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For all systems, the regression values for the adjustment of these data to a four-parameter logistic curve were larger than 0.97, and the LOD increased in the following order: vt2< eae<<16S< eae< vt1 (Table 3), being around 10 cfu/mL (0.9 ng/μL), which would correspond to 200 cfu in the PCR tube.
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Many of the prediction models were developed and presented as simplified scoring systems, whereby the regression coefficients were rounded to integers and then summed to obtain an overall integer score for a particular individual.
CDI patients categorized by the ATLAS scoring system, using the regression formula* derived from the ATLAS system applied to the 003 clinical trial.
Linear predictions, and their 95% CIs, were calculated for each clinical system from the regression models, overall (model 1) and by indicator group (model 2).
Since treatment response might be also an additional potential factor affecting the LNY, our present study evaluated also the pathological tumor response according to Sinn by a standardized classification system for the regression grade [ 16].
Linear regressions were used to derive parameters for eqs 29, 30, and 31 from the adjusted data excluding systems omitted from the regression of multibranch loop initiation free energy and using Δ H° from 1/ TM vs ln CT/4) for all sequences, including G_CG_G/C_C.
Even more, the mode sequence has mostly considered a priori known and independent of the system parameters and the regression vector.
The simplified scoring system based on the regression model is displayed in Table 4.
Although various statistical learning approaches were explored for the systems, logistic regression proved to be the most accurate method in all cases.
Concentration and temperature dependences of the electrical conductivity for all the studied low-temperature multi-component systems were described by the regression equation.
In addition, to get rid of the possible biases and imprecision associated with the difference GMM estimator, system GMM estimator combines the regression in difference with regression in levels.
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