used by the independent participant sample datasets can be found in the supplementary methods ofhttps://unstats.un.org/unsd/methodology/m49/). These samples were selected from the Human Genome Diversity Project18, 1000 Genomes Project20, Balto-Slavic speaking populations21, George Busby’s genotype data for a set of 163 worldwide populations22, a genome-wide study of the Jewish population23, Genetics from Turkish-speaking Nomads24, Siberian Genome25, the Genetic Atlas of Human Admixture2, individuals from the Caucasus26, and the Euro180 cohort from27. A detailed description of the samples used can be found in A European reference dataset (n = 1166) was compiled of samples with varying geographical origins within Europe to represent the North (n = 414), South (n = 438), East (n = 234), and West (n = 80) cartesian regions according to definitions of the four regions by the United Nations (UN) geoscheme  with genotypes from the shared markers  using PLINK2 '--pca' flag and visualized using the assigned regional labels . An anthropometric mask of 7160 spatially dense quasi-landmarks was aligned and morphed to the shape of each facial scan. An initial, scaled, non-rigid alignment was performed using 5 manually placed landmarks , a second, non-rigid, iterative registration, where neighboring points move together based on symmetrical correspondence and weighted k-neighbors, was performed to morph the mask onto each individual’s face. These images were symmetrized by averaging the original and its reflection (inverted X coordinate) and aligned in space via Procrustes superimposition with scaling, as only shape was relevant for this study (centroid sizes showed no significant difference between groups (ANOVA p = 0.5)).The 3D images were registered using the MeshMonk registration frameworkThe 744 symmetric facial scans were corrected for covariates by regressing the 744 individuals × 7160 landmarks matrix of Procrustes-aligned and scaled landmark coordinates onto sex, age, age-squared , height, weight, facial (centroid) size, and camera system (2 systems coded as -1 and 1) using Partial Least Squares Regression (PLSR). The corrected faces were then generated by adding the coordinates of the mean shapes back onto the residuals of the regression. Genetic PCs were not corrected at this stage.2 value) were calculated. This test was run with varying input groups: n = 744 , n = 186 (per sub-region), or n = 372 (between two sub-regions) to determine which geographical gradients each PC described.Permutation tests based on residuals from the PLSR, with 10,000 permutations of the model were performed to determine if correction for ancestry for the first four genetic PCs via PLSR significantly altered the shape of the facial scans and thus described a significant amount of the facial variation present. For computational efficiency, shape variation was reduced by PCA of the landmark coordinates and only those explaining up to 96% of variation were retained . The scores on each PC were normalized to have unit variance. Genetic PCs were tested individually and in combination. Both the p-value and percentage of variance explained  and added onto an average of all 744 faces corrected for all covariates: including the first 20 genetic PCs. The difference in the resulting faces between ancestry correction via PLSR using all four PCs and using only three PC (one excluded) was calculated to represent the effect of the excluded PC and exaggerated eight times for visualization purposes only. For regional PC faces, the difference between ancestry-corrected faces (first four PCs) and the faces before ancestry correction was calculated, averaged per region (n = 186), and added onto the average face corrected for all covariates. Effects were exaggerated four times for visualization.The maximum ancestry shift was determined by calculating the absolute distance between the pre- and post-ancestry correction facial scans per landmark per individual. The maximum distance over all individuals per landmark was used. Facial ancestry effects were visualized as normal displacement maps (magnitude of shape change), and angle differences (direction of shape change).Consensus faces were created by averaging the 186 faces per region, after correction for all covariates but excluding ancestry, and subsequent Procrustes alignment. Each of the 744 individuals was then corrected for ancestry by subtracting their region’s consensus face from their individual facial scan. The resulting residual was added to the average face from the initial PLSR  to provide us with each individual’s consensus corrected face. A permutation test for the ancestry PCs was performed after consensus face correction to analyze if the genetic PCs still described a significant amount of variation after consensus correction.Sample size distribution for the target data (n = 3814) showed greater representation above the regional median from the North (n = 830) and West (n = 660) regions and the least representation from the East (n = 186) and South n = 230) regions. Within the regional subsets (n = 186), male and female distribution was equally balanced for the North and West regions , while the East  and South  regions were limited in the number of male individuals available above the regional median. Genetic PC1 separated out the South population while PC2 created the gradient East-South-Northwest. PC3 differentiated North from West while PC4 explained some additional variation . It was also significant in the East  and showed the highest pairwise significance in the East–West gradient. Its high significance in mostly Eastern comparisons may indicate some Eastern-specific variation that does not exist between regions. No other PCs showed overall significance, but some  showed significance in one or more between-region comparisons and these remain significant even after ancestry is removed see  online. When combining the effects of the first four genetic PCs, the nose demonstrated the greatest maximum ancestry shift alongside the chin. The smallest shift was seen in the cheeks and nasolabial area Fig. a. By comWhen regions were compared to each other, the East and South show similar shape changes in the nose tip and upper lip with the South having a stronger effect. Opposite effects are visible in the width of the face and shape of the eyes. A flatter mid-face is seen in both the North and West. The South to North gradient is defined by a slimming of the face and protrusion with a downward turn of the nose and mid-face. Differential effects were also visible along the West to East gradient in the cheeks, chin, and forehead. Comparison of the West to South showed the largest difference with opposing effects in the midface, jaw, and outer eye area. North to East showed very little shape differences but larger magnitude differences Fig. .Figure 5A direct comparison of the derived consensus faces to the regional faces created from the genetic PCs showed only minor angle (shape direction) differences, most visible in the Eastern nose. The eyes, cheeks, and upper chin showed the least magnitude  differences in all but the East. The consensus faces showed wider cheeks and forehead in the North, with a larger jaw, and displayed the largest magnitude changes over all regions. For the West, a wider jaw, taller forehead, and increased protrusion of the nose tip and upper lip were visible in the consensus face in comparison to the genetic PC face. The least difference in magnitude was visible in the South with a wider jaw, longer chin, and protruding lower lip in the consensus face Fig. . VisuallPermutation testing using genetic PCs after consensus correction showed that genetic PCs 1 and 2 were no longer significant. PC3 showed an increase in p-value from 0.2 to 0.99, indicating it was defined within the consensus face and does describe facial ancestry . PC4 and PCs 5-20 remained unchanged, suggesting these PCs do not describe phenotypic ancestry between regions within this cohort see  online.Although facial ancestry has been explored previously within Europe, it has frequently been limited to only a few populations and has less dense landmarking techniques. In addition, there is a paucity of research to link the genetic PCs (which are used to correct ancestry) to facial ancestry, as well as discussions on the accuracy of using genetic scores for phenotypic correction. Here we have attempted to address these aspects by describing facial ancestry variation within Europe using two different approaches: genetic and anthropological, and visualizing the differences between the two.3, and research has shown that genetic PCs within Europe separate out the four Cartesian regions: PC1 often describes the North–South gradient, while PC2 describes East–West differences33 which correspond with our findings. Our results show that genetic PC1 defined the differences between the South and Northwest, while PC2 separated East from Northwest within our dataset. Only these two PCs significantly affected the face, as such there was no separation between the North and West phenotypically, which coincides with ideas put forward on the settlement of these regions. We also replicate previous findings on higher ancestry signals in the face within the South and East due to known migratory routes to Europe through the East and South which extended to the North and West via the serial founder effect4. As such, the genetic variation observed in the Northwest was much smaller as it stemmed from fewer founder individuals, in addition to gene flow between these regions which may in part account for similarities seen within our data4. Furthermore, these regions migrated back to the Southeast during the last glacial maximum, reintegrating their genetics into the Southeast and increasing variation there. Post-glacial recolonization included even fewer individuals, reducing genetic variation in the North once again34. Eastern Europe is closest to Africa and Asia, thus, migration from these regions brought new genetics to Eastern Europe first, increasing variation in a gradient from East to West and South to North4. In addition, specific phenotypes are highly affected by ancestry due to climate adaptation6. For example, our data show a larger, more downturned nose in the South, with a smaller more upturned nose in the North. Noses in warmer regions display a larger, wider, and more downturned nose, allowing the humid air to be cooled; in colder regions, a narrow high nose allows the air to be moistened and warmed36. Moreover, variations in chin shape found within our data set may also suggest a link to migration patterns of hunters/gatherers and differences in diet37. Furthermore, we replicated European facial data; when considering an Italian (South), Lithuanian (North), and German (West) population, previous research has shown that the North and West had a rounded nose tip, while the South showed a straighter nose tip, high eyebrows and slightly upturned mouth corners10.One main point is evident in this research: Europe is not a homogenous population and shows ancestral facial variation. Genetic ancestry has previously been shown to mirror geography38. Overall, both approaches showed similar facial ancestry shape changes, with slight differences in the magnitude of the shape change.Although these facial differences between geographic regions have been linked to N–S and E–W gradients described by the first 4 genetic PCs, a slight discourse was visible between the genetic data and its phenotypic expression within our