Amphilophus astorquii, endemic to Crater Lake Apoyo in Nicaragua, using 11 polymorphic microsatellite loci . We found no population structure in A. astorquii either within colonies , or at the lake-wide level (pairwise population differentiation FST = 0–0.013 and no clustering), and there was no sex-bias  to this lack of genetic structure. These patterns may be driven by the colony-breeding reproductive behaviour of A. astorquii. The results suggest that strong philopatry or spatial assortative mating are unlikely to explain the rapid speciation processes associated with the history of this species in Lake Apoyo.Fine-scaled genetic structuring, as seen for example in many lacustrine fish, typically relates to the patterns of migration, habitat use, mating system or other ecological factors. Because the same processes can also affect the propensity of population differentiation and divergence, assessments of species from rapidly speciating clades, or with particularly interesting ecological traits, can be especially insightful. For this study, we assessed the spatial genetic relationships, including the genetic evidence for sex-biased dispersal, in a colony-breeding cichlid fish,  Molecular methods have revealed very fine scaled genetic structuring among many animal populations, especially those of lacustrine fish7. These patterns often result from low levels of migration, a lack of significant larval dispersal, and a tendency for philopatry by the adults. Interestingly, high levels of such philopatric behaviour have been found across a wide range of taxa, including insects, fish, amphibians, birds and mammals10.Dispersal, when resulting in gene flow, is a fundamental process in evolution. In interaction with other behavioural characteristics and demography, it can exert a large influence on genetic population structure13. However, such ecological barriers are not necessarily required to induce philopatric behaviour15. For example, in Variabilichromis moorii, a monogamous Lake Tanganyikan cichlid fish, mitochondrial DNA sequences indicated that dispersal is very infrequent and at similar levels along continuous stretches of rocky shore and across localities separated by sandy habitat14. Dispersal patterns affecting the population genetic structure can also be sex specific. For example, in superb fairy-wrens, Malurus cyaneus, that exhibit fine-scale genetic structure, any male dispersal from their natal territory occurs only over very short distances, whereas female fairy-wrens show also relatively long distance dispersal16. In cichlid fish of Lake Malawi, in turn, gene flow occurs primarily by dispersal of sub-adult males that search for breeding territories, with females tending to stay in their natal area17. As a result, kin structuring of the population may be evident only within the philopatric sex, typically males in birds and females in mammals and fish8. Such sex-biased dispersal may be related to avoidance of the risk of inbreeding, competition with kin, sex biased co-operation with kin, or sex specific territorial behaviour21. More generally, mating system characteristics, such as the patterns of territoriality and mating competition, can markedly contribute to the genetic structure25.Patterns of population structure may also relate to habitat use and feeding preferences, with trophic and habitat specialists often exhibiting more restricted gene flow over any habitat discontinuities than more opportunistic species31. This is especially true for philopatry: for example the outstandingly species rich radiations of cichlid fish in the African Great Lakes have been associated with philopatric tendencies, as well as genetic differentiation over short distances within the lakes14. Researchers have also suggested that groups with a greater population genetic structure should be more diverse than clades without fine-scale structure, as reduction in gene flow may be linked to isolation12. On the other hand, lacustrine fish are also known to be particularly prone to speciate in sympatry32, with pronounced differences in speciation rates between even closely related clades33. Therefore, to investigate the importance of different speciation mechanisms, studies on the present genetic structure in clades that are exceptionally fast (or slow) to speciate can be particularly informative35.Factors that affect dispersal and smaller scale genetic structure can also influence the likelihood of speciationAmphilophus astorquii, which is endemic to Crater Lake Apoyo in Nicaragua. Amphilophus astorquii was chosen for the study for multiple reasons. First, it is a species from a particularly rapidly speciating cichlid clade that inhabits mostly Nicaraguan freshwaters, with six endemic species of the group living in Lake Apoyo alone37. Indeed, similarly to its congeners in Nicaraguan crater lakes, A. astorquii seems to have evolved through sympatric speciation39. Second, the species is notable for breeding in dense breeding aggregations, while also showing high levels of territorial competition with its conspecific, and occasionally heterospecific, neighbours40. Third, it has a more benthic life style than Amphilophus zaliosus, a closely related and sympatric pelagic species38. In this regard, pelagic cichlids are often thought to have less population structure than those using more benthic habitats42. Finally, at the time of the study, A. astorquii was locally the most abundant breeding cichlid species43. Such colony-like breeding aggregations allowed sampling at different levels of physical distance, in order to make interferences about migration and gene flow within the lake. Specifically we tested: (a) whether there is genetic differentiation between breeding colonies, and if so, whether such patterns are sex-biased, either of which would suggest site fidelity in the scale of the whole lake; and (b) whether there is a spatial pattern of relatedness within colonies, which would suggest fine-scale site fidelity and/or kin-grouping influencing spatial distributions.In this study, we assessed the population structure at different spatial scales, as well as the genetic evidence for sex-biased dispersal, in a cichlid fish, 44 and is the largest of its kind in Nicaragua, with diameter of ~5 km, shoreline of ~20 km and maximum depth of 175 m45. Our model species, A. astorquii, is endemic to the lake, where it arose by sympatric speciation from a generalist ancestor46. It is the most distinctly colony-breeding species in the Midas cichlid complex, in which males and females are thought to pair monogamously around the same time they claim a territory47 and then provide parental care until their fry are approximately six weeks old. The fish may survive to breed during more than one breeding season .The sampling was conducted in Lake Apoyo. The crater lake formed approximately 23 000 years agoA. astorquii breeding pairs, from here on referred to as ‘colonies’  within Lake Apoyo were associated with beds of Chara algae. Chara does not occur everywhere in the lake, contributing to the patchiness of different habitat types (i.e. sandy and predominantly rocky areas with and without Chara). The colonies were the densest at depths of 4–8 metres, with each pair maintaining a territory of approximately 0.5 m diameter, this also being the minimum distance between adjoining territories ; Colony 2 n = 23 ; Colony 3 n = 37 .Several locations around the lake were pre-surveyed within the limitations of time constraints, divers’ access to the lake from the shore and reasonable dive/swim times from these access points. Five to six colonies were identified, and logistic limitations allowed sampling of three representative colonies Fig.  that wereds Fig.  in areaseds Fig. , with hawth Fig. . The netwth Fig. : Colony 49, M1M, M2, M7, M1250, Unh012, Unh01351, and TmoM752. Ten percent of individuals were repeated as technical replicates. For each locus, one primer was labelled with a NED, FAM or HEX fluorescent dye (Applied Biosystems) for subsequent visualization. Loci were genotyped in an ABI 3130xl (Applied Biosystems) and sized according to Rox standard (Applied Biosystems).Genomic DNA was isolated from ethanol-preserved tissue using standard proteinase K digestion followed by high salt extraction methods, resuspended in water and stored at −20 °C. Eleven microsatellite loci were amplified using standard PCR conditions: Abur151, Abur82, Abur28, Abur4553. Analyses of all samples in a single population suggested a low presence of null alleles (excess homozygosity) at Abur 28 (0.06 Brookfield frequency in homozygotes), Abur45 (0.17), and Unh012 (0.14)54. However this signal was not found when each colony was tested separately , suggesting it is not a pervasive bias in the data and so all loci were retained. To verify the inferred pattern, analyses that might be particularly sensitive to homozygosity were also conducted using a reduced number of loci (see Results). Data are available in Table Genotyping was quality controlled for errors and null alleles in Micro-checker55 and statistical significance assessed with 9999 permutations. Infinite allele model was assumed for calculating the analysis of molecular variance (AMOVA). FIS statistics were calculated in GenePop on the Web v4.2 .Summary statistics of diversity and population structure were calculated in GenoDive v2.0b2756. Genetic and geographic distance matrices were calculated for each of the three colonies using 11 loci and significance assessed with 999 permutations. Distance classes were set at 3  and the number of distance classes set at 30.Spatial genetic autocorrelation was assessed over the three ‘populations’ in Genalex v 6.50157, with model setting for correlated allele frequencies and admixture among populations without population information (no pop flag), run for 600,000 MCMC generations after 100,000 generations burning. Six replicates were run for K = 1 through K = 4, and log-likelihood results plotted in Structure Harvester58.Genetic variation was visualised with an individuals-based Principal Coordinates Analysis (PCoA) of genotypic distances (co-variance-standardized method) in GenAlEx. A genetic clustering analysis was conducted in Structure v 2.4.3Sex-biased dispersal was tested in Genalex. As that test tolerates no missing genotypes, we replaced any missing data with the two most common alleles at that locus  and removed one individual and one locus (TmoM7), with a higher proportion of missing data, in colony 3.MARENA Nicaragua issued the necessary permits for the study (permit numbers 026/-11007/DGAP and DGRNB-IC-006-2007) and it was performed in accordance with the laws of Nicaragua.Data are made available in Table 2 = infinity, Df = 62, Prob = High sign). Each colony also deviated significantly from HWE when assessed separately, though there was no strong pattern across loci and colonies, with the exception of high FIS in Unh012 for all colonies . The PCoA plot (axis 1 vs axis 2) shows some weak separation along coordinate 1 between colony 1 and 2 vs. colony 3, but considerable overlap of individuals from different colonies  being poorly supported, with lower likelihood and higher standard deviation  but remained significant based on FST . Overall this suggests very low population differentiation between colonies.Population genetic differentiation from a pairwise analysis between colonies was also low, with  3 Table , though r = 0.081, p < 0.003) but as this is only one distance class at high linear distance we do not consider it biologically significant. Importantly, in all other distance classes, r falls within the upper and lower bounds and the error bars on r always include 0 . When distance classes are reduced to 15, to only include the maximum class at which there are data from all 3 colonies (as the linear length of colonies differs), there is still no evidence for spatial genetic