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Previously reported methods using linear ion traps with low mass resolution have been shown to be useful for the detection of fructans with a DP up to 49.
Moreover, the new method yields integer results whereas the analytical methods using linear programming usually produce impractical non-integer results.
The above results on the relation between muscle phospholipid fatty acid composition and MRS were further corroborated by phylogenetic GLS regression models (see Material and Methods, using linear terms for both body weight and phospholipid components).
Decoding methods using linear models of the relationship between neural activity and limb movements, such as the Wiener filter and Kalman filter, are most commonly used in experimental research on BMIs.
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These methods use linear fragments and search through combinations of conditions for expression of activity, and were used to extract common substructures in dopamine agonists [13].
Genome visualisation methods use linear or circular representations.
Most of these methods used linear correlation coefficients to measure the relationship between miRNAs and their targets.
However, these previous methods used linear ion traps, which have a limited mass range and low resolution and so are not suitable for the monitoring of larger fructans with DP > 50.
Amongst all methods used, linear SVM, radial SVM and LR have the highest sensitivity in identifying recipients with DGF (83.8, 88.8 and 85.5 %, respectively), at the expense of identifying recipients without DGF (72.0, 57.9 and 65.0 %, respectively).
The first limitation is that these methods use linear scoring functions to guide the sequence template alignment (Hildebrand et al., 2009; Jones, 1999; Shi et al., 2001; Wu and Zhang, 2008; Xu et al., 2003; Zhou and Zhou, 2005).
A finite-difference (FD) method is used to investigate the method using linear and fan-shaped receiver spreads.
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