dataset. Both genetic plots and facial ancestry describe the same gradient for genetic PC2, whereas for PC1, East showed an opposing position to South genetically, but an intermediate phenotype within the face indicating that phenotypic variation may not be entirely represented by genetic differences. PC18 describes a significant amount of facial variation, but not genetic variation. The facial variation described by PC18 is thus missed with only a genetic depiction of ancestry and may be linked to post-translational modifications or gene–gene interactions not accounted for or correlated to significant genetic PCs. However, it was not influenced by the consensus faces and thus may describe intra-region variation and not variation between our geographic regions. Yet overall, consensus faces and genetic PC faces showed similar phenotypes. Facial variation described by genetic PC1 and PC2 was completely accounted for by the consensus faces and did not describe any further variation after the faces were adjusted for the consensus faces. Although genetic PC3 was not significantly associated with facial shape in our smaller cohort, when testing a larger population of European individuals (n = 3814), it did pass the significance threshold (data not shown). Importantly, facial ancestry variation from PC3 was completely described by the consensus faces, indicating that it does seem to describe facial ancestry. On the other hand, genetic PC4 described no significant amount of facial variation. Neither PC4 nor PCs 5-20 facial variation was explained by the consensus faces suggesting that these PCs describe variation that may not be linked to regional ancestry, though possibly ancestry on a subpopulation level. It is important to note, however, that correcting for insignificant PCs does not significantly change the face shape and correcting for insignificant covariates has been shown to not negatively affect results, although it does reduce the degrees of freedom12. Genetic ancestry in itself is arguably a non-linear construct that we have long forced into a PCA space, which has seen a surgency of alternative methods to better facilitate its reduction41. Typical anthropological practices of describing ancestral variation using consensus faces often rely on available self-reported ancestry, although recent work is moving towards the inclusion of genetic ancestry42. However, in our modified consensus face approach, this is overcome by using genetics as a prior through an unbiased classification of the region in which an individual belongs via ADMIXTURE. The benefit over PCA is that it is based on contributions from each of the source populations (k) and less so on their individualized placement in an ancestry space. Although choice of source population groups certainly influences categorization of individuals, it seems a more conservative approach overall.Although both genetic PC facial correction and phenotypic consensus face correction describe comparable facial ancestry signals, each has its benefits and drawbacks. For ancestry analyses, ancestral populations must first be determined and described. The main benefit of PCs are that they reduce the dimensionality of genetic data down to only a few summary variables which is easily visualized on plots that seem to separate ancestral populations. Individuals are projected onto a space along these PC axes, and distances between clusters are assumed to reflect the genetic, geographic, and phenotypic distances between populations. Yet statistically these only describe the greatest variation which may or may not be entirely linked to ancestryOnce genetic ancestry or ancestral groups are described, their relationship to the phenotype must be modeled. Linear regressions further limit the amount of shape changes that can be described in the face and are not efficient at considering covariate interactions when given multiple variables for ancestry. Nonlinear regressions and mixed effect models are a solution but can quickly become highly complex. Although consensus faces model a nonlinear relationship between ancestry and phenotype, they require a sufficient number of 3D facial scans to adequately generate the consensus face of the region, which can be a difficult task as one must ensure that all clusters within the research cohort are adequately represented for phenotypic ancestry correction. Yet the main advantage is that consensus faces describe the entirety of phenotypic variation and are not reduced to a few linearized genetic gradients between populations.A distinct advantage of the consensus correction approach relates to reproducibility and comparability between research studies. With PC scores and linear regressions, these can vary depending on which individuals are included, leading to differing results for a phenotype that should not vary if new individuals are added as facial ancestry is fixed. While standardizing the choice of reference populations may help ensure more comparable PC scores, the regression models will still fluctuate. On the other hand, once a consensus face is created to represent a particular region, it can be shared across research groups without ethical implications as it does not describe an individual. As such, the consensus face bypasses issues with cohort-specific correction effects and varying methodologies for covariate correction and remains a less complex, and less error-prone, method of describing facial ancestry variation.43. Consequently, further refinement is needed with smaller subpopulations analyzed to discover which ones need individual consensus faces.In exploring these two ancestry facial correction approaches, it is important to also describe the limitations of this study. Although we attempted to select individuals with an equal genetic variation spread, our East and South populations did show higher PC score variation than West and North. The West cluster was more compact, while a lack of East samples and a wider spread led to more diverse individuals which may have influenced the results. The lack of individuals in some regions limited our cohort to only 744. In addition, our data included only individuals that clearly clustered (K-means) to a specific region, but not every sub-population within these regions was represented. These clusters were assigned to regions using the UN geoscheme and this meant it was predetermined which countries were assigned to each region. This leaves unanswered questions as to (1) how to correct for admixed individuals, and (2) if and what are the sub-population specific effects. Our results show that PC1 remains significant within the Southern population even after consensus correction and PC18 is significant within the East, indicating that individuals vary within in their facial phenotypes within these regions, which may also be attributed to sub-population ancestry variation. Previous publications have shown that genetic ancestry can vary even within specific sub-regions44. Especially SNPs that are fixed in global populations will not be discovered unless admixed individuals are considered45. From an anthropological approach, this leads to the issue that many individuals self-identify as a specific population but in reality, show varying amounts of admixture46. For a genetic approach, PCs are not adequate to describe admixture as it is a general model that does not consider the basics of population genetics but instead places an individual in space between two populations with the assumption that the distance to each population cluster describes its admixture proportions48. Nevertheless, other genetic methods such as Tractor39 have been described to facilitate local ancestry and admixture correction, and programs such as STRUCTURE and ADMIXTURE show positive results for predicting admixture proportions48. Potential points of discussion and future avenues of research have shown that admixed individuals usually show an intermediate phenotype between their ancestral phenotypes50, which may allow us to include admixed individuals for consensus face correction.While some associations found in European facial GWAS have been replicated in other populations, many have not41.Although the goal of facial GWAS is to discover genes that affect individual facial variation (not ancestry), understanding how, where, and in what pattern ancestry affects the face is important not only for correction, but for exploring evolutionary events associated with phenotypic selection. It also allows us to consider the potential for improving future individualized facial predictions that require the reintegration of covariates. There have only been approximately 300 genes linked so far to human facial variation, and associated variants in/around these genes explain only a fraction of the heritability. One limiting factor is the lack of power to find more variants with low penetrance. This can stem from the relatively small sample sizes (by modern GWAS standards) available for these types of studies, or the inability to meta-analyze results for larger sample sets due to incompatible methods and the inability to share datasets. Or more recently discussed, lack of power due to simplified linear adjustment of non-linear covariates51. Thus, if we can create a database of consensus faces, we can increase GWAS power with larger cohorts, improved ancestry correction, and including the consideration of admixture, make the first step towards a standardized method for meta-analyses. This would also contribute to anthropology, allowing a more comprehensive characterization of facial diversity using matched genotype and phenotype sample in large cohorts across the world. Furthermore, population genetic studies would benefit from studies describing phenotypic change across regions.It may be worthwhile to consider a combined approach to avoid many of the issues described above. By using ADMIXTURE to measure the proportion of genetic likelihood of belonging to a particular reference population to determine an individual’s ancestry, we do not need to rely on self-reported data. If we then apply a weighted (dependent on admixture proportions) consensus face correction, we avoid methodological issues related to PCA as well as replicability and comparability issues. This also avoids limiting cohort size by allowing the use of global populations that include admixed individuals. Although it may be difficult to collect faces for every population, previous research has shown that relatively robust facial averages can be created from approximately 30 individualsThis study shows that ancestry cannot be ignored even within more homogenous populations. Europe demonstrates clear ancestral differences in the face, particularly within the East and South, that are similar between genetic facial ancestry and consensus faces. Thus, using consensus faces instead of genetic PCs may be a viable alternative. We propose combining both methods and describe a mixed approach to ancestry correction, using both genetic and anthropological components. By using genetic methods to define an individual’s ancestry but describe facial ancestry using consensus faces, we bypass most of the above-stated concerns. Consensus faces defined by genetic ancestry proportions are more replicable and easier to apply, simplifying both analyses and comparability of multiple studies, and may, in the future, provide more robust global GWAS and meta-analyses in craniofacial variation studies.Supplementary Information 1.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.