autocorrelation .A. astorquii, reflected as no significant difference between the mean AIc (Assignment Index correction) values for males vs. females in assignment tests. Both male AIc = 0.190 ± 0.275 and female AIc = −0.172 ± 0.234, (mean ± SE) bounded zero, and no difference between the two sexes was found   Fig. .A. astorquii, a colony-breeding cichlid fish living in Nicaragua’s largest crater lake, Lake Apoyo. The results show no spatial genetic structure within the three breeding A. astorquii colonies, contrary to what might have been predicted under site fidelity or kin-clustering. Similarly, there was little or no population differentiation among the colonies; the lake is effectively a panmictic population. Furthermore, there was no sex-bias to this lack of genetic structure. The lack of genetic differences among colonies suggests that there is no colony fidelity in A. astorquii. Either adult individuals commonly settle into different breeding colonies for different breeding seasons or, after becoming independent, juveniles disperse away from their natal colony. In other words, the seasonal aggregations of breeders may temporally alternate with more widely ranging life-history phases. In this respect, the results are similar to many generalist and pelagic cichlid species13, including A. zaliosus from Lake Apoyo38 and Amphilophus xiloaensis from nearby crater lake Xiloá59 that were not found to show any significant population structure across different sites within the lakes. However, our results are in contrast with previous studies on African cichlids that have shown that benthic  species tend to have moderate to high levels of spatial genetic structuring, including high fidelity to breeding grounds and an inhibition to crossing habitats62.The scope of this study was to assess within and between colony population genetic structure in 61, the size of the lake alone cannot explain the observed lack of population structure in these cichlids. One potential explanation for this difference could be fluctuations in the existence and distribution of Chara algae beds where all A. astorquii breeding grounds were located at the time of this study , and hence the location of breeding aggregations associated with Chara may not remain stable enough for philopatry to evolve or for any population structuring to build up detectable genetic signal. This hypothesis is also in line with our finding that there was no sex-bias to the lack of structure between or within the colonies, while earlier studies on African cichlids have suggested that dispersal in cichlids can be either male64 or female65 biased.Because more pronounced geographic structure has been found in fish at scales much smaller than the size of Lake ApoyoAmphilophus astorquii belongs to the extremely rapidly speciating lineage of cichlid fish, and it has speciated sypatrically within a few hundred generations after the formation of Lake Apoyo (approximately 23000 years ago44) within the lake46. Our results are therefore relevant also in the context of inferring the most likely modes of rapid speciation taking place within the lake. The comparison between colonies from different parts of the lake suggests that there is no biologically significant level of population structure within the species. This makes the occurrence of parapatric speciation via strong ‘homing’ unlikely30. Indeed, together with the previous studies that found no significant population structure within other Nicaraguan crater lake cichlids56, the current study provides evidence against the existence of barriers to gene flow within the lake. The lack of such barriers is in accordance with the proposition of sympatric speciation being important in the origin of these species39. Other modes of speciation, e.g. through disruptive natural selection, have been proposed to explain elevated speciation rates in taxa showing less genetic population-structure68 and are also likely to play an important role in the (sympatric) origin of cichlid species in Nicaraguan crater lakes, such as Lake Apoyo69.A. astorquii either at the colony or lake-wide level. The results are important in suggesting that strong philopatry or sex biased dispersal are unlikely to contribute markedly to the rapid speciation process the species has undergone within Lake Apoyo, providing further support for sympatric ecological speciation as the driver of the extremely rapid diversification of these cichlids in Nicaraguan crater lakes in Nicaragua.To conclude, we found no population structure in Supplementary materials


Bombus) species are showing a clear decline worldwide. Although many plausible drivers have been hypothesized, the cause(s) of this phenomenon remain debated. Here, genetic diversity in recent versus historical populations of bumblebee species was investigated by selecting four currently restricted and four currently widespread species. Specimens from five locations in Belgium were genotyped at 16 microsatellite loci, comparing historical specimens (1913–1915) with recent ones (2013–2015). Surprisingly, our results showed temporal stability of genetic diversity in the restricted species. Furthermore, both historical and recent populations of restricted species showed a significantly lower genetic diversity than found in populations of co-occurring widespread species. The difference in genetic diversity between species was thus already present before the alleged recent drivers of bumblebee decline could have acted (from the 1950’s). These results suggest that the alleged drivers are not directly linked with the genetic variation of currently declining bumblebee populations. A future sampling in the entire distribution range of these species will infer if the observed link between low genetic diversity and population distribution on the Belgium scale correlates with species decline on a global scale.Since the 1950s, bumblebee ( Ne)Ne is the size of an ideal population which has the same genetic diversity as the actual population of interestNe which is generally smaller than the corresponding census population size (Nc)Ne one can determine the viability of a population  to assess the impact of demographic changes. Over a period of 100 years, genetic diversity decreased, while genetic differentiation increased. In contrast, other studies clearly showed temporal stability of population structures1617Currently, several methods are described to estimate In bumblebee species, which are all essential pollinators in natural and managed ecosystems  with a lower level of heterozygosity showed a higher prevalence of the gut parasite Crithidia bombi. The same negatively correlated interaction between parasite prevalence and genetic diversity was found for the parasitic mite Locustacarus buchneri in B. muscorum but not in B. jonellusNosema bombiDue to the impact of these drivers, the effective population size of restricted bumblebee species may decrease. Genetic drift  may lead to a decrease of genetic variation within a population4422272054versus those currently restricted and limited to specific localities, to investigate whether a reduction in genetic diversity has occurred over time. More specifically, we compared the genetic diversity of eight bumblebee species before and after the general bee decline that started in Europe around the 1950s3537B. ruderarius, B. sylvarum, B. humilis and B. soroeensis) and four are currently widespread  in Belgium. For each location and species, samples were genotyped with microsatellite DNA markers. Here, we hypothesize that (i) widespread bumblebee species have larger effective population sizes than sympatric restricted bumblebee species; (ii) genetic diversity in the potentially smaller populations of restricted species will decrease over time by the influence of genetic drift.In this study, we compared two groups of bumblebee species, those currently widespread and found in different parts of Belgium Bombus species. Genotype replications for all loci were consistent, with a correct repetition of 99.71%. Based on our exclusion step of maximum 6 loci of missing values allowed within the genotype profile of a single specimen, 85 specimens were excluded from all further analyses for the historical data, and 11 for the recent ones . Within the recent populations of the widespread Bombus species, the genetic diversity parameters (AR and HE) were high, ranging from 3.820 to 6.590 and from 0.409 to 0.755, respectively  and for the widespread species with 0.589 versus 0.594  was the best fitting model (delta = 0.000) and had the highest weight  fitting the observed pattern of the genetic diversity variable t 0.632; . For theIC score . Althougectively  and 4.HE  and AR   , which me  in comparison with the Ne within the populations of all stable bumblebee species  comparable with the Ne observed in the populations of B. sylvarum, B. soroeensis, and B. ruderarius in Torgny.The effective size of each population, with data from two time points available, were measured with MLNe . Within  species . Furtherhortorum . B. pasculations , althougth century, thus well before the 1950s when the agricultural revolution started with a massive use of pesticides and fertilizers. Although our results clearly support the latter hypothesis, we do not rule out the possibility that a small reduction in genetic diversity has occurred during the last 100 years. A possible and small reduction in genetic diversity may be undetectable due to the lack of statistical power and the rather small sampling sizes within the experimental setup of our study. Although we admit that an increase in sample size would be preferable to strengthen the power of the results in our study, the potential reduction would be much smaller than the here observed difference between restricted and widespread species. Furthermore, we provide here the best possible sampling and setup considering the possible difficulties when using and genotyping historical old museum and collection material. Hence, a tendency towards a small decrease in genetic diversity over time can be seen for the restricted bumblebee species, B. ruderarius and B. soroeensis , Myrmica sabuleti, which experiences a decline, while the butterfly’s genetic diversity is maintained by gene flow or dispersal; iii) too low statistical power in the analysis of genetic diversity due to insufficient historical sampling, or iv) the historical populations were already genetically impoverished before the start of their study. The latter explanation turned out to be the most likely one with a lower genetic variation due to long-term isolation from nearby populations2430Our results are indicative for a temporal stability of genetic diversity within the studied populations of the restricted bumblebee species. A comparable example of temporal stability of genetic diversity is shown in Danish populations of the large blue butterfly th century, it is impossible to test this possibility, let alone the general technical difficulties in using haplodiploid species. In our study we present species belonging to different subgenera which could represent a different genetic diversity and could bias the difference observed between restricted and widespread species. Indeed, the over representing of different species of the subgenus Thoracobombus could bias the observed lower genetic diversity within the group of restricted bumblebee species. However, we speculate that this is not the case. Indeed, even within the subgenera Thoracobombus, the widespread species (B. pascuorum) had a significantly higher genetic diversity than the restricted species  and Torgny (Torgny and Lamorteau) to allow a comparable sampling area as within the other localities. Historical specimens from the RBINS collection were collected in the bumblebee foraging season 1913, 1914, and 1915  in Brussels. Five locations in Belgium were selected due to the presence of sufficient available historical bumblebee specimens of multiple species for genetic analysis, and since these five locations represent the main bio-geographical units of the country. In these five locations  the available abundance data of these species within the Belgian collection36et al.et al.et al.et al.et al.et al.One middle leg of each individual bumblebee specimen was used for DNA extraction. DNA extraction, PCR amplification with 16 microsatellite markers (four multiplexes of four loci), and visualization with capillary electrophoreses on an ABI-3730xl sequencer (Applied Biosystems), were performed with the method as described in Maebe et al.Some of the genotyped specimens were excluded prior to data analyses, after applying several validation steps following Maebe AR) estimated as the sample size-corrected private allelic richness with the program HP-Rare 1.1HE;For each population we determined the genetic diversity based on two parameters: the allelic richness  could cause bias in our analyses. For instance, bumblebee species of the subgenera Pyrobombus may have higher dispersal abilities than Thoracobombus species56To examine whether genetic diversity differed between species, and/or whether other factors such as species distribution, species subgenera, locations and/or time period had an effect on genetic diversity, we conducted LMM’s in RStudioNe of each population was performed using one multiple temporal method: a maximum-likelihood approach implemented in the program MLNeNe in natural populations under both ideal and migration scenarios .