Expressions for filter coefficients as a function of the cut-off frequency and the sampling period are derived.
In panel B, the number of connections, misconnections, and their difference as a function of the cut-off tracking radius R, for each protein under study are shown.
The sensitivity and specificity of the different scales vary as a function of the cut-offs used and of the patient clinical condition.
These data show that a high percentage of the normative sample obtains at least one or more low scores at most cut-off scores and that the number of low scores varies as a function of the cut-off score.
According to the Gaussian distribution, the number of low scores varies as a function of the cut-off score the higher the cut-off score, the more abnormal test scores are required to diagnose cognitive impairment.
This can be seen in Figure 3, where we show two different ω inferences for a set of SCPE simulations as a function of the cut-off λ, together with the value calculated by counting the proportion of accepted mutations (see Methods).
Correlation between number of inter-residue contacts and binding affinity (ΔGs) as a function of the distance cut-off used to calculate the contacts.
TG-ROC analyses plot sensitivity and specificity as a function of cut off, with the cut-off value that optimized sensitivity and specificity utilized (1.65).
Peak density at the center of the sample as a function of the final depth of the evaporative cut on the RF frequency.
(B ) Isotropic recoil of circular cut boundaries after laser ablation of the blade at 22 hAPF, as a function of the average cell area in the region of the cut, for WT and dumpy ov1 wings.
(A ) Anisotropic recoil of circular cut boundaries after laser ablation of the blade at 22 hAPF, as a function of the projected average cell elongation in the region of the cut, for WT and dumpy ov1 wings.