Echinococcus multilocularis (Em), the causative agent of human alveolar echinococcosis (AE), is present in the Holarctic region, and several genetic variants deem to have differential infectivity and pathogenicity. An unprecedented outbreak of human AE cases in Western Canada infected with a European-like strain circulating in wild hosts warranted assessment of whether this strain was derived from a recent invasion or was endemic but undetected. Using nuclear and mitochondrial markers, we investigated the genetic diversity of Em in wild coyotes and red foxes from Western Canada, compared the genetic variants identified to global isolates and assessed their spatial distribution to infer possible invasion dynamics. Genetic variants from Western Canada were closely related to the original European clade, with lesser genetic diversity than that expected for a long-established strain and spatial genetic discontinuities within the study area, supporting the hypothesis of a relatively recent invasion with various founder events. In small mammals, the parasite invades and develops in internal organs (primarily the liver), being highly pathogenic. Therefore, the host either dies from the infection or is easily preyed on by the definitive hosts [In the current Anthropocene epoch, biological invasions, due to trans- and inter-continental movements, are generating a global biotic homogenization, influencing at the same time global patterns of disease ,2. Such orldwide ,6. This ve hosts . Humans ve hosts .E. multilocularis is limited to the Northern hemisphere, its distribution and prevalence in wild and domestic hosts are increasing across its range, likely due to human activities [E. multilocularis (or particular genetic variants) into new areas [Although the geographical distribution of tivities –11. The tivities –15. Furttivities ,17. Addiew areas ,18,19.cox1, nad2 and cob), Em genetic variants have been grouped into four clades, mostly linked with their geographical origins: Asian, European, North American and one restricted to Mongolia [E. multilocularis in Western Canada, to assess the extent of the spread of European strains, their distribution in the main definitive hosts (coyotes and red foxes) and to elucidate potential sources of invasion of these strains.Using concatenated sequences of three mitochondrial genes  markers, yet these markers have relatively low variability. Conversely, the microsatellite EmsB, a multi-locus nuclear DNA marker, has greater discriminatory power than classic mtDNA markers, and has been used to identify spatio-temporal characteristics of E. multilocularis transmission in several European countries, at continental, national and local geographical scales [In Canada, genetic characterization of l scales –34. Addil scales .E. multilocularis in Western Canada, based on EmsB profiles and mtDNA haplotypes; (ii) investigate genetic relationships with E. multilocularis isolates from other geographical regions across the globe, to identify possible sources of invasion; and (iii) evaluate the spatial distribution of the detected genetic variants to elucidate the spread dynamics of the European-like strains, possible spatio-temporal scenarios of its invasion process, and the likely origins of these European strains in Western Canada.With this study, we aimed to: (i) assess the genetic diversity of . 2 (a)Echinococcus multilocularis specimens were collected from gastrointestinal (GI) tracts of red foxes and coyotes of either road-killed or trap-harvested animals (trapped for purposes independent of this study), collected between 2012 and 2017 in Western Canada. Trapped animals were obtained from licensed trappers with the collaboration of the Alberta Trappers Association. GI tracts were screened using a modification of the scraping, filtration and counting technique, to identify and collect Echinococcus spp. worms [Em worms from 70 coyotes and 13 foxes from northern, central and southern Alberta (AB); four coyotes from north-west British Columbia (BC); and 10 coyotes from southeast Saskatchewan (SK). Extraction of DNA was performed on up to five individual worms per host using the Nucleospin 96 Tissue Kit  for samples processed in France (Anses Nancy Laboratory for Rabies and Wildlife) and the E.Z.N.A. MicroElute Genomic DNA Kit  for samples processed in Canada . Extraction was performed following the manufacturer's instructions, and DNA was stored at −20°C until processed.p. worms ,36. We a (b)E. multilocularis was characterized by sequencing the genes nad2 (1068 bp), cob (882 bp) and cox1 (1608 bp). We used one to five worms per host, depending on worm availability and quality (intactness), using primers and PCR parameters as previously described [Em worms from our previous studies were also included [https://blast.ncbi.nlm.nih.gov). To analyse genetic relationships and possible origins of haplotypes identified in Western Canada, we constructed a haplotype network, based on Hamming distance, including reported European, Asian, Mongolian and North American haplotypes  [pegas 0.14 [cob, nad2 and cox1, and using Echinococcus granulosus sensu stricto (G1) as the outgroup. Reversible-jump Markov chain Monte Carlo analyses were run for 1 million generations, producing 10 000 trees, with the first 2,500 regarded as burn-in. The tree was plotted using the R-package phangorn 2.3.1 [Genetic diversity of escribed . Sequencincluded ,26. The nd cox1) ,24,25,29gas 0.14  were usegas 0.14 , considern 2.3.1 . (c)https://ewet-db.univ-fcomte.fr/) [A fluorescent PCR assay was used to amplify the EmsB microsatellite, following a previous protocol . Fragmenmte.fr/) . Briefly (i)p-values (au) [E. granulosus sensu stricto (G1) as an outgroup. A distance threshold of 0.08 (average genetic distance observed after three generations in the rodent Meriones unguiculatus) was used to identify unique EmsB profiles; any branching below this threshold was considered genetically identical [Genotyping and hierarchical cluster analysis were performed by calculating the Euclidean distance between profiles and using the average link clustering method UPGMA . The uncues (au) . A dendrdentical . Unique dentical . To undedentical .E. multilocularis isolates from Europe , Asia  and North America , each unique EmsB profile identified was compared to a reference world collection of profiles from the EWET database  [pvclust [To evaluate genetic relationships with genotyped il 2017)  and fromil 2017) ,44. Prof[pvclust  and NMDS (ii)We evaluated the diversity of EmsB profiles in the province of Alberta (AB), the most extensively sampled area in our study. We divided this area into six geographical subregions: North-West (AB–NW), North-East (AB–NE), Central-West (AB–CW), Central-East (AB–CE), South-West (AB–SW) and South-East (AB–SE).et al. [q ≤ 2, where q is a number that determines the sensitivity of the measures to ‘species’ (i.e. EmsB profiles) abundances. Second, asymptotic diversity estimates were calculated using a sample size-based rarefaction and extrapolation sampling method [q ≥ 0 included the three most widely used diversity measures . Third, if data did not contain sufficient information to accurately infer the true diversity, this was inferred for a standardized sample coverage [q = 0, 1 and 2. The standardized sample coverage  was selected as the minimum among the coverage values for samples extrapolated to double the size of the reference sample. Lastly, the evenness (i.e. distribution of EmsB profiles abundances) was calculated for each subregion. A bootstrap method (n = 100) was applied to obtain the associated 95% confidence intervals for all estimates.We used an integrated approach based on the framework of Hill numbers to assess: (i) sample completeness, (ii) asymptotic diversity estimates to infer true diversities, (iii) non-asymptotic standardization via rarefaction and extrapolation and (iv) evenness of each subregion (except for AB–NW due to low sample size), following the methodology described by Chao et al. . First, g method . The samcoverage . Here, wβ-diversity was performed based on the Bray–Curtis dissimilarity index and complete linkage agglomerative clustering for hierarchical agglomeration [Cmax). Analyses were performed using the R package iNEXT- 4steps [vegan [Analysis of meration . Diversimeration ,50. Dive- 4steps  and vegas [vegan . (iii)vegan [Alberta is divided into 174 wildlife management units (WMUs). Samples were collected from 38 WMUs and the location of each host was assigned as a random point within a radius of 15 km (minimum radius of a WMU) from the centroid point of the WMU where it was collected. This methodology was used because the exact geo-reference  for all the hosts was not available. A Mantel test was used to assess the hypothesis of genetic isolation by geographical distance, comparing a matrix of the genetic distance between EmsB profiles, and a matrix of the geographical distances between samples based on Euclidean pairwise distances . A Mantevegan  and Prim. 3 (a)E. multilocularis based on mtDNA, we analysed 96 concatenated sequences of cox1, nad2 and cob genes in adult worms recovered from 13 foxes and 77 coyotes. Virtually all (93/96) were identified as the previously described European-like haplotypes ECA, EAB, ESK, ESK2 and BC1 [a), indicating a possible stem from a single invasion event. The phylogenetic tree confirmed the close relationship between the European-like haplotypes in Western Canada and the original European clade. Within this clade, the three similar haplotypes  were in the same branch with the E4, whereas BC1 was in another , and more deeply branched, indicating a distinct most recent common ancestor. These branching nodes were supported by the Bayesian posterior probabilities (b).To characterize the genetic diversity of  and BC1 ,26, and bilities b.Figure (b)doi:10.6084/m9.figshare.16818607). Comparing the similarity of these 204 profiles, we identified 16 unique EmsB profiles