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Due to some irregularities of sparse matrices, the use of a single compression format is not satisfactory.
In order to investigate the complexity of the mechanistic basis for template selective recognition in these polymeric matrices, the use of a quantum chemical approach has been attempted providing new insights about the mechanisms underlying template recognition, and in particular the crucial role of the crosslinker agent and the solvent used.
Other potential problems include differences in size and density of connection matrices, the use of different parcellation schemes, data sources, anatomical tracing methods, spatial resolution, inclusion or lack of thalamic regions, and uncertain regional homologies.
Also for other non-invasively collected matrices, the use of relatively new '-omics' technologies has opened the door for the detection of new biomarkers of effect.
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Triplicate aliquots of the supernatants were used to determine the HA concentrations within the matrices using the ELISA kit.
We reconstruct the phylogenomic trees from the distance matrices using the Neighbor-joining method as implemented in the PHYLIP package.
Principal Component Analysis was performed on these matrices using the prcomp function.
The results varied for the different matrices using the three different cones.
We parameterize the gain matrices using the LMI conditions.
We conducted all analyses except the dominance matrices using the software program JMP 7 (SAS Institute Inc., Cary, NC, USA).
The MRP reference tree was then computed from the matrix using the PARS program included in the PHYLIP package [ 48].
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