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Co-expressed Kvβ2 significantly increased the travel distance and the percentage of moving time of YFP-Kv1.2 puncta, and increased the frequency of anterogradely-moving puncta (Fig. 1F,H).
Next, we consider the impact of moving time on energy consumption.
In order to understand the impact of moving time and the number of moving nodes on energy consumption, their relationship is shown in Fig. 5. Figure 5 shows that, on the one hand, when the number of moving nodes is less than 4, energy consumption is small for any moving time, and when the moving time is less than 40 s, energy consumption has similar result.
Expressing Kvβ2, a Kv1 accessory subunit, markedly increased the velocity, the travel distance, and the percentage of moving time of these puncta in both anterograde and retrograde directions.
Moreover, suppressing EB1 or KIF3A significantly reduced the frequency of anterograde YFP-Kv1.2 puncta but did not change the percentage of moving time (data not shown).
Next, based on those parameters, we calculated the travel velocity (v = d/tm), the percentage of moving time Pmoving = tm/ tm + ts) ×100, and the frequency of transport events (or the average number of moving puncta per movie) F(#/min) = n/(198 sec/minsec/min).
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This was done by replacing each original data point with the median value of a moving time window of ±500 values around the respective VMR value.
Get the total length of the moving time.
A polarization analysis of waveform within a moving time window of 0.5 s during a lapse time was performed.
You're going to hate all of them come moving time.
The t represents the moving time of the target.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com