based on the 0.08 threshold average genetic distance. These profiles formed three significant clusters: the first with samples from AB only, the second with samples from AB and SK, and the third with samples from AB and BC (a).Through fragment analysis of the multi-locus EmsB microsatellite, 204 individual EmsB profiles were computed and technically validated   were the most frequent, each representing 32.8% of the worms. When comparing the two definitive hosts, profile P13 was present in 69% of foxes and 64% of coyotes, and was the most prevalent in both hosts, representing 56% and 45% of worms in foxes and coyotes, respectively. Profile P4, the third most common (11.8%), was present in AB and SK. Profile P11 was only in BC and AB, corresponding to 5.9% of the worms. The other profiles represented 1–3.4% of worms and were primarily from AB .E. multilocularis isolates, 13 of our 16 unique profiles (81.2%) clustered with isolates from Europe, with eight being genetically indistinguishable (based on the 0.08 threshold) from profiles present in several European countries , and only three profiles clustering with samples from the Arctic (i.e. Saint Lawrence Island (Alaska) and Svalbard archipelago (Norway); figure 2b; electronic supplementary material, table S2).When comparing our profiles with profiles from the EWET world database  to evaluate genetic relationships with other a) and the mtDNA haplotypes (b).When the NMDS ordination analysis was performed, the representation of the genetic distances between profiles in two dimensions had the lowest stress , with the three clusters identified being linked with the location (i.e. provinces) a and theplotypes b.Figure (c)q ≥ 0), implying that there was undetected diversity (mostly less abundant EmsB profiles) in all subregions (except for AB–CW), and nearly all abundant EmsB profiles had been detected. The areas with the lowest sample coverage (q = 1) were AB–NE and AB–CE (89%) and the area with the highest coverage was AB–CW (100%). When Alberta was analysed as a single area, the sample completeness profile was 100% for all diversity orders .The sample completeness profiles increased with diversity order , implying that the asymptotic diversity estimates could be used to infer true diversities (Shannon and Simpson), but not to estimate true genetic richness. When comparing the asymptotic estimates for q = 1 and 2 , the highest diversity indices were found in AB–SW  and the lowest in AB–CW  and AB–SE , with similar values for the other subregions.The size-based rarefaction and extrapolation analysis revealed that for each subarea the sampling curve stabilized for orders Cmax = 94.5% . For this Cmax, the corresponding highest richness estimate (q = 0) was found in AB–CE (8.2) and the lowest in AB–CW (2.7). For evenness profiles, all values of q showed the highest evenness of EmsB profiles in AB–CW and the lowest in AB–SE.Since data were insufficient to infer the true richness, non-asymptotic coverage-based rarefaction and extrapolation curves were calculated  and measβ-diversity was performed by constructing a dendrogram based on the Bray–Curtis distance to compare genetic diversity between areas. There was a higher similarity between the central and northern regions, and a second branch grouping for the southern regions , with low dissimilarity among all regions (0.27 to 0.53). When comparing non-asymptotic diversity indices from subregions in Alberta with diversity reported in historical endemic areas from Europe , the range of values (min. and max.) for all q orders was higher in European countries than in most subregions in Alberta. Moreover, when Alberta was analysed as a single area, estimates for orders q = 1 and q = 2 were lower than in all areas in Europe . The same pattern was observed for the evenness profiles, with Alberta being the most uneven area (0.2–0.3), which indicates the presence of a dominant EmsB profile and low diversity.Analysis of  (d)vice versa. To assess the hypothesis of genetic isolation by geographical distance, we used a Mantel test comparing a genetic distance matrix between EmsB profiles and a matrix of the geographical distances between samples, based on Euclidean pairwise distances [r= 0.02, p = 0.30). However, the Mantel test yielded a significant correlation  when including samples from BC and SK in the analysis. The Mantel correlogram indicated a significant positive correlation (more similar samples) within 50 to 550 km, but a negative correlation (more dissimilar samples) when > 600 km apart .Profiles were spatially clustered, with profiles P13 and P15 found exclusively in Alberta , and thoistances . There wF = 30.36, p < 0.001) .In a dbRDA using the two spatial variables, latitude and longitude, 21.1% of the variance of the genetic matrix was explained by geographical distance . Still, only the longitude—the axis dividing provinces—was significant  could have also been determinant in the first stages of invasion, boosting local spread, but producing in turn geographical isolation of the different genotypes introduced.During the process of invasion, the likelihood of successful establishment will strongly depend on the propagule pressure (i.e. the number of introduction events and the number of infective stages released). Therefore, the repeated release of a large number of individuals in multiple locations can facilitate the long-term establishment and increase the rate of spread of non-native populations . HoweverThe EmsB microsatellite enabled detection of more profiles  in comparison to only seven mtDNA haplotypes, helping to identify local genetic variation and trace the invasion process of the parasite. When compared to a world database, most of the profiles we found (13 of 16) clustered within the European group, having a low genetic distance to European isolates (ranging from 0.05 to 0.1). Moreover, half of the profiles were genetically indistinguishable from known European ones, supporting the hypothesis of a recent introduction of these European genetic variants. By contrast, the remaining three EmsB profiles (P1–P3), which were genotyped as North American haplotype N2 based on mtDNA, clustered within the Arctic clade and were genetically more distant from the Alaskan and Svalbard isolates (0.21 to 0.42), suggesting a more prolonged genetic isolation, consistent with the greater divergence between the haplotypes N1 and N2 ,57.et al. [Complete agreement between the two markers, in correlation with the geographical location, is not guaranteed. Umhang et al.  describeCmax = 94.5%, were as high as 15 (q = 1) and 11.5 (q = 2), with an estimated richness of 23 profiles. In comparison, in the province of Alberta , only 15 EmsB profiles were found, with genetic diversity indices between 2.3 to 5.1 (q = 1) and 2.1 to 4.1 (q = 2) in the five subareas, indicating low genetic diversity. This is inconsistent with the hypothesis of an undetected historical endemic area of the European-like clade. Moreover, the genetic indices recalculated for Switzerland were more than three times higher than those calculated for all of Alberta.In Europe, the expansion history of the parasite from the historical endemic core area into peripheral regions has been governed by a mainland–island system of transmission, in which the ancestral focus in central Europe served as a ‘mainland’ supplying the peripheral areas (islands), perhaps due to dispersals of fox populations. From a genetic perspective, this resulted in invasion events with introduction of only a few genetic profiles, resulting in low genetic diversity in the colonized region compared to greater diversity in the ancestral endemic foci ,50,58. IE. multilocularis populations from all five subregions was low, with widespread distribution of two profiles (P13 and P15) representing 77% of the samples from this province. Moreover, these two profiles might be considered as part of one population, due to: (i) the low genetic distance between them (0.14), (ii) their overlap in geographical distribution and (iii) the relatively high frequency of co-occurrence in the same host (19 hosts). These results were consistent with previous studies using mtDNA showing a higher prevalence and wider geographical distribution of the haplotype ECA, the causative agent of the most recent human cases of AE in Alberta [E. multilocularis individuals in that province.In this Canadian province, the highest genetic richness was found in the central east region, and the highest genetic diversity based on the abundance of each EmsB profile was found in the southwestern region. However, the degree of differentiation between  Alberta ,26. Ther2) and higher dispersal distances (up to 300 km) [The subsequent spread across the province was likely aided by red foxes, but mostly by the most abundant host, the coyote, which has a larger home range (greater than 100 km 300 km) –62. In a 300 km) , which c 300 km) . Furtheret al. [To estimate the expansion rate of the parasite, Takumi et al.  used a met al. . Similaret al. .In a previous study using EmsB to assess the genetic diversity of the parasite at a continental scale in Europe , only 5%et al. [Despite the smaller sample size in Saskatchewan and British Columbia, our results were consistent with previous studies using mtDNA characterization in these areas. In British Columbia, Gesy et al.  reportedet al. . Howeveret al. .E. multilocularis in North America. The use of EmsB improved characterization of parasite diversity, even at a fine geographical scale, whereas the use of mtDNA aided in unravelling the evolutionary process of the invasion of the European strain. As evidenced in this and previous studies, the implementation of genetic population structure analysis is a powerful tool to trace the origins and history expansion of parasites and their hosts in the areas invaded and for the assessment of public health risks. For example, a genetic study of raccoons (Procyon lotor) in the Netherlands (where they were previously absent) and their GI nematode parasite (Baylisascaris procyonis) showed that most of the Dutch raccoons and their roundworms were introduced through ex-captive individuals, which ultimately aided in the development of control measures of these invasive populations [Schistosoma mansoni, a blood fluke that infects humans and that was introduced into the Americas from Africa during the Trans-Atlantic slave trade [S. mansoni parasites were pre-adapted to the Americas and able to establish with relative ease.This was the first large-scale study using both nuclear and mitochondrial markers to assess the genetic diversity of ulations . Recentlve trade . In thisE. multilocularis including wild and domestic hosts from British Columbia and Saskatchewan, as well as from eastern Canadian provinces where the parasite has been recently detected, and in central US states, will be pivotal to understanding the current distribution and expansion trend of the European strains in continental North America, including the role of the main definitive hosts (coyotes and foxes) in harbouring and spreading these strains.Further work on the study of genetic diversity of


