To investigate historical patterns of genetic variation, we extracted DNA from 114 wolf samples collected in zoological museums over the last ~150 years.
To overcome the problem of segment fragmentation and geometric variation, we extract lines in the scale space.
Applying the algorithm on a digital design and according to parameters behavior in the presence of variation, we extract for each parameter value that will lead to power and variation reduction.
Within this latitude range, NRLMSISE-00 does not contain the types of tidal longitude variations that we extract from the CHAMP and GRACE data, precluding the possibility that such variations could arise through the density-temperature conversion process.
Then, we vary the scheduling policy and the frequency, we note the power and performance variations and we extract energy models.
To eliminate the variation between individuals, we extracted the total RNA from these individual samples and assessed by the Agilent 2100 Bioanalyzer, and then RNAs were equally pooled.
To assess the variation between libraries we extracted RNA from a pool of zebrafish embryos and made 12 replicate transcript counting libraries with different indexing sequences using 1 μg each.
In the first step, we extracted temporal variations of the minimum slant range, corresponding elevation angle, and effective reflection height (Eq. (4)) of one-hop GB echoes.
Thus, to reduce in our CV measurements the extrinsic noise linked to cell-to-cell variations in global physiological factors, we extracted a subset of cells that were very homogeneous in size and granularity.
We extracted mean PM2.5, seasonal variation, city, country, site description, and geocoordinates as available.
