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We also used resistance surfaces to understand how various landscape features affect gene flow between populations by using an approach that is rooted in circuit theory and implemented in the program CIRCUITSCAPE [ 69].
Our study included ten populations in one geographical area (at most 50 km distance between populations) and we characterised the social type of colonies and inferred the gene flow between populations by using genetic markers (nuclear and mitochondrial), and screened the occurrence of several endosymbionts.
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The Samova analysis [40] allows to determine genetic boundaries between n populations by using both their geographic location and their allelic frequencies at a given locus.
Quantifying the similarities between bacterial populations by using Fast UniFrac analysis indicated that bacterial profiles were apparently divisible into 6 oropharyngeal types in the tested subjects.
Once allometry was filtered out, shape variation was also analyzed between sexes and populations by using the same statistical test.
Furthermore, principal coordinate analysis (PCoA) was also employed to examine the genetic relationships between the detected populations by using GenAlEx version 6.4.1 (Peakall and Smouse 2006).
We evaluated mean differences in study variables between Hq and wild-type populations by using appropriate t-tests (SigmaStat software, Richmond, CA) to compute the means, standard deviations, and p values as indicated.
Pairwise comparisons were made between the three epithelial cell populations by using empiric Bayes-moderated t statistics [ 19] and array quality weights [ 23].
After adjusting for confounding via matching, we explored empirical differences between the diabetes and control populations by using matching weights to compute average exposure effects (AEE – see Additional file 1: Appendix A) [ 24, 25].
Analysis of molecular variance (AMOVA) was conducted to calculate the partitioning of genetic variation between and within the two populations by using GenALEx version 6.4.1 (Peakall and Smouse 2006).
To test for geographical genetic structure, analyses of molecular variance (AMOVA) with 10000 permutations were assessed according to the degree of differentiation between regions (ΦCT), between populations within regions (ΦSC) and between all populations (ΦST) by using ARLEQUIN 3.1 [ 44].
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