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To describe a transfer event we use the following rule: if a cell C1 and a cell C2 have, respectively, a quantity p 1and p 2 of P-gp activity before transfer, then, after transfer, C1 (respectively, C2) will have an activity p 1-f |p 1-f |p)(p 2-p 1) (respectively, p 2-f(|p 1-p 2|)(p 1-p 2)).
Under conditional independence assumptions the posterior distribution for the Bernoulli model is given by Bayes Theorem p (β, τ 2, γ | d a t a ) ∝ L (d a t a | β, τ 2, γ ) p (β, τ 2, γ ) = L (d a t a | β, τ 2, γ ) × { ∏ i = 1 p p (β j | τ j 2 ) p (τ j 2 ) } p where the quantity p is the prior density function, and L data| β, γ, τ ) denotes the likelihood of the Bernoulli model.
This quantity P, now called work function, depends on the kind of solid used, as discovered by Lenard.
In the non-Newtonian theory, the quantity p is a characteristic of the medium.
In the non-Newtonian fluid theory, the quantity p is a characteristic of the medium.
In the non-Newtonian theory, the quantity ((p,q)) is a characteristic of the medium.
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It is proved that the relation between the quantities p and N/2 plays a crucial role.
Similar findings were observed with enzyme activities, which had significantly positive correlations with microbial quantities (p < 0.05).
The values Pr are linked to the quantities P through the relation Pr = P cos 2(θ − (ϕ ± 90°)).
The measured quantities (P 12 − P 17) are listed in Table 7, and they are appropriately combined to define the observational parameters as listed in Table 8.
In particular, we find the revival of oscillations of the local quantities p μ ( n ) after the correlations traverse the whole lattice.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com