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Additionally, the detection data are of great significance to the evaluation and optimization of the reactor performance.
Model-averaging survival probabilities accounted for the uncertainty in which model(s) best fit the detection data, and coho survival estimates were generally robust to the inclusion of those survival covariates that were shared among populations and species.
Using the observed data from the detection data set and simplified adapting it to the bi-allelic case to derive a power estimate for the confirmatory sample using Quanto 1.2.4, we estimated that 363 trios would have a power of 97% to detect an RR effect of RR = 1.7 under a log-additive mode of inheritance (minor allele frequency 15%, αtwo-sided = 0.05).
Prior to statistical analysis, the detection data and original records in the database were checked thoroughly.
The detection data is binomial (Positive or Negative), and logistic regression a widely accepted method of modelling binomial responses.
The detection data consisted of 276 subjects with ALS and 271 healthy controls while the replication data consisted of 221 subjects with ALS and 211 healthy controls.
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Two data sets were created; one was the assembly data set and the other the SNP detection data set.
While this approach is applicable to the case with the target detection, data association and multitarget issues, this paper concentrates on the target trajectory estimation using associated measurements.
The SNP detection was performed with MAQ [ 19] (default parameters) using the SNP detection data set and one of the reference genomes (c50, c50ca, or c50caB).
CU, FP and PC performed the non-denaturing polyacrylamide screens, interpreted the mutation detection data and performed sequencing reactions.
A real-time data-smoothing algorithm for the SPR detection data is adopted; this can reduce the noise level to 5 × 10−7 RIU (refractive index units).
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