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For statistical analyses of the whole metabolic profile, spectra were data reduced in numerical format by integrating spectral regions (buckets) every 0.02 ppm (parts per million) and scaled to the total intensity of the spectrum with Amix 3.6.8 software (Bruker Analytische Messtechnik, Germany) from 0.8 to 8.6 ppm; the water peak area was excluded from each spectrum (4.4 to 5.2 ppm).
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Log-transformation of the data reduced the CV in all simulations.
As with the results from Table 3, log-transforming the data reduced the CV in all simulations.
A 10-fold increase in data reduced the median underestimation of branch lengths of 1.4 substitutions/site from 30%to15%5%.
All data were collected at 100 K. Data were reduced in Denzo and scaled using Scalepack.
However, the GSH/GSSG ratio as determined from LC-MS data was reduced, in contrast to the NMR data.
The steady-state transport data were reduced in terms of an analytical model for simultaneous reaction and diffusion which permitted the direct determination of the diffusional and enzyme kinetic parameters.
The big data is reduced in such a way that high dimensions of input space do not affect the cardinalities of corsets.
This provided five times more mapping data and reduced in part the sequence length-dependency of the prediction accuracy.
Although the numbers of patients with complete data were reduced, in both models A1C at the first antenatal visit remained a significant independent predictor of pre-eclampsia.
Most parent questionnaires in the reported studies lacked adequate validity and reliability data, reducing confidence in these results.
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