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Molecular characterization is important for efficient utilization of germplasm and development of improved varieties. In the present study, we investigated the genetic purity, relatedness and population structure of 265 maize inbred lines from the Ethiopian Institute of Agricultural Research (EIAR), the International Maize and Wheat Improvement Centre (CIMMYT) and the International Institute of Tropical Agriculture (IITA) using 220,878 single nucleotide polymorphic (SNP) markers obtained using genotyping by sequencing (GBS).Only 22% of the inbred lines were considered pure with <5% heterogeneity, while the remaining 78% of the inbred lines had a heterogeneity ranging from 5.1 to 31.5%. Pairwise genetic distances among the 265 inbred lines varied from 0.011 to 0.345, with 89% of the pairs falling between 0.301 and 0.345. Only <1% of the pairs had a genetic distance lower than 0.200, which included 14 pairs of sister lines that were nearly identical. Relative kinship analysis showed that the kinship coefficients for 59% of the pairs of lines was close to zero, which agrees with the genetic distance estimates. Principal coordinate analysis, discriminant analysis of principal components (DAPC) and the model-based population structure analysis consistently suggested the presence of three groups, which generally agreed with pedigree information (genetic background). Although not distinct enough, the SNP markers showed some level of separation between the two CIMMYT heterotic groups A and B established based on pedigree and combining ability information.The high level of heterogeneity detected in most of the inbred lines suggested the requirement for purification or further inbreeding except those deliberately maintained at early inbreeding level. The genetic distance and relative kinship analysis clearly indicated the uniqueness of most of the inbred lines in the maize germplasm available for breeders in the mid-altitude maize breeding program of Ethiopia. Results from the present study facilitate the maize breeding work in Ethiopia and germplasm exchange among breeding programs in Africa. We suggest the incorporation of high density molecular marker information in future heterotic group assignments.The online version of this article (10.1186/s12864-017-4173-9) contains supplementary material, which is available to authorized users. Zea mays L.) is the second most popular staple crop in Ethiopia after tef (Eragrostis tef) and its production in the country has doubled in less than two decades [−1, respectively (http://faostat3.fao.org), which is the second highest national average yield reported in sub Saharan Africa (SSA), only after South Africa [Maize  define core subsets of germplasm for specific traits , (ii) seSeveral marker based studies were conducted to determine the genetic diversity, relationship, population structure, and heterotic grouping of CIMMYT , 18–21 a−1. DNA quality was measured as described in our previous study [http://www.biotech.cornell.edu/brc/genomics-facility). DNA samples of the 265 inbred lines in this study plus several other inbred lines with known pedigree and genetic purity from previous studies [ApeKI as restriction enzyme and 96-plex multiplexing as described by Elshire and colleagues [A total of 265 inbred lines developed by EIAR, CIMMYT and IITA and widely used in the maize breeding program for the mid-altitude sub-humid maize agro-ecology of Ethiopia were used in this study .Imputed GBS data for a total of 955,120 SNP loci distributed across the ten maize chromosomes was received from the Institute of Biotechnology, Cornell University, USA. The genotype data was initially filtered using a minor allele frequency (MAF) of 0.05 and a minimum count of 80% of the sample size using TASSEL v.5.2.24 software , which gfind.clusers” function in R, which runs K-means with increasing values of K from 1 to 12. We compared the different groups using Bayesian Information Criterion (BIC), with the lowest BIC value used to infer the ideal number of groups (sub-populations). The first two principal components from DAPC were plotted for visual examination of the clustering pattern of the inbred lines. The model-based clustering in STRUCTURE v.2.3.4 was ran by varying the number of clusters (K) from 1 to 12, with each K repeated thrice with a burn-in period of 10,000 and 10,000 MCMC (Markov Chain Monte Carlo) replications after burn-in. Individuals with probability of membership ≥60% were assigned to the same group, while those with <60% probability memberships in any group were assigned to a “mixed” group [Population structure was estimated from 21,645 SNPs, hereafter designated as “dataset 2” obtained after further filtering the 220,878 polymorphic SNPs using a MAF of 0.10 and a physical distance of 10 kb between adjacent markers. Additional filtering was done to select evenly distributed SNPs across the ten maize chromosomes that can easily be handled by the software used for population structure analysis. Population structure was estimated using both an admixture model-based clustering method implemented in the software package STRUCTURE v.2.3.4  and the d” group .4 or later generation as a fixed inbred line for evaluation in hybrid combination. Inbred lines are considered pure or fixed when the proportion of heterozygous SNP loci does not exceed 5% [4) as parents for hybrid formation. This approach was used to attain higher seed yield in the prevailing poor inputs and agronomic practice. This in turn lowers the price of hybrid seed production thereby decreasing cost of seed and increasing access to seed by small scale farmers [4 or later generations [Among the 955,120 SNPs used for genotyping the 265 inbred lines, only 23.1%  were polymorphic in our germplasm, each with a minor allele frequency ranging from 0.05 to 0.50. The percentage of missing data per marker after imputation varied from 0 to 19.2% and the overall average was 9.2%. The number of SNPs per chromosome for dataset 1 varied from 15,324 SNPs on chromosome 10 to 35,002 SNPs on chromosome 1 . Ecuador 573, which is an OPV originally obtained from Ecuador and improved through reciprocal recurrent selection with Kitale synthetic II  is a croAs shown in Fig. Lack of clear grouping of the inbred lines in this study based on their origin  is partly attributed to EIAR’s continuous acquisition of germplasm from both CIMMYT and IITA, and also germplasm exchange among maize breeders at CIMMYT and IITA. Maize breeders from the NARS in SSA often have limited source germplasm for their breeding program and are mainly dependent on CIMMYT and /or IITA maize germplasm. CIMMYT provide free access to over 576 publicly available CMLs and many other advanced inbred lines to maize breeder’s worldwide. Adapted inbred lines received from CIMMYT and IITA are crossed to various locally developed maize inbred lines to derive new improved inbred lines and hybrids. The classification of CIMMYT/IITA inbred lines into three heterotic groups , has indirectly influenced many of the NARS breeders in SSA to adopt a similar system of heterotic grouping, which is essential for establishing a line and hybrid development pipeline.Genetic variation and population structure of maize inbred lines available for the mid-altitude sub-humid agro-ecology in the maize breeding program of Ethiopia were assessed using high density SNP markers. Results of the study demonstrated the presence of high level of heterogeneity within most of the inbred lines studied, suggesting the requirement for additional generation of inbreeding to obtain fixed inbred lines, as deemed necessary. Genetic distances among most pairs of inbred lines clearly showed the high level of variation among the most inbred lines available for the mid-altitude sub-humid maize agro-ecology of Ethiopia. Although many of the inbred lines developed by EIAR shared the genetic background of some CIMMYT’s and IITA’s popular inbred lines, a high level of genetic variation was found among most EIAR inbred lines. The different multivariate methods revealed the presence of three distinct groups that is broadly in agreement with the genetic backgrounds of the inbred lines. There was no clear pattern of grouping of the inbred lines along institutional origin , which could be due to the continuous exchange of genetic material and development of many inbred lines using the CIMMYT and IITA inbred lines as parents. Our results provide useful information about the extent of genetic variation and population structure of maize inbred lines developed and/or widely used by breeders in the mid-altitude sub-humid maize agro-ecology of Ethiopia. The result also facilitates germplasm exchange among breeding programs in SSA.Additional file 1:Summary of the 265 inbred lines and population structure. (XLSX 47 kb)Additional file 2:Genetic distances between pairs of 265 inbred lines based on 220,878 polymorphic SNPs. (XLSX 447 kb)Additional file 3:Relative kinship coefficients between pairs of 265 inbred lines based on 220,878 polymorphic SNPs. (XLSX 507 kb)Additional file 4:Heat map of the 265 inbred lines based on relative kinship matrix estimated from 220,878 polymorphic SNPs. (DOCX 211 kb)