Contrast maximization (CMax) is a framework that provides state-of-the-art results on several event-based computer vision tasks, such as ego-motion or optical flow estimation. However, it may suffer from a problem called event collapse, which is an undesired solution where events are warped into too few pixels. As prior works have largely ignored the issue or proposed workarounds, it is imperative to analyze this phenomenon in detail. Our work demonstrates event collapse in its simplest form and proposes collapse metrics by using first principles of space–time deformation based on differential geometry and physics. We experimentally show on publicly available datasets that the proposed metrics mitigate event collapse and do not harm well-posed warps. To the best of our knowledge, regularizers based on the proposed metrics are the only effective solution against event collapse in the experimental settings considered, compared with other methods. We hope that this work inspires further research to tackle more complex warp models. Event cameras ,2,3 offeThe main idea of CMax and similar event alignment frameworks ,28 is toContrary to the expectation that event collapse occurs when the event transformation becomes sufficiently complex ,27, we s(1)A study of the event collapse phenomenon in regard to event warping and objective functions .(2)Two principled metrics of event collapse (one based on flow divergence and one based on area-element deformations) and their use as regularizers to mitigate the above-mentioned phenomenon .(3)Experiments on publicly available datasets that demonstrate, in comparison with other strategies, the effectiveness of the proposed regularizers .In summary, our contributions are:To the best of our knowledge, this is the first work that focuses on the paramount phenomenon of event collapse, which may arise in state-of-the-art event-alignment methods. Our experiments show that the proposed metrics mitigate event collapse while they do not harm well-posed warps.Our study is based on the CMax framework for event alignment  ,30, hencIn the second step of CMax, several event-alignment objectives have been proposed to measure the goodness of fit between the events and the model ,13, estaIn which estimation problems does event collapse appear? At first look, it may appear that event collapse occurs when the number of DOFs in the warp becomes large enough, i.e., for complex motions. Event collapse has been reported in homographic motions (8 DOFs) [(8 DOFs) ,31 and i(8 DOFs) , where aHow did previous works tackle event collapse? Previous works have tackled the issue in several ways, such as: (i) initializing the parameters sufficiently close to the desired solution  [optimum) ; (ii) reoptimum) ,31; (iiioptimum) , thus choptimum) ; and (v)optimum) ,16. ManyLet us present our approach to measure and mitigate event collapse. First, we revise how event cameras work  and the L at a pixel exceeds a contrast sensitivity Event cameras, such as the Dynamic Vision Sensor (DVS) ,3,32, arThe CMax framework  transforrough it , left, ufunction ,13 measuEach IWE pixel ximation , such as the IWE , given bZ (1-DOF warp). In theory, translational motions also require the knowledge of the scene depth. Here, inspired by the 4-DOF in-plane warp in [To analyze event collapse in the simplest case, let us consider an approximation to a translational motion of the camera along its optical axis  warp in  that app warp in   see a. This pk≪1) see B. The opThe above example shows that event collapse is enabled (or disabled) by the type of warp. If the warp does not enable event collapse (contraction or accumulation of flow vectors cannot happen due to the geometric properties of the warp), as in the case of feature flow (2 DOF) ,30 Figub or rotaOn the other hand, if the warp allows for event collapse, more complex scenarios can be described by such a broader class of motion hypotheses, but the optimization framework designed for non-event-collapsing scenarios  may not hold anymore. Optimizing the objective function may lead to an undesired solution with a larger value than the desired one. This depends on multiple elements: the landscape of the objective function , and the initialization and search strategy of the optimization algorithm used to explore such a landscape. The challenge in this situation is to overcome the issue of multiple local maxima and make the problem better posed. Our approach consists of characterizing event collapse via novel metrics and including them in the objective function as weak constraints  to yield a better landscape.Inspired by physics, we may think of the flow vectors given by the event transformation  charges . SourcesThe warp For the warp in , we obta. Hence,  defines Computing the divergence at each event gives the setTo have a 2D visual representation (“feature map”) of collapse, we build an image (like the IWE) by taking some statistic of the values In practice we focus on the collapsing part by computing a trimmed mean: the mean of the DIWE pixels smaller than a margin In addition to vector calculus, we may also use tools from differential geometry to characterize event collapse. Building on , the poiθ)∂xsee . The detFor the warp in , we haveComputing the amplification factors at each event gives the setWe build a deformation map (or image of warped areas (IWA)) by taking some statistic of the values This assumes that if no events warp to a pixel To concentrate on the collapsing part, we compute a trimmed mean: the mean of the IWA pixels smaller than a margin Event-based feature tracking is often described by the warp In-plane translation warps, such as the above 2-DOF warp, are well-posed and serve as reference to design the regularizers that measure event collapse. It is sensible for well-designed regularizers to penalize warps whose characteristics deviate from those of the reference warp: zero divergence and unit area amplification factor.As the previous sections show, the proposed metrics designed for the zoom in/out warp produce the expected characterization of the 2-DOF feature flow (zero divergence and unit area amplification), which is a well-posed warp. Hence, if they were added as penalties into the objective function they would not modify the energy landscape. We now consider their influence on rotational motions, which are also well-posed warps. In particular, we consider the problem of estimating the angular velocity of a predominantly rotating event camera by means of CMax, which is a popular research topic ,27,28,29 formula ,36).Divergence: It is well known that the flow is y matrix . Hence(Area element: Letting –(34) in ,(19)detZ axis clearly present no deformation, regardless of the amount of rotation, and this is captured by the proposed metrics because: (i) the divergence is zero, thus the flow is incompressible, and (ii) Rotations around the Planar motion is the term used to describe the motion of a ground robot that can translate and rotate freely on a flat ground. If such a robot is equipped with a camera pointing upwards or downwards, the resulting motion induced on the image plane, parallel to the ground plane, is an isometry (Euclidean transformation). This motion model is a subset of the parametric ones in , and it The 1-DOF zoom in/out warp in We propose to augment previous objective functions  with peWe may interpret us score ) as the We evaluate our method on publicly available datasets, whose details are described in MVSEC dataset [outdoor_day1 sequence with a forward motion. This motion is reasonably well approximated by collapse-enabled warps such as  of the box sequence, and 15 million events (11 s) of the dynamic sequence.The  dataset  is the d dataset ,46,47,48 dataset   and the selected sequences in the ECD dataset have different type of motions: forward (which enables event collapse) vs. rotational (which does not suffer from event collapse). Each sequence serves a different test purpose, as discussed in the next sections.N pixels  are the average endpoint error (AEE) and the percentage of pixels with AEE greater than L metric  to assesFollowing previous works ,27,28, rlignment .The event time windows are as follows: the events in the time spanned by ndard in ,18,41), ndard in . The regG: the IWE variance  work well to mitigate the collapse, as observed in smaller AEE and hitening , our reghitening , or 10–2hitening ); howevendard in ): using The FWL values of our regularizers are moderately high (≥1), indicating that event alignment is better than that of the identity warp. However, because the FWL depends on the number of events , it is nThe collapse results are more visible in The landscapes of loss functions as well as sensitivity analysis of imestamp ) suffer G in  requires more