OSCR

Stretch and flow at the gliovascular interface: High-fidelity modeling of astrocyte endfeet.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results › Robustness and Sensitivity to Perivascular Pathway Resistance and Waveforms. ↔ endfoot-mechanics-main.zip/plotting/compare_scenarios.py, lines 29–63 · score 0.69 · maximal resistance, minimal resistance, low resistance, endfeet gaps, flow rates, membrane
  2. [2] § Results › Osmotic Challenge Drives AQP4-Enhanced Net Fluid Flow. ↔ endfoot-mechanics-main.zip/plotting/compare_multiple_scenarios.py, lines 54–159 · score 0.64 · flux rates, von Mises, flow velocities, ECS outer, flow rates, AQP
  3. [3] § Results › Osmotic Challenge Drives AQP4-Enhanced Net Fluid Flow. ↔ endfoot-mechanics-main.zip/flow_metrics.py, lines 201–264 · score 0.63 · Peak endfoot, endfoot volumes, gap flow, flow rate, intracellular, surface
  4. [4] § Results › Osmotic Challenge Drives AQP4-Enhanced Net Fluid Flow. ↔ endfoot-mechanics-main.zip/plotting/compare_scenarios.py, lines 109–172 · score 0.63 · flux rates, von Mises, flow velocities, ECS outer, AQP, stress
  5. [5] § Results › Robustness and Sensitivity to Perivascular Pathway Resistance and Waveforms. ↔ endfoot-mechanics-main.zip/plotting/compare_scenarios.py, lines 29–63 · score 0.59 · maximal resistance, minimal resistance, low resistance
  6. [6] § Results › Osmotic Challenge Drives AQP4-Enhanced Net Fluid Flow. ↔ endfoot-mechanics-main.zip/plotting/compare_pressure.py, the whole file · a weak match · score 0.54 · PVS pressure, gap flow, PVS volume, um3, Pa, simulated
  7. [7] § Results › PVS Stiffening Reverses Gliovascular Flow Dynamics. ↔ endfoot-mechanics-main.zip/plotting/compare_multiple_scenarios.py, lines 54–159 · score 0.52 · von Mises, flow velocities, ECS outer, flow rates, stress, sheath

