As one blog for Silicon Angle explained, information on everything from how far and fast we run to what we eat and whether or not we smoke is now being provided through apps and wearable technology to hold us more accountable and to provide the critical data to shows what good healthy habits can do for us.
The relationship between TFs and target genes was explained using information from previous bioinformatics studies, and served to increase our understanding of transcriptional regulation within the silkworm.
The application of this rule-based approach is explained using information from a real past conflict in Chile, where nine criteria were considered, four key decision makers were interviewed and 81 rules were created.
As explained earlier, information from the original pseudogenome alignment is preserved by the annotation step.
Furthermore, since the premenopausal E2 and FSH plasma levels for 14 out of the 16 women reclassified as premenopausal may be explained from information from the eight-page questionnaire (hysterectomy, use of hormone containing intrauterine device, etc)., our cut-off point seems to be well-chosen.
Together, these facts help explain why information ensuing from such studies is often conflicting.
Analysts were left in the dark on such basic issues in the past, which helps explain why bogus information from fabricators was included in some prewar intelligence reports on Iraq.
That is why, the first two principal components are enough to explain all useful information from the sensor structure and to distinguish investigated substances [19, 20].
Although the superiority of ME over SE in CV2 can be explained by borrowing information from the same lines across environments (Burgueño et al. 2012), gains in CV1 may likely attribute to more accurate estimates of environment-specific marker effects by utilizing genetic correlation.
This effect may explain the information gain curves derived from sequential and simultaneous lineups (Wells et al., 2015a).