computation than the non-regularized objective. However, complexity is linear with the number of events and the number of pixels, which is an advantage, and the warped events are reutilized to compute the DIWE or IWA. Hence, the runtime is less than doubled . BecausSpecifically, we use the MVSEC dataset, in a clip where the scene consists of two motions: the ego-motion (forward motion of the recording vehicle) and the motion of a car driving in the opposite direction in a nearby lane (an independently moving object—IMO). We model the scene by using the combination of two warps. Intuitively, the 1-DOF warp  describeWe have analyzed the event collapse phenomenon of the CMax framework and proposed collapse metrics using first principles of space-time deformation, inspired by differential geometry and physics. Our experimental results on publicly available datasets demonstrate that the proposed divergence and area-based metrics mitigate the phenomenon for collapse-enabled warps and do not harm well-posed warps. To the best of our knowledge, our regularizers are the only effective solution compared to the unregularized CMax framework and whitening. Our regularizers achieve, on average, more than 90% improvement on optical flow endpoint error calculation (AEE) on collapse-enabled warps.This is the first work that focuses on the paramount phenomenon of event collapse. No prior work has analyzed this phenomenon in such detail or proposed new regularizers without additional data or reparameterizing the search space ,16,27. A


P. trituberculatus is an economically important mariculture species in China. Evaluating its genetic diversity and population structure can contribute to the exploration of germplasm resources and promote sustainable aquaculture production. In this study, a total of 246,243 SSRs were generated by transcriptome sequencing of P. trituberculatus. Among the examined 254,746 unigenes, 66,331 had more than one SSR. Among the different SSR motif types, dinucleotide repeats  were the most abundant. In 173 different base repeats, A/T (96.86%), AC/GT (51.46%), and ACC/GGT (26.20%) were dominant in mono-, di-, and trinucleotide, respectively. GO annotations showed 87,079 unigenes in 57 GO terms. Cellular process, cell, and binding were the most abundant terms in biological process, cellular component, and molecular function categories separately. A total of 34,406 annotated unigenes were classified into 26 functional categories according to the functional annotation analysis of KOG, of which “general function prediction only” was the biggest category . KEGG pathway annotations revealed the clustering of 34,715 unigenes into 32 different pathways. Nineteen SSRs were identified as polymorphic and, thus, used to assess the genetic diversity and structure of 240 P. trituberculatus individuals from four populations in the Bohai Sea. Genetic parameter analysis showed a similar level of genetic diversity within wild populations, and the cultured population indicated a reduction in genetic diversity compared with wild populations. The pairwise STF values were between 0.001 and 0.04 with an average of 0.0205 (p < 0.05), suggesting a low but significant level of genetic differentiation among the four populations. Structure analysis demonstrated that the four populations were classified into two groups including the cultured group and other populations. The phylogenetic tree and PCA revealed that a vast number of samples were clustered together and that cultivated individuals were distributed more centrally than wild individuals. The findings contribute to the further assessment of germplasm resources and assist to provide valuable SSRs for marker-assisted breeding of P. trituberculatus in the future. P. trituberculatus, is an edible portunid of great commercial significance, which has been widely farmed in China. Due to its high nutritional value and rapid growth, P. trituberculatus has become one of the most important economic crab species in marine aquaculture  have become an ideal molecular marker in population genetics research because of their co-dominant inheritance, high polymorphism, reproducibility, hyper-variable, transferability, random distribution in the genome, and ease of analysis via PCR  (Macrobrachium nipponense) (Scylla paramamosain) (Paphia textile (Penaeus monodon (Pinctada maxima)  is widely used in the study of species genetics because of its wide dynamic range, precise, sensitivity, unbiased quantification of transcripts, and comprehensive coverage of all expressed sequences in a given tissue sample . Now, RNnbergii) , orientaponense) , mud craamosain) , Paphia  textile , Penaeus monodon , and pea maxima) . Transcr maxima) . Neverthutrition , developutrition , reproduutrition , and sexutrition , while mP. trituberculatus; and 3) evaluate the genetic diversity and structure among different populations using the polymorphic SSR markers from transcriptome sequencing. Our findings not only contribute to molecular genetic analyses but also provide valuable information for effective breeding and conservation strategies of the P. trituberculatus aquaculture.The present study aimed to 1) develop SSR markers with RNA-seq technology; 2) characterize the transcriptome of A total of 240 swimming crab samples from four populations were collected , includiP. trituberculatus  were collected from the national breeding farm of swimming crabs in Huanghua, China. The crabs were all anesthetized on ice and dissected to collect muscle and ovaries samples. All of the samples were rapidly flash-frozen in liquid nitrogen and stored at −80°C for RNA extraction. An equal amount of either muscle tissue or ovaries tissue was dissected from each of three individuals and pooled into one mixed sample. A total of 12 mixed samples were produced, including 6 mixed muscle samples (3 wild and 3 farmed) and 6 mixed ovarian samples (3 wild and 3 farmed), each containing three biological replicates. Total RNA was extracted from each mixed sample using the TRIzol reagent  following the manufacturer’s protocol. RNA degradation and contamination were monitored on 1% agarose gels, and RNA purity was checked using the NanoPhotometer® spectrophotometer . RNA concentration and integrity were measured using the Qubit® RNA Assay Kit in Qubit® 2.0 Flurometer  and the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system , respectively.A total of 18 individuals ® Ultra™ RNA Library Prep Kit for Illumina®  following the manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). First-strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase (RNase H−). Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3′ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure was ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 250–300 bp in length, the library fragments were purified with AMPure XP system . Then 3 μl USER Enzyme  was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95°C before PCR. Subsequently, PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and Index (X) Primer. Then PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system. At last, the library preparations were sequenced on an Illumina HiSeq 4000 platform and paired-end reads were generated at Novogene Corporation .A total amount of 1.5 μg RNA from each sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using the NEBNext−5 (Raw data (raw reads) of fastq format were firstly processed through in-house perl scripts. In this step, the clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N, and low-quality reads  from raw data. At the same time, Q20, Q30, GC-content, and sequence duplication levels of the clean data were calculated. All the downstream analyses were based on clean data with high quality. Transcriptome assembly of the high-quality clean reads was accomplished using Trinity software with default settings . In orde−5  was perf−5 .P. trituberculatus transcriptome sequencing using MISA software version 1.0 (http://pgrc.ipk-gatersleben.de/misa/misa.html). The minimum number of repeats was defined as ten for mononucleotide repeats, six for dinucleotide repeats, five for tri-, tetra-, penta-, and hexanucleotide repeats. Primer pairs for each SSR locus were designed using Primer3 (http://primer3.sourceforge.net/releases.php) according to the following criteria: 1) primer length of 18–25 bp; 2) annealing temperature (Tm) between 55°C and 62°C; 3) GC content from 40% to 60%; 4) PCR product length of 90–250 bp; 5) avoidance of primer dimers and hairpin structures. SSR primers were synthesized by General Biosystems Co., Ltd. . Twelve samples were used to identify the polymorphism of the selected SSR primers through PCR amplification and 8% non-denaturing polyacrylamide gel electrophoresis with pBR322 DNA/MspI (MBI) as a standard DNA marker.SSR loci were identified throughout all unigenes generated by the 2O. Amplification cycles consisted of initial denaturation (5 min at 95°C), followed by 34 cycles of denaturation (30 s at 95°C), annealing (30 s at 60°C), extension (30 s at 72°C), and further extension (10 min at 72°C). After amplification, PCR products were diluted 10 times in sterile water. The pooled sample was composed of 20 μl Hi-Di formamide and 0.2 μl GeneScan 500 ROX Size Standard. An ABI 3730XL Genetic Analyzer  was used to conduct capillary electrophoresis (CE) following the manufacturer’s instructions. Each CE sample contained 1 μl diluted PCR product and 15 μl pooled sample. Allele sizes (in base pairs) were determined with GeneMarker® Fragment Analysis Software  on the comparison of the position of the internal size standard in each lane with the position of the peak value of each sample.A total of 19 pairs of polymorphic SSR primers were identified and used for subsequent analysis . All forNa), the effective number of alleles (Ne), the observed heterozygosity (HO) and expected heterozygosity (HE) using POPGENE version 1.3 (PIC) per locus was estimated by PIC-CALC