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Finally, the last step is responsible for the merge of core clusters and it is performed with a single MapReduce algorithm.
After including the outgroup 560 (13%) of 4,460 were core clusters and of these 363 were single copy.
The clusters C in ℬ with (C) > 0 are then partitioned into core clusters and ambiguous clusters (Lines 13-17).
Using this multiset a collection of 15 non-trivial clusters is derived from the input trees, of which 12 are core clusters and 3 are ambiguous clusters.
However, dispensable clusters and strain-specific clusters are still rich in transcription, replication, recombination and repair and cell wall/membrane/envelope biogenesis and cell motility, while the clusters' numbers of translation, ribosomal structure and biogenesis decrease sharply as compared to the core clusters and whole clusters.
Using this multiset and the default value of ⌈l/2⌉ = 3 for the threshold t we obtain the 4 non-trivial core clusters and the non-trivial ambiguous clusters { O, S} (two copies) and { I, O, S} (one copy) to build a consensus tree.
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Using these instances, Amazon launched a 26,496 core cluster and evaluated it against the recent Top500 scores for supercomputers.
Note that if every element in ℳ has multiplicity 1 then every cluster is a core cluster and our algorithm works precisely like the greedy consensus method for phylogenetic trees [ 18].
Unlike the widely used butterfly-like all-reduce algorithm that incurs network contention in SMP/multi-core clusters, the proposed algorithm can achieve contention-free communication in almost all contemporary clusters, including SMP/multi-core clusters and Ethernet switched clusters with multiple switches.
The top 25 genes that were differentially expressed in the sarcomatoid SCK cells compared to the three adenocarcinomatous CC lines are in Additional file 6. Clustering data within groups revealed that the core clusters I and II were associated with transdifferentiation. Genes in cluster I appeared to be downregulated in the SCK cells, compared to ordinary CC cells.
Furthermore, a hierarchical parallelization scheme is designed to map the MD algorithm to Godson-T many-core cluster and a simple performance model is derived, which suggests that the optimization scheme is likely to scale well toward exascale.
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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