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The clustering transformation prepares the algorithm for the scheduling process by grouping vertices according to criteria such as strong connectivity or strong data dependency between vertices.
Where meta-analysis was not possible, synthesis took a narrative approach using techniques described by Popay and colleagues, namely: tabulation, textual descriptions, grouping and clustering, transformation of data to construct a common rubric, vote counting, and translation of data through thematic and content analysis [ 34- 36].
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Table 5 The comparison of the performance of the four clustering algorithms over different matrix transformation methods by various indices (Dunn, Davies Bouldin, eta-squared and omega-squared) and correlations between MSt and MSb of clusters Transformation methods Clustering algo.
Beyond facilitating the cluster transformation, the scaffold allosterically alters the cluster environment.
The DNA scaffold facilitates the cluster transformation by hybridizing with and concentrating two violet cluster conjugates.
The scaffold for the cluster transformation shares several characteristics with DNA templates for organic reactions.
We studied DNA reorganization that accompanies analyte recognition and the concerted cluster transformation.
This scaffold expands the scope of DNA ligands for silver clusters because hybridization directs the cluster transformation.
This DNA-directed cluster transformation was further interrogated by identifying the scaffold constituents that support the near-infrared cluster.
We propose that these correlated changes in DNA structure and silver stoichiometry underlie the violet to near-infrared cluster transformation.
Hybridization recovers the near-infrared cluster that is favored by the S1 sequence alone, and this cluster transformation optically signals the target oligonucleotide.
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