software (Fna) for loci was calculated using GenePOP  at each locus with POPGENE version 1.3. Genetic differentiation index (FST) between populations and analysis of molecular variance (AMOVA) were calculated using GenAlEx 6.5  between successive K-values, the structure output was entered into Structure Harvester (The phylogenetic tree was constructed using the neighbor-joining (NJ) method as implemented in MEGA7 . Principarvester  to deterP. trituberculatus was conducted to generate RNA sequences, and the statistical data has been shown in The transcriptome sequencing of 12 mixed samples from muscle and ovary of Zootermopsis nevadensis , Daphnia pulex , Tribolium castaneum , Stegodyphus mimosarum , Crassostrea gigas , and other   annotated, followed by PFAM , Nr , SwissProt , KO , KOG , and Nt  database . In all,, 71.5%) .For the functional annotation and classification of the assembled unigenes, 87,079 unigenes were assigned to 57 GO terms which included three ontology categories: biological process , cellular component , and molecular function  . The maiIdentification of the biological pathways was performed according to the KEGG annotations, which showed the clustering of 34,715 unigenes into 32 pathways . DetaileP. trituberculatus. The distribution density was one SSR per 1.11 kb, and 66,331 sequences contained more than one SSR , followed by mono- , tri- , tetra- , penta- , and hexanucleotide   . The num, 0.16%) .Of the 246,243 SSR loci, 173 different repeat motifs were detected . Among tP. trituberculatus.A total of 104,424 pairs of SSR primers were designed, 150 of which were randomly selected to identify polymorphism . FinallyPIC values ranged from 0.598 (TRNA3) to 0.948 (PrMa05) with a mean of 0.802, showing that these SSRs has high polymorphism (PIC > 0.5) and are suitable for the evaluation of genetic diversity in P. trituberculatus populations. A total of 243 alleles were found with an average of 12.79 per locus. The Ne values ranged from 2.751 to 20.156 with a mean of 7.095. The HO and HE ranged from 0.467 to 0.938 (mean: 0.692) and from 0.636 to 0.95 (mean: 0.824), respectively. The three wild populations  showed a similar level of genetic diversity, while the cultivated population indicated a reduction in genetic diversity compared with them due to the relatively smaller genetic parameters (HO < HE) was observed, with the exception of PrMa01, DX05, and DX19.All parameters of the 19 SSR loci were calculated . The PICrameters . NotablyFST (0.040) (p < 0.05) whereas the lowest FST of 0.001 (p < 0.05) was observed between populations PL and QHD       than shellfish, fish, and other crustaceans (P. maxima (1/29.73 kb) (Apostichopus japonicus) (1/29.2 kb) (Siniperca chuatsi) (1/26.28 kb) (Paramisgurnus dabryanus) (1/6.99 kb) (Cheilinus undulatus) (1/5.35 kb) (Lota lota) (1/4.25 kb) (Harpadon nehereus) (1/3 kb)  (1/0.756 kb) (Eucommia ulmoides) (1/0.73 kb)  , sea cuc29.2 kb) , mandari6.28 kb) , large-s6.99 kb) , giant w5.35 kb) , burbot 4.25 kb) , and the(1/3 kb) , while l.756 kb) , tu-chun0.73 kb) . The dif0.73 kb) . In addi0.73 kb) .Scapharca kagoshimensis), AC/GT was also predominant among dinucleotide repeats (Chlamys farreri) (Argopecten irradians)   and bay radians)  from the and heterozygosity  is more accurate than the observed heterozygosity (HO) for evaluating the level of population genetic diversity because Ho is easily influenced by sample sizes (HE = 0.73–0.84) to high (HE = 0.916–0.918) genetic diversity of P. trituberculatus was revealed by HE ranged from 0.675 to 0.706 which is lower than those observed in the above studies, thus showing a lower level of genetic diversity among the four populations. This may be attributable to the special geographical location of the Bohai Sea. The Bohai Sea is a semi-enclosed shallow water body and has limited connectivity to the Yellow Sea by the Bohai Strait, which restricts the dispersal of crabs and thus results in low genetic diversity  which is less than 0.05  adjacent to Liaodong Bay, respectively, and found low but significant levels of genetic differentiation , suggesting that large-scale stock enhancement of P. trituberculatus presents potential genetic risks on wild populations, and that the relevant management measures should be formulated to achieve successful stock enhancement and resource restoration for the swimming crab.ntiation . The curhan 0.05 , indicatl region . This gePortunus pelagicus)  was significantly greater than that among populations (2%). The result corresponded to the genetic variation found in blue swimmer crab (lagicus) . Most lolagicus) . AdditioP. trituberculatus, the understanding genetic structure is crucial for the development of effective management strategies and can provide a genetic tool for breeding and offer a scientific support for resource conservation of this species (P. trituberculatus specimens from the four populations cluster into two groups including the cultured group and the wild group (A stable genetic structure is central to species survival. Its disintegration will lead to decreased populations or even extinction. Given the economic significance of  species . The resld group . Some ofld group .P. trituberculatus in the Bohai Sea for formulating scientific management measures to prevent mutual interference between them.The individual-level phylogenetic tree and PCA illuminated that all individuals showed some degree of genetic connectivity, and that the cultured individuals were relatively concentrated in comparison with wild individuals. Despite the annual release activities, the gene exchange between cultivated and wild populations is limited when compared to that between wild populations in the open sea, thus leading to the separation of cultured individuals from all individuals, showing more obvious particularity. Therefore, it is vital to further investigate the genetic structure of wild and cultured populations of P. trituberculatus. Nineteen polymorphic SSRs were identified and used to investigate the genetic variation and structure of the four P. trituberculatus populations from the Bohai Sea. The findings revealed a lower level of genetic diversity in P. trituberculatus populations from the Bohai Sea when compared to the other populations from the Yellow Sea and the East China Sea. The pairwise FST values showed low but significant genetic differentiation between populations. The population structure analysis, phylogenetic tree, and PCA showed a mixing of wild and cultivated individuals, which corroborated the genetic connectivity between them, but cultivated individuals were distributed more centrally than wild individuals. In addition, heterozygote deficiencies, null alleles, and significant deviation from HWE at many SSR loci were observed. Therefore, practical and effective measures are expected to be taken to reinforce the identification and protection of genetic diversity and prevent degeneration of P. trituberculatus germplasm. For example, developing high-quality markers such as SNPs using a chromosome-level genome of P. trituberculatus (P. trituberculatus in the Bohai Sea. Additionally, increasing the scale of swimming crab aquaculture, extending the fishing moratorium, and performing long-term genetic monitoring is also helpful for the conservation and utilization of germplasm resources in P. trituberculatus. In conclusion, the results improve our understanding of the population genetic structure of P. trituberculatus in the Bohai Sea and provide valuable information for the selection breeding of this species.Overall, this study performed assembly of transcriptome sequences, functional annotation, and SSR markers discovery of rculatus , and car


Peste des petits ruminants virus (PPRV) causes a highly devastating disease of sheep and goats, peste des petits ruminants (PPR), which is targeted for global control and eradication by 2030. The serological diagnostic tool kits for accurate diagnosis of PPR have inherent strengths and weaknesses that require parallel validation and optimization across animal species. Thus, the objective of this study was to evaluate diagnostic performance of haemagglutinin based PPR blocking ELISA (HPPR- b-ELISA), that was developed by Africa Union Pan African Veterinary Vaccine Center for specific detection of anti- PPRV antibodies.In preliminarily investigation, diagnostic performance of the HPPR-b-ELISA®, commercial PPR competition ELISA (c-ELISA) and virus neutralization test (VNT) were compared for the detection of anti-PPRV antibodies in goats, sheep, cattle and camels.The sensitivity and specificity of HPPR- b-ELISA® were 79.55 and 99.74%, respectively, compared to c-ELISA. The HPPR- b-ELISA® was in perfect agreement (κ = 0.86) with the c-ELISA in all sera collected from goats, sheep and cattle. There was almost perfect agreement between the species of goats (κ = 0.82) and sheep (κ = 0.98), while the agreement was substantial in cattle (κ = 0.78) and no agreement was observed in camels (κ = 0.00). Similarly, the sensitivity and specificity of the HPPR b-ELISA were 80 and 96.36%, respectively compared to VNT with almost perfect agreement in goats (κ = 0.83) and sheep (κ = 0.89), moderate in cattle (κ = 0.50) and none in camels (κ = 0.00).Our study revealed that HPPR- b-ELISA is a suitable and valid method that can alternatively be used for screening and monitoring of PPR in sheep, goats and cattle except for camels. Peste des petits ruminants virus (PPRV) causes a highly devastating disease of sheep and goats, peste des petits ruminants (PPR), that threatens food security and the conservation of wild small ruminants , 2. CurrExposure to PPRV is primarily diagnosed by several serological assays that target anti-PPRV antibodies . The genSerological tests like blocking ELISA and competitive ELISA are the leading PPRV antibody detection methods and known for their simplicity and capacity to screen large number of samples, hence, they are suitable for sero-surveillances