the Duchy of Aquitaine. 2. The Capetian Kingdom ... Central England, excluding Devon, Cornwall, and the counties north of the Humber. 4. Northern France and ...
function Fi(x) with i = 1,...,m, there exist some number ti â (0, 1) and a vector zi = (1 â ti)y1 + tiy2 such that: Fi(y2) â Fi(y1) = DxFi(zi)(y2 â y1). (4). Choosing h ...
compound that can be retrieved from public databases such as Scifinder and ... 2. 3. 4. 5. 6. 7. 8. 9. 10. 11 sign al in te n sity. pH of a 4 mM aqueous solution a) b) ...
Predictor Hazard Ratio P-value. Hazard Ratio ... In each set of patients, the multivariate Cox proportional hazard ratios [37] for GSVD and age ..... P value 1 10 1.
Fig A shows the UML class diagram, while Table A shows the description of the state variables of the ... price (P). Firms' sales price. No. No .... accelerator effect and, together, drive a continued and exponential economic growth in our model.
brightness comparison condition, trials began with the occluder presented for 500 ms. An oval image then appeared to the left and the right of the occluder and ...
observed outcome and model-predicted probabilities as measured by the model deviance (-2 Ã the log-likelihood). To reduce the number of possible models ...
Hybridization Energies under no neighbor influence. Free energies âGx y are obtained from hybridization energies which include the effect of both the base-.
The former occurs from the interior of Washington to the Mojave Desert in California and the ... Munroe, 1976 (type locality: Washington, Mt. Rainier. [CNC]), L. a.
For a (high) intercept of. ( )0 exp. 100 β = , all models had near identical results, although the log-link models resulted in slightly lower mean, variance, skewness ...
Now we consider all possible generations with potential retroposon insertions that later ... by S. Then the total numbers of retroposon insertions with properties j.
elements of informed consent required by federal regulations. The QuIC-A survey has ... Insurance Portability and Accountability Act (HIPAA). 5.4 Data Safety ...
S1 Appendix. Survey items utilized in the analysis of .... Question: S9Q6A: Have you EVER had any type of cancer? Value Label. 1 Yes. 2 No. 7 Don't know.
Each curve Ci is offset by a horizontal distance m. The slope spk at each point pk of the curves is calculated using pk+1 in first order Taylor expansion. According.
The standard error (SE) for the death rate x m was calculated using ... (i, x=65, 70, 75, 80, 85) to estimate the standard error for life expectancy at age x, where i x.
Putative products of ECANG7_10984 were predicted using the Expasy translate tool (http://web.expasy.org/cgi- · bin/translate/dna_aa). Since ECANG7_10984 ...
of Covert Attention through Pupillometry (S1 Appendix). Sebastiaan Mathôt1*, Jean-Baptiste Melmi1, Lotje van der Linden1, and Stefan Van der Stigchel2.
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Comparison of 4-state and 9-state Models of CaM-protein Binding. ... The high cooperativity of Ca2+ binding at each CaM terminus has led to the development.
product because the height of peak 9 is greater than that of peak 10 (this case ... the expected peak heights of drop-out alleles are strictly different in each allele, ...
for the They're Calling on You campaign and monthly zoo visitor numbers. Table 1. Number of mobile phones donated to Zoos Victoria every month during 2009 ...
in binding efficiency makes survival of viral DNA a ârunawayâ process: it becomes progressively less plausible to completely exterminate viral DNA after the first ...
S1 Appendix CRISPR-induced reduction in the viral burst In the main text we estimated the number of CRISPR spacers that maximizes survival of a host cell. Here are compute the number of spacers which minimizes the viral burst (and thus the number of secondary infections) from a doomed host cell with still functioning CRISPR system. As in Eq. (7), the total interference rate is assumed to be proportional to the total binding probability multiplied by the copy number of viral DNA. This is an overestimating approximation as in reality there is a spreading of a fixed number CRISPR effectors over increasing number of copies of viral DNA, which inevitably makes binding to any given protospacer less probable. Such a reduction in binding efficiency makes survival of viral DNA a “runaway” process: it becomes progressively less plausible to completely exterminate viral DNA after the first round of DNA replication. We also approximate viral DNA replication as a continuous process and obtain the following kinetic equation for the copy number of viral DNA V (t), X dV (t) = V (t) D − a Bi dt i
! ,
(S1)
with the solution "
! # D−a
V (t) = exp
X
Bi
t .
(S2)
i
Here D is the viral duplication rate and it is assumed that initially the host cell contained a single copy of viral DNA, V (0) = 1 Without active CRISPR system, the number of viral DNA copies reaches the native burst size Vb after time θ, Vb = exp [Dθ] .
(S3)
Assuming that the viral maturation time θ is not affected by CRISPR activity, the viral burst in the presence of CRISPR VCRISP R becomes, " VCRISP R = Vb exp −a
#
X i
# θX Bi . Bi θ = Vb exp −χ τ i 1
"
(S4)
Figure A: The maximum of the product (S6) for various χ. The number of spacers S that maximizes expression S6 for: the host cell survival with χ = 1.4 (left panel) and the size of viral burst with χ0 = νχ = 1.4 ∗ 6.65 ≈ 9.3 (right panel). As in the main text, mi = µi−1/2 . The factor ν ≡ θ/τ is the number of cycles of replication of viral DNA and can be estimated from the burst size, 2ν = Vb . Steps analogous to those leading to Eqs. (13-15) in the main text show that the burst size in host cells infected with viruses with S protospacers each having probability mi to remain mutation-free is
VCRISP R = Vb
S Y
{1 − mi [1 − exp (−νχBi )]}
(S5)
i=1
. Comparing Eq. (S5) to Eq. (15) in the main text reveals that the minimum of the product S Y
{1 − mi [1 − exp (−νχBi )]}
(S6)
i=1
maximizes the host cell survival probability when ν = 1 and minimizes the viral burst size when ν equals to the number of cycles of replication of viral DNA. For a typical burst size vb = 100, the number of replication cycles ν ≈ 6.65 (see Eq. S2 in Appendix S2), which, as seen comparing left and right panels of Fig. A, usually increases the optimal number of spacers (see also Fig. 5(C) in the main text showing the dependence of the optimal number of spacers on χ.)