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To segment glial areas and vascular spaces, we applied automatic thresholds to the red RGB channel to generate a binary overlay representing axon-free nerve areas (FluoRender, University of Utah).

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To assign the pose and position of real objects in virtual space, we applied a circle-based tracking method which uses a safety sign instead of the planar-square-shaped marker generally used for registration.

To better understand variation in sewage-impacted benthic macrofauna from subtropical tidal flats over time and space, we applied a five-factor linear model at a hierarchy of spatial (Condition – Contaminated or Non-Contaminated, Tidal Flat and Plot) and temporal scales (Season and Fortnight).

To obtain the biological particles per cubic meter of cabin space, we applied two factors.

To convert continuous place field activity distributions to a discrete partitioning of space, we applied a winner-take-all process.

To investigate the composition of organisms in this space, we applied culture-independent approaches to microbes harvested from the air of two densely populated urban buildings, from which we analyzed 80 megabases genomic DNA sequence and 6000 16S rDNA clones.

For normalization to the standard MNI space, we applied the deformation fields of participants' structural MRI images to their functional MRI images.

To assess the quality and completeness of the assembled gene space, we applied the CEGMA pipeline [ 31] to map a set of 248 low copy Core Eukaryotic Orthologous groups (KOGs) which are conserved across higher eukaryotes.

To search for the maximum-likelihood point over the parameter space, we applied the ECM (Expectation/Conditional Maximization) method (Meng and Rubin 1993), which was previously used in the demographic inference study by Excoffier et al. (2013).

Note that for the ODE system (1) the identified parameters with units molecules were scaled by the system volume Ω that was set to Ω=1 μm [ 39]. > In order to reduce the parameter search space, we applied a set of constraints.

In the mid-level feature space, we apply the Nonparametric Weighted Feature Extraction (NWFE) to construct a compact yet discriminative subspace model.

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