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In the following, we discuss a modified LMS approach, where we first combine the microphone signals to obtain an improved reference signal for the adaptation of the LMS filters.
We first combine systematic plot data and aerial survey data to estimate density, size distribution, and spatial autocorrelation for Dipteryx oleifera Benth (Fabaceae), a fruit tree species that acts as an important food resource for several large mammals on BCI [23].
To derive causal relationships of PTMs, we first combine BN received from structure learning step with GHCs, forming a network of PTMs and their hidden confounders.
Therefore we first combine the set of P-values derived from various experiments by an order statistics-based approach to obtain a P-value of P-values as a scalable measure of overall significance.
We first combine the similarity metric proposed in (Iorio et al., 2010) with the metric defined in the STITCH database (Kuhn et al., 2012) as a new drug similarity metric as follows.
Specifically, we first combine the reference genome of the host species under study with the consensus virus genome created in the previous step (designated as a separate pseudo-chromosome, chrVirus).
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We first combined three-dimensional structural analysis and literature data to identify hCXCL1 amino acids suitable for mutagenesis.
To create the maps, we first combined all the evt1 files for the individual time intervals (the resulting level event file for Hrc I).
We first combined a blocking metric and a blur metric by global optimization as shown in Equation 14.
We first combined the key concept scores and alternative conception scores relating to the written explanation evolutionary trait gain items and the two written explanation trait loss items.
We first combined the latest census data from the 5 permanent plots with that from 7 selected stands to represent the stand-level growth of Japanese cedar plantations covering with spatial variation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com