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The point removal led to the appearance of one local Q φ maximum on the time interval from 13.04 to 21.04 with an amplitude approximately twice higher, about 1.3 nT2.
Once A-beta has accumulated beyond a particular point, removal becomes difficult and repeated deposition results in more, larger plaques.
The terminal elimination rate constant (λZ) was estimated by log-linear best-fit regression of the observed plasma concentrations in the terminal elimination phase, without data point removal.
However, although let-7 induces and miR-294 suppresses the G1 restriction point, removal of the restriction point fails to inhibit differentiation.
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Greedy-point removal.
In this section, we apply the greedy-point removal scheme to AMA representation and propose a new method denoted as GPRAMA.
Within the AMA representation framework, we have developed a GPRAMA method based on the greedy-point removal scheme and a mesh patching technique.
Demaret et al. [11, 12] have introduced the greedy-point removal (GPR) scheme that first constructs a triangular mesh using all the image points and then removes the sample points that yield smallest reconstruction error repeatedly.
For representation methods without greedy-point removal technique, (mathbb {M}_{H,3}) and (mathbb {M}_{text {aniso,3}}) provide better quality (see Table 1) but have higher computational cost than ED method (see Table 6).
This paper introduces a framework of anisotropic mesh adaptation (AMA) methods to image representation and proposes a GPRAMA method that is based on AMA and greedy-point removal (GPR) scheme.
AMA methods such as (mathbb {M}_{text {aniso}, k}): anisotropic mesh adaptation method using metric tensor (mathbb {M}_{text {aniso}}) with the initial mesh being adapted k times to generate the desired mesh, proposed in Section 3. GPR: greedy-point removal scheme proposed by Demaret et al. [11, 12].
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