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Early approaches made use of thin-layer chromatography (TLC) for purification and separation, and immunoassay and lectin-binding techniques for quantitative and qualitative analysis of specific glycan motifs19,20.
Both approaches made use of similar allometric equations [9, 24] (see 20 for more on this) relating the field measurements to estimates of AGB, but differ in the way those field data are associated with the GIS data layers (CA approach) or the satellite observations (DR approach).
Both approaches made use of similar allometric equations [ 9, 24] (see 20 for more on this) relating the field measurements to estimates of AGB, but differ in the way those field data are associated with the GIS data layers (CA approach) or the satellite observations (DR approach).
Similar(57)
special approaches, making use of various minimization algorithms based most often on Newton algorithms, are proposed.
The two approaches make use of the Medial Axis Transformation and Projection Vector.
For this purpose, several approaches make use of statistical methods or machine learning algorithms.
We regard this aspect as a major advance over available nonspecific approaches making use of, for example, retinoid creams.
Many of these approaches make use of local features to classify each pixel or segment to an object label, therefore involving an extra step to fuse pixelwise decisions.
As some of these approaches make use of new geometries and materials, the aspect of absolute calibration in the context of these proposals is evaluated in this contribution.
Thereafter, we outline two approaches making use of RetroPath2.0.
These approaches make use of a series of intensifiers, punctuation transformation, emoticons, and many other heuristics.
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