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These data are then transformed, using a transformation proposed by Bollen and Stine (1993), so that the variance covariance matrix and means are exactly as specified under the model H0.

Each of these sorted lists are then transformed using the agreed keyed hash algorithm and key KAB.

The outputs of the RBF network are then transformed using the softmax function, giving a vector prognosis indicator for each patient P = exp ⁡ (y ) ∑ j exp ⁡ (y j ).

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Each left-out landmark on in vivo CT was then transformed using the corresponding transformation matrix.

The data were then transformed using arcsine square root transformation before ANOVA.

The data were then transformed using the variance stabilization transformation algorithm (VST) and normalized using the quantile normalization method.

The data were then transformed using discrete Fourier transform (DFT) with a rectangular window to obtain the DFT coefficients.

Phase images were then transformed using the composed transforms modified to eliminate intensity scaling (which was relevant only for the magnitude images), and a mean phase image in atlas space was computed.

This matrix is then transformed using "inverse document frequency" (part c), from which the similarities between pairs of users are computed (part d) and from which a local access network associated with a subject s3 is built(part e).

Raw expression data were normalized to a target intensity of 500 to account for differences in global chip intensity, and expression values were then transformed using the logarithm base 2. Probe sets with very low average expression were eliminated because their expression measurements were not reliable.

The spheroplasts were then transformed using pMS104D:: tat, :: rev or :: nef1 and methods described below.

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