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To facilitate further characterization, we synthesized LXY30-biotin and LXY30-FITC conjugates and scrambled LXY30 [cGd-Hyp-Phe 3,5-diF -GNcR] conjugated with FITC (S-LXY30-FITC) using the approach shown in Fig. 2a.
These arguments justify our decision to integrate LibrasTV using the approach shown in Fig. 3 (i.e., with the LIBRAS translator component integrated with TV stations, the Exhibitor component coded as an interactive application and running on DTV receivers and the LIBRAS dictionary stored in an extended memory device).
The standard deviations for changes from baseline in each group were estimated using the approach shown in the Appendix.
As alluded to above, records without at least one shared identifier will not be linked using the approach shown.
In this work we obtained pooled estimates of the case fatality rates for both VTE and major bleeding and their 95% confidence intervals using the approach shown in appendix I. Finally, we calculated the case fatality rate ratio of VTE versus bleeding.
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The suitability of an area for the introduction of ASF from wildlife reservoirs (objective 1) was therefore considered using the hierarchical approach shown in Figure 1.
Firstly, basing on the comparison between the analytical model and the experimental results, it seems that the obtained curve using this approach shows well concordance with the experimental one (Figure 2).
Firstly, based on the comparison between the analytical model and the experimental results, it seems that the obtained curve using this approach shows well concordance with the experimental one (Fig. 2).
Correspondingly, the results of a secondary analysis using the SAGE approach showed the estimated avertable burden to be about 30%0-700-70%lower.
Predictions of buckling knockdown factors using the SPLA approach showed advantages over the use of eigenmodes as the SPLA approach provides a clear design point and does not require experimental data for calibration.
In Section 4, we present the results using all the approaches showing that certain approaches are better suited for a certain classification task, e.g., PCA + DIVS works well for the case of cars vs vans, while PCA + DFVS works well for the case of sedans vs taxis.
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