Paper

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The authors' code

Python · 267 lines · 9.6 KB · CC-BY-4.0 · 3 matches

  2. import sys
  3. sys.path.append('.')
  4. from plotting.utils import (load_results, mesh_name, sim_name,
  5. m3topl, efcolor, ecscolor, pvscolor)
  6. from pathlib import Path
  7. import matplotlib.pyplot as plt
  8. import seaborn as sns
  9. import numpy as np
  10. import yaml
  11. import pandas as pd
  12. import matplotlib.gridspec as gridspec
  13. sns.set_theme()
  14. sns.set_style(style='ticks')
  16. m32fl = 1e18
  17. m2nm = 1e9
  18. m2mum = 1e6
  20. def add_percentage_change(ax, xvals, bs):
  21. b1, b2 = bs
  22. for x, bi1, bi2 in zip(xvals, b1, b2):
  23. reldiff = 100 * (bi2 - bi1)/bi1
  24. ax.annotate(f"{reldiff:.0f}%",
  25. (x, max([bi1, bi2])), horizontalalignment="center",
  26. verticalalignment="bottom", textcoords='offset pixels', xytext=(0, 2))
  29. flows = [
  30. 'endfeet_gap_flow',
  31. 'endfeet_neck_flow',
  32. 'aqp_membrane_flow',
  33. 'non_aqp_membrane_flow',
  34. 'ecs_flow',
  35. #"outlet_flow"
  36. #'inlet_flow',
  37. #'outlet_flow',
  38. #'membrane_volumetric_flow_rate'
  39. ]
  41. area_names = [
  42. "endfeet_gap_id_area",
  43. "endfeet_neck_outer_id_area",
  44. "aqp_membrane_id_area",
  45. "interf_id_area",
  46. "ecs_outer_id_area",
  47. #"pvs_outlet_id_area"
  48. ]
  50. flow_labels = ["EFG","AP","AdM", "AbM", "ECS"] #
  51. show_signs = False
  53. colordict = {"WT":"purple", "euler":"cornflowerblue",
  54. "closed":"purple", "high resistance":"purple","maximal resistance":"brown",
  55. "open":"teal","low resistance":"teal", "minimal resistance":"goldenrod",
  56. "AQP4-KO":"darkgoldenrod",
  57. "EF-KO":"lightsalmon", "lagrange":"gold",
  58. "WT-VLF":"cornflowerblue","AQP4-KO-VLF":"black",
  59. "highPerm" : "slategrey", "stiff":"deeppink", "closedouter":"blueviolet",
  60. "standard":"black", "refined":"red"
  61. }
  62. colordict.update({i:c for i,c in zip(["baseline","x2","x4","x6","x8"], sns.color_palette("mako"))})
  63. print(colordict)
  64. def get_abs_max(ar):
  65. m = max(ar, key=abs)
  66. return abs(m), "-" if m > 0 else "+"
  69. def plot_panel(results, scenario_names, filename, layout="3x2"):
  70. size = (2,3)
  71. #size = (1,6)
  72. #size = (3,2)
  73. fig = plt.figure(tight_layout=True, figsize=(size[1]*3, size[0]*3))# if layout=="3x2" else (16,8))
  74. gs = gridspec.GridSpec(*size)
  75. ax1 = fig.add_subplot(gs[0])
  76. ax2 = fig.add_subplot(gs[1])
  77. ax3 = fig.add_subplot(gs[2])
  78. ax4 = fig.add_subplot(gs[3])
  79. ax5 = fig.add_subplot(gs[4])
  80. ax6 = fig.add_subplot(gs[5])
  82. colors = [colordict.get(n, None) for n in scenario_names]
  83. # max flow rates
  84. nr = len(results)
  85. width = {2:0.4, 3:0.4, 4:0.3, 5:0.35}[nr]
  87. x = np.arange(len(flows))
  88. max_flows = [np.zeros(len(flows)) for i in results]
  89. signs = [[None]*len(flows) for i in results]
  90. for j,r in enumerate(results):
  91. for i, q in enumerate(flows):
  92. max_flows[j][i], signs[j][i] = get_abs_max(r[q])
  94. for i,(mf,c) in enumerate(zip(max_flows, colors)):
  95. b1 = ax1.bar(x - width/2 + i*width*2 / nr, mf*m32fl, width, color=c)
  97. if show_signs:
  98. for i, (bar1, bar2) in enumerate(zip(b1, b2)):
  99. ax1.text(bar1.get_x() + width/2, bar1.get_height(),
