Sentence examples for optimal subject from inspiring English sources

Optimal subject number is a challenging question for preclinical studies.

However, each centre was allowed to choose the optimal subject recruitment method.

With optimal subject selection, exposure estimates can be modeled for the entire cohort, supported by direct measurement of selected pollutants in a subset of the study population.

When deciding the cut-off values and numbers of groups for hospital volumes or prosthesis manufacturer volumes, we attempted to determine the number of groups and cut-off values that would allow optimal subject numbers in each group and the most noticeable difference in the revision rate among the groups based on hospital volumes or prosthesis manufacturer volumes.

Medical observations show that, for all organs, clinical manifestations of deficiency appear when this slope crosses 30 50% of maximal (100%) values of optimal subject's functionality; this is termed "failure threshold".

Optimal subjects should experience recurring symptoms that vary sufficiently in subjective reality during brain scanning.

Recommendations for the optimal subjects-to-variables ratio in factor analytic studies vary considerably.

A subject's strategy can be strictly inferential (i.e. no redundant tests), in which case it is automatically optimal, subjects could systematically attempt to refute the apparently likeliest hypothesis (Popperian strategy) or, as happens often in reality, they may try to confirm those abnormal findings that support their currently favorite hypothesis.

Ironically, with the disease model of independent sufficient causes, Risch's model for absence of epistasis, the strategy of ignoring other loci is no longer optimal; subjects carrying high-risk alleles at other loci should be given less weight in the analysis.

This is the proposition that relaxes the condition defined by P v, min. In effect, weak optimization provides the optimal probability,, subject to a pre-determined R rather than a given P v, min > 0. Fig. 3 shows σ* versus R for representative parameter settings.

This high-dimensional, non-linear warping algorithm selects conserved features, which are informative for registration, thus minimizing structural variation among subjects and providing optimal inter-subject registration.