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To automate masking of patterns we used an open source Matlab GP toolbox [17].
To identify such patterns, we used ordination analyses that relate species occurrence to implied environmental gradients.
For a quantitative analysis of XRD patterns we used Diffrac-Plus Topas (Bruker AXS, Germany, version 4.1) with structural models based on ICSD database [76].
For quantitative analysis of XRD patterns we used Diffrac-Plus Topas (Bruker AXS, Germany, version 4.1) with structural models based on ICSD database [11].
To obtain deeper insight into contamination patterns, we used accumulation data from the liver since it is the most frequently investigated organ and prone to pollution accumulation.
To identify functional connectivity patterns, we used a biophysical model to simulate larval dispersal, and then prioritized highly-connected patches using a reserve selection algorithm.
To analyze the degree of similarity among footprint patterns, we used Spearman's rank correlation coefficients (Black et al. 2009) to compare footprints in terms of the rank of commodities arranged in descending order of each footprint value.
In performing a quantitative assessment of the above transition patterns, we used recurrence quantification analysis, a nonlinear technique widely used in many fields of science to discover the time correlations and the hidden dynamics of statistical time series.
I am not talking about the destructive patterns we used to engage in.
For classification of autoantibody patterns we used a linear SVM algorithm [21], [26].
To capture and classify the various response patterns, we used self-organizing maps.
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