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This joint data set was then split into two parts (denoted PV_Joint and Me_Joint) reflecting the two reference populations.
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In this study, the integrated genetic linkage map was calculated by using the joint data approach and regression mapping algorithm of JoinMap® 4 [ 17].
This work investigates the optimal design of wireless body area networks by studying the joint data routing and relay positioning problem, in order to increase the network lifetime.
By using these joint data we derive an estimate for the distribution of the absorbing column density in Sy2s.
For a more in depth analysis of the relationship between the joint data sets sourced from the core scenario survey and the attribute analysis data, the researcher utilized the McNemar Test for Binary Matched Pair.
Furthermore, the joint data collection and analysis of the collected material allowed combining different professional perspectives.
Runs with the joint data sets took considerably longer, up to 30 days to exceed 10,000,000 steps and approach convergence.
For no pooling, the model was applied to each population in turn, for complete pooling to the joint data set.
This data set exhibited relatively small SNP numbers (Fig. 1), which averaged 37%% of those featuring the smaller of the joint data sets (PV_Joint) across genotype missing data thresholds.
Importantly, results for the joint data sets were consistent among independent runs and are very similar to results for mtDNA data alone, in which convergence was confidently achieved.
We used a geometric heating scheme, generally using high values for the heating parameters (for example, g1 = 0.9, g2 = 0.8), particularly for the joint data sets.
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