Challenged by population increase, climatic change, and soil deterioration, crop improvement is always a priority in securing food supplies. Although the production of grain legumes is in general lower than that of cereals, the nutritional value of grain legumes make them important components of food security. Nevertheless, limited by severe genetic bottlenecks during domestication and human selection, grain legumes, like other crops, have suffered from a loss of genetic diversity which is essential for providing genetic materials for crop improvement programs. Illustrated by whole-genome-sequencing, wild relatives of crops adapted to various environments were shown to maintain high genetic diversity. In this review, we focused on nine important grain legumes  to discuss the potential uses of their wild relatives as genetic resources for crop breeding and improvement, and summarized the various genetic/genomic approaches adopted for these purposes. Wild plants have been domesticated for thousands of years since the beginning of human civilization, as a means to ensure a stable food supply. Through plant breeding activities over the centuries, crop plants have been manipulated to develop new and desirable traits . The artChallenged by the demand of the ever-increasing global population , the negWild relatives are potential genetic resources for crop improvement ,6,7, as The idea of generating exotic genetic libraries in order to accelerate plant breeding is further supported by the development of genomic and genetic tools in the post genomic era . NonetheMolecular plant breeding has been hailed as the foundation of 21st-century crop improvement and it integrates traditional plant breeding practices, molecular markers, genomic research and biotechnology . UndoubtGrain legumes refer to legumes the seeds of which are harvested for consumption. Commonly known grain legumes include soybean, peanut, pea, chickpea, common bean, lentil, cowpea, lupin and pigeonpea. The current agronomic challenge is the generally lower yield in grain legumes compared to cereals, together with the difference between their role as a diet staple for certain populations and the geographical locations where they are grown . HoweverIn this review, we will focus our discussions on grain legumes as 2016 has been declared the International Year of Pulses by the United Nations. We will include a brief history of their domestication, a description on the impacts of genome sequencing on the studies of wild relatives through the generation of genetic maps, the use of the new strategy of genotyping-by-sequencing (GBS) and the search and introgression of wild alleles in breeding programs. Finally, we will discuss our perspectives on the roles of wild relatives in the new challenges that arise in agriculture.Grain legumes were planted as companion crops of wheat and barley when agriculture began in the Near East ,18, whilGlycine max L.): Soybean is one of the oldest crops  Millsp): Populations of the wild progenitor, Cajanus cajanifolius (Haines) Maesen, have been identified in eastern Peninsular India alongside a diverse group of other Cajanus species. C. cajanifolius is rare today, probably due to habitat loss  Walp. ssp. unguiculata cv.-gr. sesquipedalis), which, interestingly, has evolved from cowpea by divergence domestication in Asia [Cowpea is a readily self-pollinating crop. Using cultivated cowpea as well as wild relatives and landraces, an important number of mapping populations have been developed during the past 30 years. Close cross-compatible relatives have been explored as genetic reservoirs and parent donors of important agronomic traits ,179,180. in Asia ,158,159.Lupinus make the scenario of interspecific gene transfer too complicated to improve specific agronomic traits [In the case of lupins, mapping studies have been done, either through traditional molecular markers or high-throughput sequencing techniques, to complement breeding programs for different species within the complex ,184,185.c traits . Some exc traits .Taking advantage of the genetic diversity of wild relatives of crop plants to improve the performance of crops is not a novel concept, but it is worth noting that, for grain legumes, it is often difficult to trace back the breeding history and the real impact on crop production. In contrast, the record-keeping on other major crops seems to be a little more comprehensive . It seemThe advent of genomics and genomics-assisted breeding have expanded our understanding of complex traits, allowing us to dissect the genetic bases of traditional and new important agronomical traits, not only to increase productivity or adapt to climate change, but also to develop alternative food production systems tailored to poor areas and small farms to grow more and better food.The use of genomics-assisted breeding using wild relatives, particularly in grain legumes, should be intensified. The skepticism of plant breeders to make use of wild and exotic plant genetic resources due to associated linkage drag is gradually overcome because the exploration of expanded gene pools is providing us with unprecedented opportunities to discover major genes controlling important traits. Once the major genes or genomic regions have been characterized, it would be worthwhile to move forward the introgression from the cultivated populations and even consider transgenesis, genome editing or the less controversial mutagenesis. Scientific developments and the knowledge generated thereof using wild relatives are revolutionizing our understanding of biological processes. However, it has been cautioned that the improvement of specific traits using wild relatives masks the true potential of genetic diversity in the wild relatives for breeding . ClearlyIn Although we have long recognized the value of wild relatives in the past, there always seem to be a repeated pattern of recognizing their potential importance following periods of calamities in food production. An example of great interests in wild crop relatives after the Second World War , and a sThe contribution of wild relatives to modern agriculture, helping to increase yield, quality and disease resistance, is significant. Joint efforts from plant breeders and collaborative institutions across the world have contributed to the examples of success. The linkage drag and reproductive barriers are a hard hurdle to overcome and the contributions made thus far to dealing with this issue should be especially recognized. However, the genetic erosion of the most important crops remains problematic, particularly when we are facing more frequent periods of flooding, drought and/or diseases with global warming, coupled with intensive agricultural systems. In this regard, the use of wild relatives may be the solution.Tanksley and McCouch  proposedPhaseolus gene pool [One major limitation in the utilization of crop wild relatives in breeding programs is due to major gaps in the genetic diversity of “gene pools”. The availability of crop wild relatives could be hampered by many factors, such as loss of natural habitats. “Gap analysis” is a tool to assess genetic conservation and to formulate conservation strategies by prioritizing among taxa containing gaps due to sampling, geographic and environmental factors. The power of this tool was demonstrated by a case study of ene pool ,196.A big international effort is underway with the aim to adapt agriculture to climate change, which includes collecting, protecting and preparing crop wild relatives. Several pulses are among the major targets: common bean, adzuki bean, chickpea, cowpea, faba bean, groundnut, lentil, lima bean, mung bean, pea, pigeonpea, soybean, urd bean and vetch ,198. The


Lutzomyia vexator over spatial scales from 0.56 to 3.79 km at a study region in northern California. The study was provoked by observations of differentiation at fine spatial scales of a lizard malaria parasite vectored by Lu. vexator.The geographic scale and degree of genetic differentiation for arthropod vectors that transmit parasites play an important role in the distribution, prevalence and coevolution of pathogens of human and wildlife significance. We determined the genetic diversity and population structure of the sand fly Lu. vexator. Alleles present at these loci were examined in four populations of Lu. vexator in Hopland, CA. Population differentiation was assessed using Fst and D , and the program Structure was used to determine the degree of subdivision present. The effective population size for the sand fly populations was also calculated.A microsatellite enrichment/next-generation sequencing protocol was used to identify variable microsatellite loci within the genome of D = 0.327; FST = 0.0185 (95 % bootstrapped CI: 0.0102–0.0264). Even though the insects are difficult to capture using standard methods, the estimated population size was thousands per local site.Eight microsatellite markers were characterized and revealed high genetic diversity  and slight but significant differentiation across the fine spatial scale examined  contains supplementary material, which is available to authorized users. Lutzomyia) are important vectors of Leishmania and other cellular and viral pathogens of medical, veterinary and wildlife significance throughout the Americas 2 − 1}. We assumed a step-wise mutation model and used a mutation rate of 10-4 [Finally, we wanted to obtain a rough estimate of the population size of these flies. Many methods have been proposed to estimate breeding population size , all with shortcomings . The gen of 10-4 .Lu. vexator, 13 Lu. stewarti and ten unidentified) across the four different sites; ‘Buck’ was the most prolific site, with 111 flies collected. As the numbers of flies collected from each of the other sites were substantially lower, we only used 62 flies from Buck for analysis. Our analysis utilized 152 Lu. vexator flies in total .Over the 24-night collection period, 213 sand flies were collected  containing a microsatellite repeat. The complete list of the contigs with repeat motif, suggested PCR primers and annealing temperatures and the FASTA files are provided in Additional files Over all sites, 152 sand flies were included in the study, with 144–152 successfully genotyped per locus. Failure to amplify a sand fly for any locus could be a result of null alleles, but only one locus (Lvx90606) showed evidence of null alleles. A large number of alleles were detected for most loci . Similarly, when data was parsed by site, only 5/112 site and locus combinations showed significant linkage disequilibrium, and three of these significant deviations were between Lvx 504 and Lvx7442 . The other two were other combinations of loci at Buck .When all populations were combined, deviation from Hardy-Weinberg equilibrium (HWE) was observed for two of the eight loci ; FST for individual pairs of sites ranged from 0.00945 to 0.0376 . While we did select for microsatellite loci that were variable, such high levels of genetic diversity are not uncommon and have been reported for other sand fly taxa [Using a panel of eight microsatellite loci, we found exceptionally high genetic diversity in the fly taxa , 38–40.FST and D) indicated genetic differentiation between sites that were approximately 0.5–3.8 km distant, and the STRUCTURE analysis provides further evidence that there are at least two genetically distinct sand fly populations at our study site. Previous studies that have documented significant levels of population differentiation for Lutzomyia sand flies did so over larger collection distances  [Lu. vexator is often found in ground squirrel burrows, which are patchily distributed across the research center, the effect of limited dispersal may be amplified in this system, leading to detectable genetic differentiation even on a small spatial scale.Both measures of genetic structure ( Brazil) , 41. The Brazil) , which c Brazil) . BecauseFST and STRUCTURE analyses) than the other sites suggests that differences in microhabitat rather than geographic distance drive some of the small-scale patterns in the genetics, as has been suggested for at least one other sand fly species (i.e. Phlebotomus papatasi) [The STRUCTURE analysis Fig.  and compapatasi) .FST analysis, and the small sample size at Foster (18 flies) may seem particularly troubling to some. Our intuition suggested that this small sample size would be more likely to lead to type II errors (i.e. being unable to detect true differentiation between populations) rather than type I , and a simple simulation using randomized data that preserved allele frequencies  and site sample sizes confirmed that the differences in sample size among sites alone does not create false patterns of population differentiation (data not shown). We therefore have reason to believe that the patterns observed in our data are real, or at least are not generated by differences in sample size among