  100. signs_1[i], ha='center', va='bottom')
  101. ax1.text(bar2.get_x() + width/2, bar2.get_height(),
  102. signs_2[i], ha='center', va='bottom')
  104. ax1.set_yscale("log")
  105. ax1.set_ylabel("flow ($\mu m^3$/s)")
  106. ax1.set_xticks(x, flow_labels)
  107. if nr==2: add_percentage_change(ax1, x, [mf*m32fl for mf in max_flows])
  109. # max flux rates
  110. areas = np.array([results[0][an] for an in area_names])
  111. max_flux = [mf / areas for mf in max_flows]
  113. for i,(mf,c) in enumerate(zip(max_flux, colors)):
  114. b1 = ax2.bar(x - width/2 + i*width*2 / nr, mf*m2nm, width, color=c)
  116. if show_signs:
  117. for i, (bar1, bar2) in enumerate(zip(b1, b2)):
  118. ax2.text(bar1.get_x() + width/2, bar1.get_height(),
  119. signs_1[i], ha='center', va='bottom')
  120. ax2.text(bar2.get_x() + width/2, bar2.get_height(),
  121. signs_2[i], ha='center', va='bottom')
  122. ax2.set_ylabel("flux (nm/s)")
  123. ax2.set_xticks(x, flow_labels)
  124. ax2.set_yscale("log")
  125. if nr==2: add_percentage_change(ax2, x, [mf*m2nm for mf in max_flux])
  128. # max mean pressure differences
  130. x = np.arange(2)
  131. ecs_outer_mean_p = [r["ecs_outer_mean_pressure"] for r in results]
  133. aqp_dp= [abs(r["astro_interf_mean_pressure"] -
  134. r["ecs_interf_mean_pressure"]).max() for r in results]
  135. layer_dp = [abs(r["ecs_outer_mean_pressure"] -
  136. r["ecs_interf_mean_pressure"]).max() for r in results]
  138. for i, (aqp, ldp, c) in enumerate(zip(aqp_dp, layer_dp, colors)):
  139. ax3.bar(x - width/2 + i*width*2 / nr, [aqp, ldp], width, color=c)
  141. ax3.set_ylabel("pressure difference (Pa)")
  142. ax3.set_xticks(x, ["AQP \n membrane", "endfoot \n sheath"])
  144. if nr==2: add_percentage_change(ax3, x, [(aqp, ldp) for aqp, ldp in zip(aqp_dp, layer_dp)])
  146. # mean flow velocities
  148. D = ["ef", "oc", "pvs", "ecs"]
  150. x = np.arange(4)
  151. mean_flow_vel = [np.array([r[f"mean_{d}_flow"].max() for d in D]) for r in results]
  153. for i, (mf, c) in enumerate(zip(mean_flow_vel, colors)):
  154. ax4.bar(x - width/2 + i*width*2 / nr, mf*m2mum, width, color=c)
  156. ax4.set_ylabel("velocity ($\mu$m/s)")
  157. ax4.set_xticks(x, [d.upper() for d in D])
  158. if nr==2: add_percentage_change(ax4, x, [mf*m2mum for mf in mean_flow_vel])
  160. mean_mises = [np.array([r[f"{d}_von_mises"].max() for d in D]) for r in results]
  161. for i, (mm, c) in enumerate(zip(mean_mises, colors)):
  162. ax5.bar(x - width/2 + i*width*2 / nr, mm, width, color=c)
  163. ax5.set_ylabel("stress (Pa)")
  164. ax5.set_xticks(x, [d.upper() for d in D])
  165. if nr==2: add_percentage_change(ax5, x, [mm for mm in mean_mises])
  167. mean_disp = [np.array([r[f"{d}_displacement"].max() for d in D]) for r in results]
  168. for i, (md, c) in enumerate(zip(mean_disp, colors)):
  169. ax6.bar(x - width/2 + i*width*2 / nr, md*1e9, width, color=c)
  170. ax6.set_ylabel("displacement (nm)")
  171. ax6.set_xticks(x, [d.upper() for d in D])
  172. if nr==2: add_percentage_change(ax6, x, [md*1e9 for md in mean_disp])
  174. # displacement over time
  176. from deformationplots.diameter_change import get_diameter
  177. times = results[0]["times"]
  179. diams = [get_diameter(r) for r in results] #lumen_diam, pvs_diam_1, ef_diam_1
  180. pvs_widths = [(pvs_diam - lumen_diam) / 2 for (lumen_diam, pvs_diam, ef_diam) in diams]
  183. """
