Merge pull request #61 from scientificcomputing/more-content2

More content

.cspell_dict.txt

@@ -2,7 +2,9 @@ addopts
 annefou
 arostica
 Astrocyte
+Biobank
 Bippe
+biventricular
 brainpulse
 budisa
 CauseMann
@@ -56,6 +58,7 @@ nforsch
 Nitsche
 numpy
 Oasisx
+odeigah
 oneline
 orcid
 peaceiris
@@ -71,6 +74,7 @@ Ragan
 saetra
 Sætra
 scientificcomputing
+scifem
 Simcardems
 Simula
 Stankelbein

.github/dependabot.yml

@@ -0,0 +1,11 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
+version: 2
+updates:
+  - package-ecosystem: "github-actions" # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"

.pre-commit-config.yaml

@@ -1,13 +1,13 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v5.0.0
     hooks:
       - id: check-yaml
       - id: end-of-file-fixer
       - id: trailing-whitespace
 
   - repo: https://github.com/streetsidesoftware/cspell-cli
-    rev: v6.14.0
+    rev: v8.17.0
     hooks:
       - id: cspell
         files: docs/(.+).md|README.md

docs/packages.md

@@ -4,6 +4,7 @@ A list scientific software (and corresponding publication) developed by personne
 
 
 ## FEniCS
+- Scientific finite element toolbox [scifem](https://github.com/scientificcomputing/scifem)
 - ADIOS4DOLFINx: A framework for checkpointing in FEniCS [ADIOS4DOLFINx](https://github.com/jorgensd/adios4dolfinx/) {cite}`dokken2024adios`
 - Multi-point constraints with DOLFINx: [DOLFINx_MPC](https://github.com/jorgensd/dolfinx_mpc)
 
@@ -16,10 +17,18 @@ A list scientific software (and corresponding publication) developed by personne
 - Surface Volume Meshing ToolKit: [SVMTK](https://github.com/SVMTK/SVMTK) {cite}`valnes2022`
 - Tool for creating idealised cardiac geometries and microstructure in FEniCS: [cardiac-geometries](https://github.com/ComputationalPhysiology/cardiac-geometries)
 - Tool for creating idealised cardiac geometries and microstructure in FEniCSx: [cardiac-geometriesx](https://github.com/ComputationalPhysiology/cardiac-geometriesx)
-
+- A collection of tools for manipulation of morphological features in patient-specific geometries [morphMan](https://github.com/KVSlab/morphMan)
+- Generate meshes from UK Biobank atlas [ukb-atlas](https://github.com/ComputationalPhysiology/ukb-atlas)
 
 ## Fluid Dynamics
 - Next generation Open Source Navier Stokes solver using FEniCSx [oasisx](https://github.com/ComputationalPhysiology/oasisx)
+- A verified and validated Python/FEniCS-based CFD solver for moving domains [OasisMove](https://github.com/KVSlab/OasisMove)
+- A collection of tools for pre-processing, simulating, and post-processing vascular morphologies [VaMPy](https://github.com/KVSlab/VaMPy)
+
+## FSI
+- A collection of tools for pre-processing, simulating, and post-processing vascular fluid-structure-interaction problems [VaSP](https://github.com/KVSlab/VaSP)
+- Monolithic Fluid-Structure Interaction (FSI) solver [turtleFSI](https://github.com/KVSlab/turtleFSI)
+
 
 ## Brain
 - Intracranial Pulsation: [brainpulse](https://github.com/MariusCausemann/intracranialPulsation) {cite}`causemann2022`
@@ -35,7 +44,7 @@ A list scientific software (and corresponding publication) developed by personne
 - `ldrb` - Library for creating rule-based fiber orientations in [FEniCSx](https://github.com/finsberg/fenicsx-ldrb) and [FEniCS](https://github.com/finsberg/ldrb)
 
 ## Other
-- General ODE translator [gotranx](https://github.com/finsberg/gotranx) {cite}`
+- General ODE translator [gotranx](https://github.com/finsberg/gotranx) {cite}`finsberg2024`
 