sites.A potential source of error in the data is our unbalanced sample sizes among sites. Generally, equal population samples are preferred, especially for P. mexicanum has been studied in its lizard host and some years the overall prevalence in the lizards can reach > 35 %. Laboratory studies show that > 90 % of sand flies do not survive past laying a clutch of eggs [Lu. vexator flies were collected. Likewise, other researchers have reported low collection yields for sand flies after intensive efforts  or modified Sherman traps/emergence traps to collect from tree holes/leaf litter could capture additional sand flies from these sites [The results of this study may also shed some light on the relative abundance of sand flies in this area. Over the past three decades, the lizard malaria parasite  of eggs , which wcaptured ). Using se sites , 43–45.Lu. vexator population is structured, it is not yet clear the extent to which this structure is important biologically and whether it might result in localized transmission of malaria at the field site. However, the fact that we have some structure at this spatial scale raises questions concerning the implications for this widespread species. If even slight genetic differentiation occurs on such a small scale, it is possible that Lu. vexator populations across North America are not one species and cryptic species may exist. Examining behavioral, biochemical and morphological differences in populations of Lu. vexator may reveal otherwise hidden diversity. Questions are also raised about the possibility of local adaptation, such as whether the presumed limited dispersal for Lu. vexator may allow for local adaptation between vector and parasite. Such patterns have been documented previously for other Plasmodium-host associations [P. mexicanum/Lu. vexator population pairings could determine the effects of this differentiation on the transmission biology of this parasite. The genetic structure of this disease vector can serve as a model system to understand sand fly/pathogen transmission dynamics across small and large spatial scales.The intent of our study was to examine the genetic structure of a sand fly species over a small spatial scale to determine if vector population subdivision could contribute to the patchy distribution of malaria we observe at our field sites. While our results indicate that the ciations , 47. WhiLu. vexator in Hopland, CA is structured, with at least two genetically differentiated populations existing within 0.5–3.8 km of each other. This structuring suggests reduced dispersal of sand flies across the field site and may contribute to the observed patchy distribution of malaria. Our findings emphasize that disease vectors can be differentiated over small spatial scales and further studies should be conducted to assess the impacts of such differentiation on disease transmission.We present evidence that the population of


Caragana microphylla (Leguminosae) is a dominant climax semishrub species in northern China. We evaluated genetic variation within and among populations sampled from three different environmental gradients in Horqin Sandy Land in northern China using intersimple sequence repeats markers and investigated the possible existence of relationships between genetic diversity and environmental factors. The results showed that C. microphylla have high genetic diversity, and environmental gradients affected genetic diversity of C. microphylla populations. Genetic diversity of all populations was affected by many environmental factors and as well correlated with warm index and soil Olsen phosphorus (SOP) concentration. These results have important implications for restoration and management of these degraded ecosystems in arid and semi‐arid areas. Landscand Wang, . This arnd Wang, . Over rend Wang,  and has nd Wang, .Caragana microphylla (Leguminosae) is a climax and dominant sand‐fixing shrub species native to northern China; it is an important component of vegetation rehabilitation efforts in the northern China, because it has several highly valuable ecological traits, which include high drought tolerance, antiwind erosion properties, and N2‐fixation capacity  markers. We used the canonical approach to investigate the potential association between genetic variation and environment, based on correlation analysis between a measure of genetic diversity and environmental variables ? If this is the case, what is the change trend and characteristics of genetic diversity within and among C. microphylla populations along environmental gradients across Horqin Sandy Land.Is there a difference in genetic diversity of C. microphylla and climatic factors in Horqin Sandy Land? If this is the case, whether consistent with previous findings  values were calculated from the formula,t = greater than 5°C monthly mean temperature. Cold index (CI) values were calculated from the formula,t = lower than 5°C monthly mean temperature. Hydrothermal synthesis index (S) values were calculated from the formula,t = month, rt = monthly rainfall, and Tt = monthly mean temperature  Habitat gradients: mobile sand dune (vegetation coverage <10%), fixed sand dune (vegetation coverage >50%), semifixed sand dune (vegetation coverage = 30–50%), and lowlands between sand dunes. (2) Thermodynamic gradients: low temperature region (AMT <6.0°C), middle temperature (AMT = 6.0–7.0°C), and high temperature (AMT >7.0°C). (3) Humidity gradients: low humidity (AP <300 mm), middle humidity (AP = 300–400 mm), high humidity region (AP >400 mm).At each location, a composite soil sample from three depths  was collected from nine sampling points. Soil samples were air‐dried and hand‐sieved through a 2‐mm screen to remove roots and other debris. A portion of each air‐dried soil sample was ground to pass a 0.1‐mm mesh for soil nutrient analyses Table . Soil av2.2Total DNA was extracted using an AxyPrep genomic DNA mini kit . DNA was quantified spectrophotometrically; samples that yielded high quantities of good‐quality DNA were used in consecutive experiments. After screening 100 ISSR primers from the University of British Columbia (UBC primer set no. 9) for well‐amplified and polymorphic bands among plant populations, we selected 15 primers for use with all individuals.2, 500 μmol/L of each dNTP, 20 mmol/L Tris–HCl (pH 8.3), 100 mmol/L KCl, and 0.3 μmol/L primer. Amplification conditions consisted of an initial step of 3 min at 94°C, followed by 35 cycles of 45 s at 94°C, 45 s at the appropriate annealing temperature , observed number of alleles (Na), effective number of alleles (Ne), Nei's genetic diversity (h), Shannon's diversity index (I), gene differentiation coefficient (Gst), and gene flow (Nm). Redundancy analysis (RDA) was used to determine the relative contribution of the measured environmental variables to genetic diversity indices of C. microphylla. Data were first analyzed by detrended correspondence analysis, which indicated that RDA was an appropriate approach (gradient length <3). To avoid overfitting in the regression model due to the large number of explanatory variables, the most discriminating variables were selected using the “forward selection” procedure of the program during analysis. Genetic diversity and environmental data were log (x + 1)‐transformed prior to analysis. RDA was performed using CANOCO version 4.5  or absence (0) of bands, and the data transformed into a 0/1 binary character matrix. The resulting binary data matrix was analyzed using POPGENE version 1.32  were polymorphic. The size of the amplified fragments ranged from 100 to 2,000 bp, with 19.2 fragments generated on average per primer. The greatest number of bands was generated with the primer UBC842. The least number of bands was generated from the primer UBC848. The percentage of polymorphic bands ranged from 84.21% to 100%. The highest percentage of polymorphic bands was generated with the primers UBC807, UBC827, UBC848, and UBC864. The lowest percentage of polymorphic bands was generated with the primer UBC810  ranged from 19 to 121, and P ranged from 6.60% to 42.01%. Na varied from 1.0660 to 1.4201; Ne ranged from 1.0385 to 1.3116. h and I values were 0.0233–0.1702 and 0.0352–0.2486, respectively. The highest genetic diversity indices were in population 15, and the lowest genetic diversity values were in population 4. At the species level, h was 0.3365, and I was 0.5041  and negative (AMT and CI) correlations between genetic diversity indices of C. microphylla populations and climatic factors  together explained 78.4% of the total variation in the data, with axes 1 and 2 explaining 69.3% and 9.1% of the total variation, respectively , whereas those of the remaining variables were not significant according to the Monte Carlo permutation test . Of the 78.4% total variation explained by RDA, 57.1% was explained by WI, and the rest (21.3%) by variables that were not significant according to the Monte Carlo permutation test.Genetic diversity of the 20 C. microphylla populations. RDA revealed that the six environmental factors  together explained 78.4% of the total variation in the genetic diversity indices of C. microphylla populations from Horqin Sandy Land. This result indicates that some other climate factors that were not considered in this study also contribute to the unexplained variation, and WI was the major factor that affected genetic diversity. AMOVA showed that significant genetic differences were detected between the three groups defined as WI conditions, with the variance among groups being 8.71% (p = .005)  Table .3.4C. microphylla populations from Horqin Sandy Land were influenced by the following soil factors: SOC, SAN, SOP, SOC/SAN, SOC/SOP, and SAN/SOP  , whereas those of the remaining variables were not . Of the total 88.6% variation explained by RDA, 76.1% was explained by SOP and the rest (12.5%) by variables that were not significant according to the Monte Carlo permutation test.The genetic diversities of the 20 P Figure . Correla) Figure . RDA sho) Figure . Of the C. microphylla populations. RDA revealed that the six environmental factors  together explained 88.6% of the total variation in the genetic diversity indices of C. microphylla populations from Horqin Sandy Land. This result indicates that some other soil factors that were not considered in this study also contribute to the unexplained variation. SOP was the major factor that affects genetic diversity. AMOVA showed that significant genetic differences were detected between the three groups defined as SOP conditions, with the variance among groups being 8.65% (p = .004)   had greater genetic diversity than those from mobile‐state sand dunes (mobile sand dunes and lowlands between mobile sand dunes), based on a variety of genetic diversity indices. These results showed that habitat environment change affected genetic diversity of C. microphylla populations, which is consistent with the results about Artemisia halodendron . However, there were negative associations between genetic diversity and a couple of environmental factors (AMT and CI). This result reflected that genetic diversity of C. microphylla was restrained by AMT and CI. This result is not consistent with the previously reported relationship between ISSR diversity and AMT in A. halodendron from Horqin Sandy Land  (Huang et al., CI were the major environmental factors that affected genetic diversity indices in C. microphylla on the large‐scale field, and WI was the major environmental factors on the small‐scale field. Relevant report found that SAN, SOP, and SAN/SOP contents can exert a chemical control over the evolution of species through changing an organism's rate of growth, or an adaptation to each situation of SAN/SOP (Acquisti, Elser, & Kumar, Ecological and environmental factors can play roles in shaping genetic diversity patterns (Gaggiotti et al., None declared.


