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Results suggest that the proposed method can estimate array tilt, azimuth, longitude, and latitude with Mean Absolute Deviations of 2.75°, 5.85°, 0.2°, and 4.08° respectively, for a typical PV system.
In the first scenario, we demonstrate how to estimate array effects in the presence of biological signal.
Satellite sequence coverage (in the context of the overall 7.5x WGS sequence coverage) was used to estimate array sizes.
We estimate array C and matrix M by maximizing the likelihood l, which can be solved by using the expectation-maximization (EM) algorithm (Dempster et al., 1977).
We estimate array variance by comparing replicate arrays, Type A comparison in Figure 1, for three types of Illumina beadarrays, the 1M-Single, the 1M-Duo, and the 660-Quad.
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Ultimately, this was traced back to unusually high red channel intensity on some arrays, despite normalization, which biased allele frequency estimates array-wide.
When comparing arrays used to allelotype the same DNA pool (henceforth referred to as 'Type A' comparisons), the variation observed can only arise due to the arrays, giving an estimate of array variance.
Briefly, within-pool variation is that observed between two arrays used to allelotype the same DNA pool (i.e. replicate arrays), and is an estimate of array variance.
1M-Duo arrays were analyzed separately by batch using batch-specific estimate of array variance for normalized data.
The two arrays used in the comparison will dictate whether an estimate of array or pooling variance is generated.
When using a ratio-based model to analyze the data, many degrees of freedom are used to estimate the array effect, explaining only a small proportion of the variability.
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