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Merge the pair of clusters, having a maximum characteristic similarity (DC) by reducing the cluster count.
Clusters are then merged iteratively, selecting at each step the pair of clusters that are closest to each other.
It merges the pair of clusters by maximizing modularity Q using Algorithm 4. So for each formed clusters it splits community and then updates corresponding Q.
Reduction stages are as follows: 1. Merge the pair of clusters, having a maximum characteristic similarity (DC) by reducing the cluster count.
Step 3. Find the pair of clusters with the smallest distance.
Therefore, the pair of clusters on a given αβ subunit has 9 (3 × 3) different possible configurations during oxidation.
Similar(52)
At each of n-1 steps, the procedure merges the pair of cluster with minimum E-distance.
This and the analysis with all 19 clusters suggest that many of the pairs of clusters have similar acoustic contents and are thus indistinguishable in terms of classification analysis.
The Mahalanobis distances (D m 1 is the Mahalanobis distance from cluster centre 1 and D m 2 is the Mahalanobis distance from cluster centre 2) that go through the midpoint (P) of the line connecting the two mean points (C 1 and C 2) of the pairs of clusters were then calculated.
We photoconverted Cse4-tdEos in a metaphase cell to reveal the pair of centromere clusters.
Around 10%% of rearrangement junctions were discarded because there was a plausible normal alignment of the pair of read clusters (90%% identity between mapped position and expected normal location, over at least 100 bp).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com