The operational lifetime of filtration membranes is reduced by the clogging of pores and subsequent build-up of a fouling or cake layer. Designing membrane operations in which clogging is delayed or even mitigated completely, requires in-depth insight into its origins. Due to the complexity of the clogging process, simplified model membranes fabricated in microfluidic chips have emerged as a powerful tool to study how clogs emerge and deteriorate membrane efficiency. However, to date, these have focussed solely on dead-end filtration, while cross-flow filtration is of greater practical relevance at the industrial scale. As such, the microscopic mechanisms of clogging in crossflow geometries have remained relatively ill-explored. Here we use a microfluidic filtration model to probe the kinetics and mechanisms of clogging in crossflow. Our study exposes two findings: (i) the primary clogging rate of individual pores depends only on the trans-membrane flux, whose strong effects are explained quantitatively by extending existing models with a term for flux-controlled flow-enhanced barrier crossing, (ii) cross-membrane flow affects the pore-pore communication, leading to a transition from correlated to uncorrelated clogging of the membrane, which we explain qualitatively by deriving a dimensionless number which captures two essential regimes of clogging at the microscale. Despite its ubiquitous use, one of the major causes of membrane performance deterioration, i.e. clogging and the subsequent build-up of filter cakes or fouling layers, remains a major challenge in the successful design of membrane operations. Clogging leads to large losses in productivity, due to reductions in flux and the cleaning required to keep the membrane functioning5. Different strategies exist to keep membranes operational, such as back pulsing, back flow reversal, various cleaning cycles and ultimately complete replacement7. And while these procedures can enhance the operational efficiency to some extent, neither address the underlying mechanism of clogging8.Membrane filtration is a crucial unit operation in a very wide variety of industrial processes, ranging from milk separation10; (i) sieving, in which a single particle whose dimensions exceed the pore size is trapped at the pore entrance and immediately causes it to become blocked, (ii) arching, in which multiple particles form a configurational arch over a pore, a scenario found in the passage of dense particulate flows through constrictions and (iii) flow-induced aggregation, where multiple particles form an agglomerate by means of irreversible aggregation, either within the flow or at a pore wall, which builds gradually over time until it completely clogs a pore. In particular in the latter scenario, clogging is a complex phenomenon in which a large variety of factors play a role. Such as particle-particle and particle-wall interactions, colloidal stability, hydrodynamic interactions, the confinement ratio, particle concentration, membrane geometry, etc.13.Traditionally, three mechanisms for clogging are identified15, microfluidic micromodels in which excellent and independent control can be obtained on these parameters, have proven valuable tools to study clogging at the microscale18. For example highlighting how particle interactions19, polydispersity20 and contaminants9 play a role, the governing role of particle flux16, the effects of complex collective dynamics22, how pore geometry influences clogging rates18 and the effects of particle softness25.To disentangle this multidimensional phase space with the aim to arrive at a more predictive and generic description of aggregation-induced clogging27, in which a flux is established both across and over the membrane, these microfluidic model experiments have primarily focussed on dead-end filtration geometries28. In industry, cross-flow geometries are used as they are found to remain operational longer, but the question remains what the exact mechanisms of this performance enhancement at the microscopic scale are. Optimizing the performance of crossflow systems is typically done by studying the decay of the crossmembrane flux as a function of time31. While valuable, these measurements do not provide insight into what occurs in and on the membrane that leads to its failure.Interestingly, while most industrial processes use a crossflow filtration strategyIn this paper we study clogging in a microfluidic clogging device that mimics cross-flow filtration. We study the effect of cross-flow velocity and trans-membrane flux on clogging by quantitative microscopic imaging of our cross-flow microfluidic device. We find that the rate of clogging only depends on the trans-membrane flux, which we explain quantitatively by extending existing models with a transition-state term that introduces the hydrodynamic enhancement of particle-wall and particle-particle sticking. Moreover, we show that the cross-flow across the membrane mainly influences the cake build-up that follows after the formation of a clog. A higher cross-flow flux reduces the rate of cake build-up and thus delays the communication and clogging between neighboring pores mediated by the filter cake. This results in a transition from cooperative to uncorrelated membrane clogging; we qualitatively treat these effects by introducing a new dimensionless number describing this transition.23. An overview of the device is given in Fig. W = 2.1 mm) and consists of 30 parallel pores , each having 19 constrictions in series, with Lc = 20 μm. All constrictions within the 30 parallel pores are equal and have the same 90 degree entrance angle  through our micromodel using a pressure controlled set-up, where we can vary the pressure to change the cross-flow and trans-membrane fluxes . Initially all pores transport dispersion, until a clog forms at one of the constrictions within the pore. Following the initial clog formation, the clog acts as a sieve such that fluid passes but particles do not; as a result a filtration cake forms, first inside the pore and eventually spilling over the top of the membrane structure. As in our previous study on dead-end filtration, we accelerate the clogging process to within a realistic experimental time window by inducing a weak depletion attraction between our particles by the addition of silica nanoparticles as depletants .We flow a dilute suspension (3% wt) of polystyrene particles  and the trans-membrane flux (QT).Our experiments are run at fixed pressure as this ensures that clogging of one or more pores does not change the trans-pore flux for the remaining open pores. A pressure controlled experiment allows the total flux to decrease as the flow resistance increases due to clog formation, which ensures that the flux per open channel remains constant. We directly measure the trans and cross-membrane fluxes at all applied pressures by collecting the outcoming fluid on a analytical balance when a particle-free fluid is flowed through the device. We average these data by collecting data for at least 90 minutes to ensure a high statistical certainty on these numbers. In this way, we determine directly both the cross-flow flux  are found downstream, while the intensity decreases upstream as a filter cake builds. We threshold this raw data and subsequently use automated image analysis to detect when and where a clogging event occurs. We repeat each experiment in triplicate to ensure reasonable statistical certainty on the data discussed below.α, as a function of time after the suspension first enters the membrane. We observe that as time progresses more and more pores become clogged, with α showing a gradual increase over time .We start by calculating the fraction of channels that have clogged, α as a function of time we use the Weibull model33, which describes clogging as a cascade of individual failure processes and is commonly used to describe a wide variety of failure kinetics which obey weakest-link scaling, including those found in dead-end filtration18. The Weibull distribution is formulated as:τc is a characteristic clogging time and β is the stretch exponent, which indicates the statistical distribution of failure times for individual pores. We fit the model to our data using least-squares regression, and find good quantitative agreement at all applied flow rates  increases τc decreases dramatically . At the clogging time t = τc, the critical number of particles is reached N = N*, such that the clogging time can be approximated asFinally, we suppose that clogging requires the sequential aggregation of a critical number of particles QT.Using Eqs δ which is significantly smaller than that expected based on the geometry of our system (δ = 2−11 m), this difference could lie in the simplification made in the calculation of Pa using Eq. Pa as the transmembrane flux increases, going from almost zero at quiescent conditions (Pa ≈ 0.001) to unity at higher fluxes (Pa = 1 for QT 0.2 ml/min); indeed, we find that at rest the dispersion is colloidally stable which must imply a very low value of Pa in the absence of hydrodynamic forces. We see that the behavior of Pa perfectly mirrors that of τc, where at low flow rates sticking is highly unlikely (resulting in a long clogging time), and as the flow rate increases Pa ≈ 1 and τc decreases rapidly. Thus, the hydrodynamic enhancement of particle aggregation appears to be the dominant term in governing the rate of clogging.As can be seen from Fig. et al.17 that shows a simple reciprocal dependency on the flux. These two results are thus in apparent contradiction. Based on the work of Dressaire et al.9, it has become clear that (i) commercial dispersions often used in these experiments contain a very small (ppm) fraction of large contaminants which (ii) lead to clogging by sieving which dominates the clogging behavior if no precautions are taken to remove them prior to the dispersion arriving at the microfluidic model. Indeed, if large impurities clog a pore singularly and this determines the clogging rate, a simple reciprocal dependence on flux must be found. In both of these previous works, carboxylated polystyrene particles are used, prepared by emulsion polymerisation, in which it is known that small amounts (1 in 1 million)9 of much larger particles are inevitably present. In this case, clogging occurs through a completely different mechanism. This could thus be the reason of the apparent contradiction remarked upon above. By contrast to these previous studies, we have introduced a micropillar filter at the entrance of our device, with the specific goal of removing such large impurities, thus enabling us to probe clogging by aggregation of the primary small particles under study. This hypothesis is further substantiated by the large difference in clogging times, despite working at