and sero-monitoring . It is iCurrently, the only commercial test available for detection of antibodies generated against PPRV is the N-based c-ELISA  . NeverthAll sera sample from goats and sheep were collected from Oromia regional State, Borena zone, Yabello district, while samples from cattle were collected from Tigray regional state around Mekele city, whilst camels’ sera were collected from Afar regional state, Awash Fentale district.A total of 480 sera samples were collected from goats, sheep, cattle and camels during the active PPR outbreak. Approximately 5 ml blood samples were collected from the jugular vein of each animal using plain vacutainer tubes. Serum was emptied and aliquoted into 1.8 ml cryovial, kept in an icebox containing icepacks, and transported to the laboratory for sample processing and laboratory investigations and temporary stored at -20 °C until tested.2SO4 was distributed to all wells and read OD in wells using ELISA reader  with an inference filter at 450 nanometers connected to a computer loaded with ELISA data information software (Gen 5.3.04) for automated reading and calculation of the percentage inhibition (PI) values. The test result is said to be positive if the PI (%) value is ≥35%.HPPR b-ELISA is PPR test developed by AU-PANVAC at Bishoftu in Ethiopia . BrieflyThe nucleoprotein-based c-ELISA kit was used and the test carried out according to the manufacturer’s protocol  .3 TCID50/ml was distributed to all wells. The control plate containing both negative and positive control was prepared separately. The Negative control contains six wells with 200 µl cell culture medium without the virus. Positive control was arranged as six wells each for 100 TCID50, 10, 1 and 0.1 TCID50/well. Plates were then stayed in the incubator for one hour at 37 °C after which 50 µl of the suspension of Vero dog SLAM cells (4 × 105 cells per ml) was distributed to all wells. The plates were put in an incubator with 5 5% CO2 at 37 °C. Finally, the plates were followed using an inverted type of microscope, to monitor the cytopathic effect (CPE) starting day 3 of incubation. The CPE was observed when the serum was negative, in contrary, no CPE was observed if there were neutralizing antibodies in the serum against PPRV. Serum was considered positive for PPRV antibodies if the neutralizing dilution was greater than or equal to 1:10 [The VNT was golden standard test used for confirmation test for ELISA test results. The test sera were thawed and deactivated by heating at 56 °C for 30 min in a water bath. Serum samples to be tested were diluted one in five and following two-fold serially diluted with minimum essential media (100 µl/well). Then, 100 µl of PPRV  at 10 to 1:10 , 16.The data from serological tests was entered into Microsoft Excel® 2013, filtered and coded and analyzed using a statistical package of STATA version 12.0. The agreement between the tests was determined using Cohen’s kappa statistics. The kappa value was interpreted as follows: Kappa value (κ) ≤ 0 as indicated no agreement, 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as substantial, and 0.81-1.00 as almost perfect agreement , 18. A sThe efficacy of the HPPR b-ELISA was compared with that of the ID Screen® PPR c-ELISA kit by employing of 120 goats’ sera (Table The HPPR b-ELISA® was found to detect 31 25.83%) anti-PPRV antibodies, while ID Screen® PPR c-ELISA detected anti-PPRV antibodies in 32 (26.67%) in sheep sera  anti-PPRV antibodies, while ID Screen® PPR c-ELISA detected 46 (38.33%) antibodies directed against PPRV in cattle sera  samples that showed negative result with HPPR b-ELISA® were shown positive with ID Screen® PPR c-ELISA, whereas samples that were negative by ID Screen® PPR c-ELISA were not positive with HPPR® b-ELISA Table .The diagnostic specificity of HPPR b-ELISA® in relation to ID Screen® PPR c-ELISA was 100.00%, while that of ID Screen® PPR c-ELISA compared to HPPR b-ELISA® was 96.67%, in camels.A total of 80 sera samples of all species were selected for testing. The samples that were positive with either HPPR b-ELISA® (N = 1) or ID Screen® PPR c-ELISA N = 15), negative (N = 43), and positive (N = 21) with both tests were used to validate the results of the two ELISA tests with VNT. Compared with VNT, the HPPR b-ELISA® had a sensitivity and specificity of 80.00 and 96.36%, respectively (κ = 0.78). The agreement between HPPR b-ELISA® and VNT was almost perfect in goats (κ = 0.83) and sheep (κ = 0.89), moderate in cattle (κ = 0.50), and no agreement in camels (κ = 0.00). The relative sensitivity and specificity of the HPPR b-ELISA® test compared to VNT in each species of animals is illustrated in Table , negativThe ID Screen® PPR c-ELISA had 92.00% diagnostic sensitivity and 76.36% specificity when compared with the gold standard test with a substantial agreement (κ = 0.61). The agreement between ID Screen® PPR c-ELISA and VNT was substantial in goats (κ = 0.69) and sheep (κ = 0.78), fair in cattle (κ = 0.30), and no agreement in camels (κ = 0.00). The relative sensitivity of the ID Screen® PPR c-ELISA test compared to VNT in each species of animals is presented in Table ® PPR c-ELISA and validate the results of the tests against VNT for specific detection of antibodies directed against PPRV in Goats, sheep, cattle and camel field samples. The performance of the diagnostic sensitivity and specificity of HPPR b-ELISA performance was found comparable to the ID Screen® PPR c-ELISA.In the present study, we compared the diagnostic performance of HPPR b-ELISA® test with the commercial ID ScreenWith the 480 sera samples collected from goats, sheep, cattle and camels from different parts of Ethiopia, HPPR b-ELISA detected PPRV antibodies in agreement to the ID Screen® PPR c-ELISA sera samples of ruminants and camels. The diagnostic sensitivity and specificity of HPPR b-ELISA® test relative to ID Screen® PPR c-ELISA were 83.33 and 99.12%, in goats, 96.88 and 100% in sheep, 73.91 and100% in cattle, 0.00% and 100% in camels, respectively. The HPPR b-ELISA® and ID Screen® PPR c-ELISA showed a good correlation(K = 0.86) which is in agreement with McHugh  who indiMore specifically, the agreement between the two tests in detecting antibodies against PPRV was almost perfect in goats (κ = 0.82) and sheep (κ = 0.98) and substantial in cattle (κ = 0.78) indicating that the two tests were in agreement in detecting anti-PPRV antibodies in ruminants’ sera. More importantly, the finding demonstrated that the HPPR b-ELISA® test can be used as an alternative to ID Screen® PPR c-ELISA in detecting anti-PPRV antibodies in small ruminants and cattle. Mapaco et al. used ID In contrast to other species, HPPR b-ELISA® and ID Screen® PPR c-ELISA had no agreement in detecting anti-PPRV antibodies in camels’ sera (κ = 0.00). Further investigations are needed why the two tests do not agree in detecting antibodies against PPRV in camels’ sera and weather a new protocol is needed in either or both tests to use them in detecting anti-PPRV antibodies in camels’ sera. For instance, previous studies revealed that the haemagglutinin protein (H)-based competitive enzyme-linked immunosorbent assay (H c-ELISA) has a lower sensitivity in cattle compared to domestic sheep and goats –23. The The study showed that HPPR b-ELISA® was slightly less sensitive than ID Screen® PPR c-ELISA which might be related to the fact that the N-protein is very important protein produced during virus replication that induce a high level of antibodies in the beginning of morbilliviruses infections or vaccination . Antibod® PPR c-ELISA were 80.00 and 92.00%, respectively relative to the VNT. The HPPR b-ELISA® test had higher specificity (96.36%) than the ID Screen® PPR c-ELISA (76.36%) compared to the VNT. Both ELISA tests had excellent sensitivity (100.00%) in goats and sheep compared to the VNT. The HPPR b-ELISA® test had higher specificity (94.12%) than the ID Screen® PPR ELISA (88.24%) in compared with the gold standard test in goats. Jacobson [The sensitivity of HPPR b-ELISA® and ID ScreenJacobson  describeSimilarly, both ELISA tests had acceptable specificity, which was 75.00 and 87.50% for the ID Screen® PPR c-ELISA and HPPR® b-ELISA, respectively in sheep. The ID Screen® PPR c-ELISA and HPPR b-ELISA® had a sensitivity of 80.00 and 50.00%, respectively, while the diagnostic specificity of ID Screen® PPR c-ELISA and HPPR b-ELISA® was 50 and 100%, respectively with a fair agreement for ID Screen® PPR c-ELISA and moderate agreement for HPPR b-ELISA® in cattle. The relative sensitivity and specificity of ID Screen® PPR c-ELISA in reference laboratories were 94.5 and 99.4%, respectively . Saliki In camels, four sera were found to be positive using ID Screen® PPR c-ELISA, while all sera were negative using both VNT and HPPR b-ELISA®. This might indicate the ID Screen® PPR c-ELISA shows false positive in detecting antibodies against PPRV in camels. VNT negative sera that were found positive with ID Screen® PPR c-ELISA and HPPR b-ELISA® might be due to the reactivity with related antibodies produced by other morbilliviruses . In factIn conclusion, both the HPPR b-ELISA® and commercial ID Screen® PPR c-ELISA tests were efficient serological methods relative to the gold standard test for the specific detection of antibodies against PPRV in goats, sheep and cattle sera. In these animal species, the HPPR b-ELISA® can be used as an alternative test for PPR screening and monitoring. Moving forward, a clear protocols and guidelines for the interpretation of PPR serological test results in a typical and wildlife host species need to be established. Parallel and replicated testing of samples with several diagnostic methods will contribute to our understanding of the respective performance and accuracy of each diagnostic test. This will help to select fit-for- purpose serology use in various epidemiological situations that may arise during and after Global PPR Control and eradication Programme.