Time-averaged simulation results and in vivo measurements to show the impact of red blood cells on the flow field in the cortical microvasculature

<p>The dataset contains&nbsp;5 files. 4 of them are time-averaged results of blood flow simulations with discrete red blood cell (RBC) tracking in realistic microvascular networks. The 5th file contains median values of RBC velocity measurements at&nbsp;capillary bifurcations in the somatosensory cortex of the mouse.</p> <p>Further notes on the simulation results:<br> -&nbsp;The realistic microvascular networks are from the mouse parietal cortex and&nbsp;have first been published in Blinder et al., 2013, Nature Neuroscience&nbsp;(<a href="https://doi.org/10.1038/nn.3426">https://doi.org/10.1038/nn.3426</a>).<br> - The numerical model to simulate blood flow in realistic microvascular networks has been described in Schmid et al., 2017, PLOS Computational Biology (<a href="https://doi.org/10.1371/journal.pcbi.1005392">https://doi.org/10.1371/journal.pcbi.1005392</a>).<br> - MVN1 and MVN2 stands&nbsp;for microvascular network 1 and 2, respectively.<br> - wRBCs and wpPs stands for &#39;with red blood cells&#39; and &#39;with passive particles&#39;. These terms describe two different numerical models: The wRBC-model&nbsp;accounts for all RBC related flow phenomena. The wpP neglects the phase-separation and the Fahraeus-Lindqvist effect, i.e. RBCs and flow are decoupled. Further details are available from Schmid et al. (2019,&nbsp;<a href="https://doi.org/10.1371/journal.pcbi.1007231">https://doi.org/10.1371/journal.pcbi.1007231</a>)</p> <p><strong>File format:&nbsp;</strong>pickle (Python)</p> <p><strong>Files 1 - 4 </strong>(Time-averaged simulation results):<br> Filenames:&nbsp;MVN1_wpPs.tar.bz2,&nbsp;MVN2_wpPs.tar.bz2,&nbsp;MVN1_wRBCs.tar.bz2,&nbsp;MVN2_wRBCs.tar.bz2</p> <p>Each compressed folder contains two files:<br> <br> edgesDict.pkl: dictionary with edge/vessel related data:&nbsp;</p> <ul> <li>flow: Flow rate in vessel [um^3/ms]</li> <li>length: Vessel length [um] (Tortuosity is considered)</li> <li>htt: Tube hematocrit in vessel [-]</li> <li>diameter: Effective vessel diameter [um]</li> <li>connectivity: Vertex indices, e.g. start and end vertex of the corresponding vessel</li> </ul> <p>verticesDict.pkl: dictionary with vertex/bifurcation related data:</p> <ul> <li>index: Index of the current vertex&nbsp;</li> <li>coords: Coordinates to describe the position of the vertex [um]</li> <li>pressure: Pressure at the vertex [mmHg]</li> </ul> <p>&nbsp;</p> <p><strong>File 5</strong> (in vivo RBC velocity measurements):<br> Filename: measurementDict.pkl</p> <p>keys:</p> <ul> <li>divergent_d1: divergent bifurcation, RBC velocity measurement in daughter vessel 1</li> <li>divergent_d2: divergent bifurcation, RBC velocity measurement in daughter vessel 2</li> <li>convergent_m1: convergent bifurcation, RBC velocity measurement in mother vessel 1</li> <li>convergent_m2: convergent bifurcation, RBC velocity measurement in mother vessel 2</li> </ul> <p><br> Data structure: list of list,<br> e.g. daughter vessel 1:<br> [[bif.1 - measure.1,&nbsp;bif.1 - measure.2,&nbsp;bif.1 - measure.3], [bif.2&nbsp;- measure.1,&nbsp;bif.2&nbsp;- measure.2,&nbsp;bif.2&nbsp;- measure.3],...]<br> bif.: bifurcation, measure.: measurement.<br> The order of bifurcations is the same for &#39;divergent_d1&#39; and &#39;divergent_d2&#39; (and for &#39;convergent_m1&#39; and &#39;convergent_m2&#39;).&nbsp;</p> <p>&nbsp;</p>