similar volume fractions and pore-particle size ratios; e.g. in the work of Dressaire and Wyss, typical clogging times are in the range of 0.1–100 seconds17, while in our case for repulsive suspensions clogging takes one or two orders-of-magnitude longer, with clogging times ranging from half an hour to several hours18.Interestingly, our experimental data of clogging time versus flux cannot be described by considering only the passage of a critical number of particles, but requires taking hydrodynamically-enhanced barrier hopping into account, in contrast to the previously published data of Wyss 18, it becomes clear that application of a cross-flow flux does not impact the primary clogging process in these experiments. In our case, primary clogging, which is the singular event in which the flow of particles through the pores becomes blocked, occurs within the membrane, where the eroding effects of the cross-flow are negligible. However, while cross-flow does not alter the primary clogging rate of individual pores, it does effect the rate of failure of the membrane as a whole.Based on our experimental results, the transition-state model and comparison to previous results for dead-end filtration with similar particle dispersionsn individual pores. However, the growth of the filter cake on top of the membrane as the result of a single pore clogging, may affect the clogging rate of its neighbors. In such a scenario, clogging may become strongly cooperative in which the clogging of a single pore enhances, through the filter cake, the clogging of neighboring pores and thus the failure of the membrane as a whole.To date, the clogging of membrane micromodels has been described as the uncorrelated clogging of We first investigate how the cross-flow induced shear forces acting on a growing filter cake effect its thickness. We visually notice a significant reduction in the cake layer build-up when the cross-flow velocity is increased see Fig. . From th27. Secondly, if the filter cake mediates the communication between pores and thus the cooperativity of the failure of the membrane, cross-flow could change the mode of membrane failure.The differences in cake build-up between dead-end Fig.  and crosx away shows no preference for neighboring clogs. By contrast, if the clogging is correlated and cooperative, the probability of finding the next clogging event should be largest for the shortest distance Δx and decay rapidly after that.To explore the extent of pore-pore communication we ask the question how the clogging of a given pore affects the clogging rate of its neighbors. To answer this question, we determine the distance, counted in number of pores, between to consecutive clogging events. If the clogging is uncorrelated, the distance between two clogging events should show a flat distribution, where the probability of finding the next clogging event some distance Δx, based on how often this possibility can occur in our micromodel. This is essentially a weighting for the configurational entropy of the clogging process. For example, while almost every pore (except the most left and most right one) will have two pores at Δx = 1, only 2 out of the 30 pores will have a possible Δx = 29.For a fair evaluation of the distance-dependence of the next-clogging probability, we must account for the relative occurrence of possible pore distances by calculating a weighted probability function, where we assign weights to each value for Δ18 clogging is strongly correlated, with the probability peaking at Δx = 1, mediated by overhanging filter cake18. Interesting, we find that two changes in the set-up both reduce the correlations and lead to uncorrelated membrane failure: (i) increasing the primary clogging rate by making the particles weakly attractive . The characteristic time τfc it takes a growing filter cake to reach the neighboring pore, in a simplified picture, is a balance between growth rate l is the average depth within the pore at which a clog occurs, which itself is governed by a Poisson distribution for a device with many identical constrictions in series. The rate of cake growth depends on the flux through the pore QT/n, the volume fraction of particles in the incoming stream ϕ and the maximum close-packing volume fraction ϕc as:Ap is the cross-sectional area of the pore. The rate of cake erosion Q‖ and details of the particle-particle interactions in the cake, following a similar transition-state argument as derived above. For the purposes here of arriving at a qualitative understanding of the transition from cooperative to uncorrelated clogging, we simply assume that the erosion rate is a positive function of the cross-flow velocity The typical time it takes a single pore to clog is prescribed by Eq. Cl as the ratio of the primary clogging time and the cake build-up time as:Cl ≫ 1 to uncorrelated failure of the membrane by a independent sequence of primary clogging events when Cl ≪ 1.This allows us to define a dimensionless clogging number N*, l and Ap) and the particle stability (Pa).With the above we can define the clogging number for crossflow filtration as:Pa, by introducing attractions -k30, 0.64 g dioctyl sulfosuccinate sodium salt (AOT) and 0.170 g 2,2-azobis(2-methylpropionitrile) (AIBN). We mix and purge the solution with nitrogen for 20 minutes to remove all oxygen and leave the flask under vacuum to perform the reaction. The reaction is left to proceed overnight at 70 °C. The particles are cleaned by repeated centrifugation and resuspension cycles in which we slowly exchange from the alcoholic reaction solvent to the aqueous solvent mixture in our experiments.We use a 3 wt% suspension of monodisperse polystyrene particles, prepared by dispersion polymerization18. We introduce these attractive forces using depletion interaction, via the addition of silica nanoparticles (Ludox TM-40)37, and estimate the resulting attraction strength as38:U is the attraction strength in units of the thermal energy kBT, a and as are the radii of the larger polystyrene particles and smaller Ludox particles respectively (as ~ 7 nm) and ϕs is the volume fraction of the Ludox particles. For our experiments we choose an attraction strength U = 2 kBT, in between those used previously18. Prior to each experiment, the particle suspension is sonicated to make sure no particle aggregation occurs at rest.We induce weak attractive forces between the polystyrene particles, as this reduces the total time required for clogging and thus increases the efficiency of the experimental procedure, while maintaining the overall clogging behavior; these effects for the identical system have been explained in detail previously39. The pattern is replica-templated in Sylgard 184 silicone rubber at a mixing ratio of base to catalyst of 10:1. We cure devices at 65 ° for at least 1.5 hours, and subsequently bond them onto glass slides using a oxygen-plasma treatment. In all of our experiments, the devices have a height of 40 μm. We use pressures of 28, 45, 63, 98, 133 and 168 mbar, controlled with an accuracy of 0.1 mbar, applied with an Elveflow OB1-MK3 pressure controller, resulting in transmembrane fluxes of 0.13, 0.23, 0.12, 0.15, 0.20 and 0.28 ml/min, respectively. The experiments are run at constant pressure such that the flux per pore remains constant, irrespective of the clogging of its neighbors40. One reference experiment was performed in dead-end geometry with the same device, at the highest inlet pressure of 168 mbar, while keeping the outlet orthogonal to the membrane closed. This results in a transmembrane flux of 0.67 ml/min.We fabricate the microfluidic devices using standard soft litography methodsRef ≤ 3, thus ensuring laminar flow conditions. The particle Reynolds number is computed to be Rep ≤ 10−3 ensuring no inertial effects that may contribute to complexity in the clogging phenomena. The particle Peclet number is Pe~104 indicating a flow dominated by advection. The microfluidic experiments are imaged using standard brightfield transmission microscopy (Zeiss Axiovert 200) equipped with a ThorLabs USB camera, at an acquisition rate of 1 Hz. The raw image time sequences are analyzed using a set of custom image processing routines as described previously18.We calculate the upper bound of the fluid Reynolds number to be


Mental imagery has a powerful impact on emotion and cognitive processing in adults, and is implicated in emotional disorders. Research suggests the perspective adopted in mental imagery modulates its emotional impact. However, little is known about the impact of mental imagery in adolescence, despite adolescence being the key time for the onset of emotional dysfunction. We administered computerised positive versus mixed valence picture-word mental imagery training to male adolescent participants  across separate field and observer perspective sessions. Positive mood increased more following positive than mixed imagery; pleasantness ratings of ambiguous pictures increased following positive versus mixed imagery generated from field but not observer perspective; negative interpretation bias on a novel scrambled sentences task was smaller following positive than mixed imagery particularly when imagery was generated from field perspective. These findings suggest positive mental imagery generation alters mood and cognition in male adolescents, with the latter moderated by imagery perspective. Identifying key components of such training, such as imagery perspective, extends understanding of the relationship between mental imagery, mood, and cognition in adolescence.The online version of this article (doi:10.1007/s10608-016-9795-8) contains supplementary material, which is available to authorized users. Adolescence is a major period of life for the onset of mental health problems  gives rise to congruent changes in positive, depressed or anxious mood  incorporating a mental imagery element  perspective or from an observer (third person) perspective. There is evidence for individual differences in preferred imagery perspective  were recruited via letters sent home from school  or advertisements in the local community (N = 15). An information sheet given to parents specified that individuals with “a history of mood or anxiety problems” were not eligible to take part. The study was approved by the University of Oxford Central University Research Ethics Committee and was carried out in accordance with the provisions of the World Medical Association Declaration of Helsinki. The current sample size of 60 was determined based on Pictet et al.  which usParticipants were randomly assigned to receive two sessions of either positive or mixed imagery training. Each participant completed both field and observer perspective imagery sessions on separate days, with perspective order counterbalanced within valence