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Digitized landmarks were analyzed for components of shape variation in a thin-plate spline analysis using tpsRelW (Rohlf 2008).
In addition to analysis of the overall cranial shape using 41 cranial landmarks (supplementary material Fig. S1a; Table S1), subsets of landmarks representing the facial skeleton (24 landmarks), cranial vault (7 landmarks) and basicranium (10 landmarks) were analyzed separately (supplementary material Table S1).
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The subset of 20 μg/g SAG-treated euploid mice that were distinct in the previous analysis of all cranial landmarks, are also distinct when the facial landmarks are analyzed separately (Fig. 4A).
We performed a true 3D analysis because the location of each landmark was analyzed relative to the others.
These landmarks also were analyzed pathologically to identify all 3 landmarks.
The landmark coordinates were analyzed using principal component analysis (PCA) (Miriam 2004) and canonical variate analysis (CVA) to identify shape trends in the various subgroups (Bilfeld et al. 2013).
The landmark coordinates were analyzed using principal component analysis (PCA) (M Z 2004) and canonical variate analysis (CVA) to identify shape trends in the various subgroups (Bilfeld et al. 2013).
Three configurations of landmark data were analyzed.
Three-dimensional landmark data were analyzed using the software MorphoJ [ 73].
DOI: http://dx.doi.org/10.7554/eLife.04147.009 Due to the lack of morphological landmarks in the LH, functional data were analyzed using the pattern recognition algorithm Non-Negative Matrix Factorization (NNMF) (Lee and Seung, 1999), which automatically extracts spatial areas possessing a common distinct time-course, further termed LH odor response domains (ORDs).
Both landmark genes and the whole genome were analyzed separately.
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