  184. gs7 = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=gs[6:9])
  186. ax71 = fig.add_subplot(gs7[0])
  187. ax72 = fig.add_subplot(gs7[1])
  188. ax73 = fig.add_subplot(gs7[2])
  190. marker = ["-", "--",":", "-,"]
  191. for n, m, pvs_w, (lumen_diam, pvs_diam, ef_diam) in zip(scenario_names, marker, pvs_widths, diams):
  192. ax71.plot(times, 1e3*(ef_diam - ef_diam[0]), m, color=efcolor, label=f"$\Delta$ EF ({n})")
  193. ax72.plot(times, 1e3*(pvs_w - pvs_w[0]), m, color=ecscolor, label=f"$\Delta$ PVS ({n})")
  194. ax73.plot(times, 1e3*(lumen_diam - lumen_diam[0]), color="crimson", label="$\Delta$ lumen")
  196. ax73.set_xlabel("time (s)")
  197. #ax7.legend(frameon=False, loc="center left", ncol=1,
  198. # bbox_to_anchor=(0, 0.5), labelspacing=4)
  199. #ax71.legend(frameon=False, loc="upper center", bbox_to_anchor=(.1, 1.4), ncols=2)
  200. ax71.legend(frameon=False, ncols=min([nr,3]), loc='upper left', bbox_to_anchor=(0., 1.6))
  201. ax72.legend(frameon=False, ncols=min([nr,3]), loc='upper left', bbox_to_anchor=(0., 1.6))
  202. ax73.legend(frameon=False, ncols=min([nr,3]), loc='upper left', bbox_to_anchor=(0., 1.6))
  204. sns.despine()
  205. ax71.set_ylabel("$\Delta$ endfeet")
  206. ax72.set_ylabel("$\Delta$ PVS")
  208. ax71.spines['left'].set_visible(False)
  209. ax72.spines['left'].set_visible(False)
  210. ax73.spines['left'].set_visible(False)
  211. ax73.set_yticks([])
  212. ax73.vlines(-0.01, ymin=-50, ymax=50, color="crimson", lw=5)
  213. ax72.vlines(-0.01, ymin=-5, ymax=5, color=ecscolor,lw=5)
  214. ax71.vlines(-0.01, ymin=-50, ymax=50, color="teal",lw=5)
  215. #ax73.text(-0.02, 0, "80 nm", rotation="vertical", color="red")
  216. ax71.set_ylabel("100 nm", color=efcolor)
  217. ax72.set_ylabel("10 nm", color=ecscolor)
  218. ax73.set_ylabel("100 nm", color="crimson")
  219. ax71.set_xlim(left=-0.01)
  220. ax72.set_xlim(left=-0.01)
  221. ax73.set_xlim(left=-0.01)
  223. ax71.spines['left'].set_visible(False)
  224. ax71.set_yticks([])
  225. ax71.spines['bottom'].set_visible(False)
  226. ax71.set_xticks([])
  228. ax72.spines['left'].set_visible(False)
  229. ax72.set_yticks([])
  230. ax72.spines['bottom'].set_visible(False)
  231. ax72.set_xticks([])
  232. """
  234. sns.despine()
  235. plt.figlegend(scenario_names, loc='upper center', #fontsize="large",
  236. ncol=min(nr, size[1]), frameon=False)
  238. fig.tight_layout(rect=(0,0,1,0.95))
  239. #fig.subplots_adjust(wspace=0.35)
  240. axs = [ax1, ax2,ax3,ax4,ax5,ax6]
  241. if size[0] > 1:
  242. for i in range(size[0]):
  243. fig.align_ylabels(axs[i::size[1]])
  244. plt.savefig(filename, transparent=True, dpi=500)
  246. if __name__ == '__main__':
  248. n = (len(sys.argv) -1) / 3
  249. print(sys.argv)
  250. sim_names = sys.argv[1::3]
  251. mesh_names = sys.argv[2::3]
  252. scenario_names = sys.argv[3::3]
  253. print(sim_names)
  254. print(mesh_names)
  255. print(scenario_names)
  257. folder = "_".join([f"{sn}_{mn}" for sn, mn in zip(mesh_names, sim_names)])
  258. print(folder)
  259. filename = (f"results/comparisons/{folder}/" +
  260. f"{'_'.join(scenario_names)}.png")
  261. results = [load_results(sn, mn) for sn, mn in zip(mesh_names, sim_names)]
  262. Path(filename).parent.mkdir(exist_ok=True, parents=True)
  263. plot_panel(results, [n.replace("+", " ") for n in scenario_names], filename)