groups.In each session, participants first completed baseline measures of mood and one measure of interpretation bias, followed by mental imagery instructions and the picture-word imagery generation task. After training, participants repeated mood and interpretation bias measures and completed two further measures of interpretation bias. See Figure S1  for a timeline of the experimental sessions.Positive and negative affective state was assessed using emotion words from the Positive and Negative Affect Schedule  to 9 (“extremely pleasant”). For analyses of pleasantness data, the dependent variable was t1–t2 standardised residuals representing change in mean ratings from t1 to t2 correcting for any differences at t1 . The stimuli comprised pictures selected from previous studies  or a negative emotional resolution  to 5 (“extremely vivid”). After every practice trial, and after every five training trials, participants were also prompted to rate the extent to which the perspective of their mental image corresponded to field or observer instructions, on a scale from 1  to 5 (“extremely involved/detached”), and to talk to the experimenter, to check task compliance. Here, the experimenter prompted participants to describe (if they could) an image from the most recent block, and gave positive verbal feedback for descriptions which (a) made use of both the picture and caption, (b) were consistent with field or observer perspective imagery  was administered to measure interpretation bias . Participants unscrambled a list of 20 scrambled sentences, using five out of the six displayed words, within a time limit of 4 min. This was completed under cognitive load (holding in mind a 4-digit number). Each sentence  could be unscrambled to form a positive resolution  or a negative resolution (“people will notice my mistakes”). A negativity score was generated by calculating the proportion of sentences completed correctly with negative valence; this was used for descriptive and inferential statistics. Two versions of the SST, each containing different stimuli, were used in the current study (i.e. for field and observer perspective imagery sessions), with SST version order counterbalanced across valence and perspective conditions. The SST provides a pen-and-paper measure of interpretation bias.r = .350, p = .004; Liebowitz r = .414, p ≤ .001; Spielberger We adapted the task from adult SST versions  was presented at the top of the screen along with four sequentially-presented recognition statements. Two sentences comprised “targets” depicting a positive (“The girls start to whisper because they think you look good with your new haircut”) or a negative (“The girls start to whisper because they think your new haircut looks terrible”) interpretation. Two sentences comprised “foils”, i.e., valenced statements that included information not explicitly given in the ambiguous situations. Participants were instructed to rate the similarity of each recognition statement to the material presented during the test phase, on a scale from 1 (very different in meaning) to 4 (very similar in meaning). As in previous studies, foils were included to investigate whether training effects specifically altered interpretational style (that were contained in targets) or whether training facilitated general response bias to valenced material . Two versions of the CRT were administered in the current study , with version order counterbalanced across valence and perspective conditions. Inferential statistics were conducted investigating effects of the within-subjects factors recognition statement valence  and recognition statement type  on similarity ratings.A computerised recognition test (CRT) based on Mathews and Mackintosh  and adapThe social anxiety scale for adolescents . For each testing site, we created a randomisation scheme using the random function in Matlab such that roughly equal numbers of participants from each site were assigned to each valence group. Within groups, participants were pseudo-randomly assigned to complete field or observer perspective imagery in session 1 and the converse in session 2 such that perspective order was counterbalanced within imagery training group. This was implemented by manually constructing a counterbalancing scheme prior to testing. Sessions 1 and 2 were completed mean (SD) 4.02 (3.00) days apart (range 1–14 days).At the start of session 1, participants completed the SAS-A . Then, for each session, participants gave baseline ratings of current positive and negative mood [t1 PANAS] and Pleasantness ratings of picture stimuli [t1 Pleasantness]. Participants then completed the standardised imagery instruction and practice procedure, followed by the computerised imagery training task in which either positive or mixed field or observer perspective imagery was generated depending on group and condition order assignment. Subsequently, participants repeated the PANAS [t2 PANAS], completed the music filler task, completed a final PANAS [t3 PANAS] and Pleasantness ratings task [t2 Pleasantness], followed by two further measures of interpretation bias, the SST and CRT .t tests to identify any differences across groups in SAS-A. Subsequently, data analysis focused on establishing the effects of positive versus mixed and field versus observer imagery generation on mood (PANAS) and cognitive bias measures  using ANOVA. SAS-A was included as a covariate in analyses of SST and CRT data to control for variance due to pre-existing social anxiety on these two measures which were administered at one time-point (post-training) only. ANOVA was conducted on vividness ratings elicited during the imagery generation task to identify any differences between valence and perspective conditions that might contribute to interpreting observed effects. The analogous analysis could not be conducted for perspective ratings as an error in the task code meant perspective ratings were not saved for the observer condition. The following data were missing due to interruption of the testing session or equipment failure: 12 PANAS measurements, 6 Pleasantness ratings, 3 SST negativity scores, 15 CRT bias scores and 11 vividness scores (see Table Randomisation checks were conducted using independent samples t(58) = 1.02, p = .312].Comparisons of characteristics of participants assigned to each imagery valence group revealed no significant differences at the start of session 1 in SAS-A , and no interaction between Valence and Perspective . For PANAS positive raw mood scores at each time-point, see Table Residualised PANAS positive mood change scores from t1 to t2 were analysed using ANOVA with between-subjects factor Valence  and within-subjects factor Perspective . As predicted, there was a significant effect of Valence  or Perspective , and no interaction .Residualised PANAS negative mood change scores were analysed in a similar manner. There was no main effect of Valence , Perspective  or Novelty .Residualised pleasantness rating change scores were analysed in a similar manner. As predicted, the interaction between Valence and Perspective was significant  but not Observer . Interactions between Valence and Novelty, Perspective and Novelty, and between Valence, Perspective and Novelty were non-significant .Paired samples t3 PANAS positive and t3 PANAS negative scores were analysed in two separate ANOVAs with between-subjects factor Valence and within-subjects factor Perspective. There were no significant effects in either analysis , indicating that Valence groups and Perspective conditions were comparable on mood prior to administration of cognitive bias measures.To check whether mood was comparable across valence groups and perspective conditions prior to administration of cognitive bias measures, F = 2.83, p1-tailed = .049, ηp2 = .049] and predicted interaction between Valence and Perspective .t tests comparing SST negativity across Valence groups for each of the two Perspective conditions showed greater negativity scores in Mixed compared to Positive Valence condition for Field  but not Observer   = .772, p = .383] or SAS-A total score  and no interaction between Perspective and SAS-A total score .SST negativity scores were analysed in an ANCOVA with between-subjects factor Valence, within-subjects factor Perspective and covariate SAS-A total score. We found the predicted main effect of Valence [F,55 = 2.8p > .1; see Table Rated similarity for negative and positive targets and foils on the CRT were analysed using ANCOVA with between-subjects factor Valence, within-subjects factors Perspective, recognition statement valence and recognition statement type, and covariate SAS-A total score. There were no significant effects , no effect of Valence  and no interaction between Valence and Perspective , and highlight the moderating effects of imagery perspective.Participants in the positive imagery condition showed a greater increase in positive mood than did participants in the mixed imagery condition, in line with findings from previous studies in adults . Whereas stimuli in the pleasantness rating task were similar in format to stimuli used in the imagery training task, SST stimuli were quite different in format. As such, this converging result from the SST supports the notion that the imagery training procedure effects transfer to tasks with a different format than the initial training.While several studies in adults have shown the impact of imagery training on the SST , since a filler task equalised mood prior to administration of measures of interpretation bias. The potentially beneficial effects of positively valenced mental imagery on two measures of interpretation bias occurred when imagery was generated from a field (first person) perspective, and did not occur when mental imagery was generated from an observer (third person) perspective. These findings highlight the impact of positive mental imagery generation on mood and cognition in male adolescents. They also suggest that explicitly instructing male adolescent participants in how to adopt a field perspective in mental imagery may enhance the impact of positive imagery training on interpretation bias. Future research should extend these findings to female adolescents and clinical groups, as the current findings are not generalizable to these populations.Lifetime mental health problems commonly have their inception during adolescence. As such, early intervention is crucial. Understanding the cognitive factors that cause, maintain, or indeed protect against psychopathology is a promising avenue for research. Findings from the current study have implications for understanding cognitive mechanisms and moderators underlying the relationship between mental imagery, mood and cognitive processing in adolescents Below is the link to the electronic supplementary material.