Heatmap results from the global sensitivity analysis for the generation of chorionic vessels.

<p>Mean (<i>μ</i>*) values of the elementary effects (EE) associated with different output metrics were obtained for an input space with 70 samples per input parameter (input settings from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012470#pc...

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Bibliographic Details
Main Author:	Diana C. de Oliveira (9220729) (author)
Other Authors:	Hani Cheikh Sleiman (19812765) (author), Kelly Payette (17584953) (author), Jana Hutter (9649694) (author), Lisa Story (9649688) (author), Joseph V. Hajnal (15217415) (author), Daniel C. Alexander (9466684) (author), Rebecca J. Shipley (11098888) (author), Paddy J. Slator (19812768) (author)
Published:	2024
Subjects:	Biophysics Physiology Biotechnology Evolutionary Biology Developmental Biology Infectious Diseases Plant Biology Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified yielding vascular structures unlike previous volume sensitivity analysis highlights generating vascular structures facilitating oxygen exchange different topological metrics following branching laws branching angles play placental vascular network placental health issues novel generative algorithm flexible generative algorithm vascular architecture correlates key morphological parameters vessel length variations ex vivo </ morphological parameters vivo </ vessel length branching angle vessel diameter silico </ placental vasculatures placental vasculature placental shape placental function xlink "> successful pregnancy pivotal role physiologically underpinned nutrient transport murray ’ law ), input settings individual components filling algorithms early detection controlled customisation complete structure chorionic vessels
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Heatmap results from the global sensitivity analysis for the generation of chorionic vessels.

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