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The log-rank statistic showed that the survival time of the three subtypes was significantly different (P = 0.008), which had a markedly higher caliber compared to the original partitions (the clinic labels, P = 0.010, see [2]) to map their differential survival profiles.
The labels P, S, and X mark P-, S-, and scattered waves, respectively.
However, the influence of sugar reduction claim on hedonic expectations and healthfulness perception was modulated by the inclusion of the traffic-light system on labels (p = 0.01).
The labels P, S, and X mark the arrival times of P- and S-waves and a phase of unknown origin, respectively.
This is significantly more than expected by random permutation of the sample labels (p = 1.9 × 10-5).
As Banningh and colleagues (2008) noted, "[we did not] monitor the effect of the consultation in which the geriatrician disclosed and explained the MCI diagnosis besides MCI, clinicians use numerous other labels" (p. 153).
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Initially, the problem at hand can be considered a multi-label classification, with two labels: P-gp and BCRP.
This experiment would be more compelling if the authors could use a system that simultaneously labels P-bodies and SGs in the same cell and show that under the same conditions, hexanediol eliminates P-bodies but not SGs.
The saddle and attracting families of periodic orbits are labelled P r and P a, respectively.
Then, define the set of new nodes labeled P as R = {v s }.
Label p positive and n negative instances from usinging the classifier h1.
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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