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We also demonstrate the application of the asymmetry correction method to experimental data by estimating the temporal RFs of retinal ganglion cells from responses to correlated, spherically asymmetric natural luminance sequences and using the RFs to predict responses to novel stimuli.
In the following analysis, we distinguished between CPM normalized and raw (unnormalized) data by estimating the mean, variance, skewness, and kurtosis of the distributions of expression values of the genes.
It is possible to accurately quantify these effects on the transcriptome from the array data by estimating the equivalent stage in the developmental series for each of the CE experiment samples.
Detailed studies have addressed this issue in relation to nutrition for the population at large in different countries, based on dietary survey data or cross-section questionnaire data, by estimating the costs of a diet adhering to a high Healthy Eating Index or similar measures [ 4- 6] or to a specific regional diet, e.g. a Mediterranean diet [ 6- 8].
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However, little is known about the quality of DNLR data. 1 The main objective of this study was, therefore, to quantify DNLR data quality by estimating the completeness and positive predictive values (PPVs) for 30 variables.
At Rockaway Beach in Queens, Joseph Mauro, a parks supervisor, was handling data collection by estimating the current populations of three segments of the shore, one at Beach 59th Street, one east of that and another to the west.
The applicability of the proposed SE approach and bad data analysis is demonstrated by estimating the operating state of two integrated AC DC multi-terminal transmission networks.
Future studies using entire-genome polymorphism and divergence data can add resolution by estimating the proportion of adaptive substitutions per gene [see [ 23]].
Comorbidity in the larger data file was first examined by estimating the annual number of discharges having each of the four groups of mental illness.
Meuwissen et al. [ 11] introduced Genomic Selection (GS) as a method that collectively uses the genome-wide marker data in predicting the phenotype by estimating the genomic breeding values for each individual.
The collected data is used to train artificial neural networks (ANNs) which would improve the accelerometer outputs by estimating the reference data from the actual sensor outputs.
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