Machine learning accurately predicts the multivariate performance phenotype from morphology in lizards.

Completing the genotype-to-phenotype map requires rigorous measurement of the entire multivariate organismal phenotype. However, phenotyping on a large scale is not feasible for many kinds of traits, resulting in missing data that can also cause problems for comparative analyses and the assessment of evolutionary trends across species. Measuring the multivariate performance phenotype is especially logistically challenging, and our ability to predict several performance traits from a given morphology is consequently poor.

Aqueous fraction of Beta vulgaris ameliorates hyperglycemia in diabetic mice due to enhanced glucose stimulated insulin secretion, mediated by acetylcholine and GLP-1, and elevated glucose uptake via increased membrane bound GLUT4 transporters.

Background: The study was designed to investigate the probable mechanisms of anti-hyperglycemic activity of B. Vulgaris.

Methodology/principal Findings: Aqueous fraction of B.