compare_scenarios.py, under CC-BY-4.0 · at the source

Overview

Authors: Marius Causemann1, Rune Enger2,3,4, Marie E. Rognes1,4
ORCID iDs: Rune Enger, Marie E. Rognes
  1. Department of Numerical Analysis and Scientific Computing, Simula Research Laboratory, Oslo 0164, Norway
  2. Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo 0372, Norway
  3. Department of Neurosurgery, Oslo University Hospital - Rikshospitalet, Oslo 0372, Norway
  4. K. G. Jebsen Centre for Brain Fluid Research, Oslo 0372, Norway
Institutions: Simula Research Laboratory (Norway); Oslo University Hospital (Norway); University of Oslo (Norway); Institute of Basic Medical Sciences of the Chinese Academy of Medical Sciences
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 11, article e2517059123
Dates: received 23 July 2025; accepted 3 February 2026; published online 13 March 2026; in print 17 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1073/pnas.2517059123 · PMID 41824500 · PMCID PMC12994201 · OpenAlex W7135236323
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: cellular / molecular (subfield)
Methods: Statistics
Keywords: astrocyte endfeet, perivascular spaces, poroelasticity, digital twin, mechanics
MeSH: Astrocytes*, Brain*, Animals, Aquaporin 4, Arterioles, Computer Simulation, Models, Biological (* major topic)
Journal subjects: Physical Sciences, Applied Mathematics, Biological Sciences, Biophysics and Computational Biology
Topic: Cerebrospinal fluid and hydrocephalus (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Norges Forskningsråd (Forskningsrådet) (#324239); Chan Zuckerberg Initiative (CZI) (2023-331840); Letten Foundation (GliaLab); HOD | Helse Sør-Øst RHF (sorost) (2025018 2024079)); Stiftelsen K. G. Jebsen (K. G. Jebsen Centre for Brain Fluid Research)
Citations: not cited yet (Europe PMC); 62 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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Zenodo 15303023

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 9 files
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (43 files), Matplotlib (39 files), seaborn (9 files), pandas (3 files), SciPy (2 files), imageio (1 file), Pillow (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
58 files
At the source:

The paper's code and data availability statement is in the Data section.

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  • 57 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Read it in the paper: doi.org/10.1073/pnas.2517059123.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 7 MeSH terms, 5 funders, 53 references.

Cite

This paper

Causemann, M., Enger, R., & Rognes, M. E. (2026). Stretch and flow at the gliovascular interface: High-fidelity modeling of astrocyte endfeet. Proceedings of the National Academy of Sciences of the United States of America, 123(11), e2517059123. https://doi.org/10.1073/pnas.2517059123

BibTeX

@article{causemann2026stretch,
author = {Causemann, Marius and Enger, Rune and Rognes, Marie E.},
title = {{Stretch and flow at the gliovascular interface: High-fidelity modeling of astrocyte endfeet}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = mar,
volume = {123},
number = {11},
pages = {e2517059123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2517059123},
url = {https://doi.org/10.1073/pnas.2517059123},
pmid = {41824500},
pmcid = {PMC12994201}
}

RIS

TY - JOUR
AU - Causemann, Marius
AU - Enger, Rune
AU - Rognes, Marie E.
TI - Stretch and flow at the gliovascular interface: High-fidelity modeling of astrocyte endfeet
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/03/13
VL - 123
IS - 11
SP - e2517059123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2517059123
UR - https://doi.org/10.1073/pnas.2517059123
LA - en
ER -

CSL-JSON

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"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
"author": [
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"family": "Causemann",
"given": "Marius"
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}
],
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"volume": "123",
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"page": "e2517059123",
"DOI": "10.1073/pnas.2517059123",
"PMID": "41824500",
"PMCID": "PMC12994201",
"ISSN": "0027-8424",
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Sleep reveals dynamics integrating and segregating movement and stimulus representations in V1.
Journal: Nature communications
In common: imageio, Pillow, pandas, 3 other tools, 1 reference

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