Sentence examples for normalizing information from inspiring English sources

The midbrain dopamine neuroblasts, dissected from the foetal brain at the time of midbrain dopamine neurogenesis, are capable of establishing a new terminal network within the host striatum, thus restoring afferent input to striatal projection neurons and normalizing information flow through the damaged motor circuitry (Björklund et al., 1981; Piccini et al., 2000).

The original implementation of PELT in "changepoint" package [ 14] makes the penalty value dependent on the size of data to normalize information content.

This suggests that the integration of the extracted and normalized information from different databases into a unified and comprehensive resource is very necessary.

Because the aquatic toxicity of AES varies according to alkyl and ethoxylate chain lengths, QSARs have been used to "normalize" information from diverse structures to a single structure or mixture which can then be used to compare the toxicity data and eventually derive the aquatic PNEC described by van de Plassche et al. (1999).

ExAC compiled summarized variant information from more than 63 000 exomes by normalizing data from individual studies through an identical pipeline.

Another shortcoming of the present study is the lack of inclusion of a healthy control group undergoing the same stimulations, although this would not add anything to the results of the study because the healthy subjects usually already habituate normally at the baseline, i.e. we cannot normalize the already normal information processing.

By normalizing this information by the total number of occurrences of that echo pair, the posterior probability of each tissue class was generated (i.e., the probability of UTE echo pair (x, y) being obtained for air/bone/soft tissue).

NGD is grounded in the well-established theory of Kolmogorov complexity [ 20] and the Normalized Information Distance (NID) between two objects [ 21].

We further normalized mutual information and conditional entropy by dividing by H(C) so that in their normalized form I*(G1,..., G n ; C) = 1 - H*(C| G1,..., G n ), so that the maximum normalized possible mutual information in the values of Table 1 is equal to one.

The bivariate normalized mutual information similarity measure utilizes complementary and shared information generated by structural and diffusion MRI and so provides additional spatial constraints in the registration process.

In order to get comparable results for each region, we normalize the information presented in Table 5.