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Fig. 6 Frequency of the length of the variable time window.
As mentioned above, lengths in the range 0.4 0.6 s are the most frequently occurring in the appraisal of the length of the variable time window (Fig. 6).
If the length of the variable time window is less than 0.2 s (1.8% of the data) or cannot be determined within 2 s (4.5% of the data), 0.6 s is used as the length of the time window.
The effect of the new approach not only improves the accuracy of the back-azimuth, but also reduces the time for the estimation compared with the conventional approach, because the average of the length of the variable time window is shorter than that of the conventional time window, as described above.
Depending on the length of the variable segment, one or two MLPA probes per region were designed, preferentially within genes (Table S2).
Also the length of the variable region that allows to discriminate between Class I tRNAs (short variable region of 4 5 nt) and Class II tRNAs (long variable region of > 10 nt, which is supposed to form a short helix) (Rich and RajBhandary 1976) does not correlate with the presence of tRNAs in core and peripheral sets.
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Figure 6 shows the frequency of the lengths of the variable time window for the dataset described above.
This model suggests that the length of this variable region is important for optimal recognition.
This distance can be correlated with the localization length parameter of the variable range hopping transport mechanism obtained from resistance measurements.
The choice of the optimal lag lengths of the variables was determined by the multivariate forms of the Akaike Information Criterion (AIC) and the Schwartz Criterion (SC).
where x EPC denotes the length (in bits) of the variable part of the EPC code.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com