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The table below shows the expected performance of the platform with respect to the approximate number of metabolites in each class you might expect to measure from a variety of biological matrices using this platform.
Comparison of these matrices using this background model showed that the matrix of 12 columns yielded the highest NWD values (Fig. 3), indicating that this PSSM was the best one to discriminate putative promoter sequences from the noncoding genomic background.
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Water insoluble polymers are used as encapsulation matrix using this technique.
We use the following simplified adjacency matrix: Using this initial adjacency matrix, (mathbf{W}_0), we can depict a network with 16 vertices.
The trace distance between the process matrix using this implementation for and the ideal Toffoli gate is shown as the lower curve in Figure 4.
Since our variance-covariance matrices (indeed correlation matrices) have all diagonal elements equal to 1, we can incorporate λ into the matrix using this simple calculation: (8) Σ λ = λ Σ + (1 − λ ) I where I is the identity matrix.
Then, we optimize all relay matrices using the receiver matrices in this iteration and the source matrices from the previous iteration.
They developed a three-dimensional traction-force microscope, which could measure the interactions between cells and this matrix, using the same collagen-derived material.
We performed PCA on this matrix using the princomp function of Matlab.
We performed hierarchical clustering on this distance matrix using the 'ward' method in hclust R function.
We applied MDS on this distance matrix using the cmdscale command in R (version 2.15.1; R Development Core Team 2011).
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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