Sentence examples for giving complementary from inspiring English sources

Data collected from the integrated experiment, from simplified tests, and from modelling are put together, giving complementary information.

"There are some differing opinions on when to start giving complementary foods to an infant, 4 to 6 months [old], but not much controversy that 'breast is best' for many reasons". The World Health Organization recommends exclusively breastfeeding a new baby for as long as six months.

The likelihood of giving complementary food at each month and the cumulative risk of giving complementary food were estimated by the life table survival analysis.

Another reason for giving complementary food was discussed in China, suggesting there is a belief that it will improve weight gain and lead to healthier babies [ 14].

Moreover, by comparison to endogenous RNA species they can help assess the burden of an implemented network on the population of RNAP and ribosomes, giving complementary information to RNA-Seq experiments.

Two hundred ninety three (38.7%) of the interviewed caregivers had started giving complementary foods or drinks other than breast milk (mostly water, gruel and other semisolid cereal-based preparations) to their infants before they reached 6 months of age.

Together, we believe these datasets give complementary support to our evaluation strategy and assure its broad relevance.

The combination of immunohistochemistry, proteomics and scanning electron microscopy gives complementary perspective on the pancreatic ECM composition and organization.

"Because some fruits and vegetables are rich in one compound while others are rich in another," Dr. Liu said, "they give complementary nutrition, so we encourage consumers to eat a wide variety of foods, and increase servings, too," as nutrition insurance.

This method allows rapid and direct exploration of multiple local visual responses, and is thus able to give complementary information to phase encoded mapping of retinotopic areas.

Most of their efforts will focus on machine learning (ML) and data analysis (DA) primitives for analyzing data that are modeled by matrices or graphs, with an emphasis on primitives that (when combined appropriately) give complementary algorithmic and statistical advantage.