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The background for most techniques is fairly constant, but this is not the case for analysis by gamma ray spectrometry.
In most cases and for most techniques (spectrophotometric assays, ELISA), this approach satisfies the basic principles of linearity and specificity.
The quality of these reagents in chromatin biology applications is of particular concern given their position as essential tools for most techniques characterizing the cellular abundance and genomic distribution of histone post-translational modifications (PTMs).
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Most techniques for measuring metabolic reactions in cells require disrupting the cell.
Most techniques for feature mapping have focused on obtaining negative feature models only.
On the other hand, for the positive points, most techniques explicitly specify and monitor dependencies between components.
Most techniques for the measurement of blood volume produce a relative value which has no units.
Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels.
Most techniques used for colour contrast enhancement are similar to those for grayscale images [28, 29].
The link condition between PU and SU is an important parameter for most of techniques in CR networks.
For example, production environments are dynamic and uncertain (e.g. job arrivals, job cancellations, machine breakdowns), which cause computational difficulties for most optimisation techniques.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com