Branching in vascular networks and in URLanismic form is one of the most common and ancient features of multicellular plants, fungi  and animals. These networks deliver resources and eliminate wastes from early development onward, and even play a vital role in the growth, prognosis, and treatment of tumors and stroke recovery. Because of these basic and applied reasons there is immense interest in identifying key features of vascular branching and their connection to biological function. Here we classify diverse branching networks-mouse lung, human head and torso, angiosperm plants, and gymnosperm plants-by harnessing recent advances in medical imaging, algorithms and software for extracting vascular data, theory for resource-distribution networks, and machine-learning. Specifically, we apply standard machine-learning techniques to a variety of feature spaces. Our results show that our theoretically-informed feature spaces-especially those that determine blood flow rate-combined with Kernel Density Estimation are best at distinguishing networks. Our categorization of networks enhances the mapping between biologic function-such as the dependence of metabolic rate on body mass-to vascular branching traits URLanisms URLans. We accomplish this by analyzing how variation in metabolic scaling exponents-around the canonical value of 3/4-arises despite differences in vascular traits. Our results reveal how network categorization and variation in metabolic scaling are both heavily determined by scaling ratios of vessel radii-changes and asymmetries across branching generations-that strongly constrain rates of fluid flow. These linkages will improve understanding of evolutionary convergence across plants and animals while also potentially aiding prognosis and treatment of vascular pathologies and other diseased states .
Branching in vascular networks and in URLanismic form is one of the most common and ancient features of multicellular plants, fungi , and animals. By combining machine-learning techniques with new theory that relates vascular form to metabolic function, we enable novel classification of diverse branching networks--mouse lung, human head and torso, angiosperm and gymnosperm plants. We find that ratios of limb radii--which dictate essential biologic functions related to resource transport and supply--are best at distinguishing branching networks. We also show how variation in vascular and branching geometry persists despite observing a convergent relationship URLanisms for how metabolic rate depends on body mass .
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These networks deliver resources and eliminate wastes from early development onward, and even play a vital role in the growth, prognosis, and treatment of tumors and stroke recovery. Because of these basic and applied reasons there is immense interest in identifying key features of vascular branching and their connection to biological function. Here we classify diverse branching networks-mouse
By combining machine-learning techniques with new theory that relates vascular form to metabolic function, we enable novel classification of diverse branching networks--mouse
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plants, and gymnosperm plants-by harnessing recent advances in medical imaging, algorithms and software for extracting vascular data, theory for resource-distribution networks, and machine-learning. Specifically, we apply standard machine-learning techniques to a variety of feature spaces. Our results show that our theoretically-informed feature spaces-especially those that determine blood flow rate-combined with Kernel Density Estimation are
and gymnosperm plants. We find that ratios of limb radii--which dictate essential biologic functions related to resource transport and supply--are
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networks. Our categorization of networks enhances the mapping between biologic function-such as the dependence of metabolic rate on body mass-to vascular branching traits URLanisms URLans. We accomplish this by analyzing
branching networks. We also show
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metabolic scaling exponents-around the canonical value of 3/4-arises despite differences in vascular traits. Our results reveal how network categorization and variation in metabolic scaling are both heavily determined by scaling ratios of vessel radii-changes and asymmetries across branching generations-that strongly constrain rates of fluid flow. These linkages will improve understanding of evolutionary convergence across plants and animals while also potentially aiding prognosis and treatment of vascular pathologies and other diseased states
vascular and branching geometry persists despite observing a convergent relationship URLanisms for how metabolic rate depends on body mass