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The rigid transformation for optimal alignment of the target CT to the reference CT dataset is determined.
Figure 2 shows the optimal alignment of the amino acid sequences of the TcTrx with five selected Trx sequences from other sources.
Figure 1 shows the optimal alignment of the amino acid sequences of the ClPDI with 3 selected ClPDI sequences from other sources.
Figure 1 shows the optimal alignment of the amino acid sequences of the TcAAD with 6 selected AAD sequences from other sources.
In these cases, especially those from a proximal distal bone transport, the circular fixator gives better results as the disconnection of the two most distal rings allows reduction and optimal alignment of the two docking ends.
The optimal alignment of the two networks is shown in Figure 2b.
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The Smith Waterman algorithm is a method for precisely characterizing how well two sequences can be aligned and for determining the optimal alignment of those two sequences.
The optimal alignment of these sequences can be found by calculating an (n + 1) × (n + 1) dynamic programming (DP) matrix containing all possible pair-wise matching scores of residue symbols in the sequences.
We then performed optimal alignments of the results from the 10 independent runs with K = 2 for each dataset and calculated average membership coefficients using the full-search algorithm of CLUMPP version 1.1.1 [ 54].
A local algorithm allows finding the optimal alignment of two fragments of the studied sequences, whereby the regions before and after the fragments forming the alignment with the maximum weight are not taken into account when the weight is calculated.
When using this pair-HMM for predicting the optimal alignment of a sequence pair with the largest probability, this ambiguity may lead to performance degradation as these potential state sequences compete against each other.
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