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In order to rebuild the past species pool matrix using present field data and historical land use map, we applied a nearest neighbour selection using spatial query.

Due to the high resolution (~1.6 Å) of density map, we applied Autobuild from Phenix software package(Adams et al., 2002) to automatically build the main P protein structure before manually adjust the structure in COOT (Emsley and Cowtan, 2004) guided by (2Fo - Fc) and (Fo - Fc) electron density maps, where Fo and Fc are the observed and calculated structure factors, respectively.

To better visualize these divergences or 'distortions' between class profiles on the map we applied an additional visualization technique known as Sammon Mapping [ 14].

To estimate the resolution of our averaged map, we applied the same averaging technique to the two independent reconstructions that were used to calculate the 4.8-Å resolution for the original density map, and then calculated the FSC between the two resulting averaged maps.

To evaluate the temporal validity of our NDVI map, we applied an ecologic map of Barcelona for the year 2004 (Centre for Ecological Research and Forestry Application 2004) to abstract the percentage of the green areas over grids of 100 m × 100 m across the Barcelona.

Similar(55)

Given a boundary probability map, we apply the following steps to find the final boundary: Two-dimensional Gaussian smoothing with σ = 2 pixels isotropic σ (x,y ) = (12, 8 μm One-dimensional non-maximal suppression on each A-scan; Hysteresis thresholding; and Boundary tracking from left to right.

To assess the responsiveness of the utility mapping function, we applied it to data from an open-label, international (Europe and Canada), 50-week trial evaluating the long-term safety and tolerability of long-acting risperidone in 725 patients with schizophrenia or schizoaffective disorder considered to be stable at study entry [ 20, 21].

To compute task-based statistical parametric maps, we applied a voxel-based general linear model (GLM), as implemented in FEAT.

To compute statistical parametric maps, we applied a voxel-based general linear model (GLM), as implemented in FEAT.

To test for such a bias in mapping efficiency, we applied our read mapping procedure to simulated RNA-seq reads from each population.

To demonstrate the performance of the proposed mapping rules, we applied this method to real EEG data.

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