 
 ## Missing a package?

docs/references.bib

@@ -1,3 +1,18 @@
+@article{10.1063/5.0160334,
+  author        = {Gjerde, I. G. and Rognes, M. E. and S\'{a}nchez, A. L.},
+  title         = {The directional flow generated by peristalsis in perivascular networks--Theoretical and numerical reduced-order descriptions},
+  journal       = {Journal of Applied Physics},
+  volume        = {134},
+  number        = {17},
+  pages         = {174701},
+  year          = {2023},
+  month         = {11},
+  abstract      = {Directional fluid flow in perivascular spaces surrounding cerebral arteries is hypothesized to play a key role in brain solute transport and clearance. While various drivers for a pulsatile flow, such as cardiac or respiratory pulsations, are well quantified, the question remains as to which mechanisms could induce a directional flow within physiological regimes. To address this question, we develop theoretical and numerical reduced-order models to quantify the directional (net) flow induceable by peristaltic pumping in periarterial networks. Each periarterial element is modeled as a slender annular space bounded internally by a circular tube supporting a periodic traveling (peristaltic) wave. Under reasonable assumptions of a small Reynolds number flow, small radii, and small-amplitude peristaltic waves, we use lubrication theory and regular perturbation methods to derive theoretical expressions for the directional net flow and pressure distribution in the perivascular network. The reduced model is used to derive closed-form analytical expressions for the net flow for simple network configurations of interest, including single elements, two elements in tandem, and a three element bifurcation, with results compared with numerical predictions. In particular, we provide a computable theoretical estimate of the net flow induced by peristaltic motion in perivascular networks as a function of physiological parameters, notably, wave length, frequency, amplitude, and perivascular dimensions. Quantifying the maximal net flow for specific physiological regimes, we find that vasomotion may induce net pial periarterial flow velocities on the order of a few to tens of   \ensuremath{\mu}m/s and that sleep-related changes in vasomotion pulsatility may drive a threefold flow increase.},
+  issn          = {0021-8979},
+  doi           = {10.1063/5.0160334},
+  url           = {https://doi.org/10.1063/5.0160334},
+  eprint        = {https://pubs.aip.org/aip/jap/article-pdf/doi/10.1063/5.0160334/18195883/174701\_1\_5.0160334.pdf}
+}
 @article{10.1088/2057-1976/ad7268,
   author        = {Finsberg, Henrik Nicolay Topnes and Charwat, Verena and Healy, Kevin E and Wall, Samuel},
   title         = {Automatic motion estimation with applications to hiPSC-CMs},
@@ -101,6 +116,21 @@ @article{haubner2023
   year          = {2023},
   doi           = {10.1137/21M143114X}
 }
+@article{https://doi.org/10.1002/cnm.2982,
+  author        = {Finsberg, Henrik and Xi, Ce and Tan, Ju Le and Zhong, Liang and Genet, Martin and Sundnes, Joakim and Lee, Lik Chuan and Wall, Samuel T.},
+  title         = {Efficient estimation of personalized biventricular mechanical function employing gradient-based optimization},
+  journal       = {International Journal for Numerical Methods in Biomedical Engineering},
+  volume        = {34},
+  number        = {7},
+  pages         = {e2982},
+  keywords      = {cardiac mechanics, contractility estimation, data assimilation, parameter estimation, patient specific simulations, stress estimation},
+  doi           = {https://doi.org/10.1002/cnm.2982},
+  url           = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cnm.2982},
+  eprint        = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/cnm.2982},
+  note          = {e2982 cnm.2982},
+  abstract      = {Abstract Individually personalized computational models of heart mechanics can be used to estimate important physiological and clinically-relevant quantities that are difficult, if not impossible, to directly measure in the beating heart. Here, we present a novel and efficient framework for creating patient-specific biventricular models using a gradient-based data assimilation method for evaluating regional myocardial contractility and estimating myofiber stress. These simulations can be performed on a regular laptop in less than 2~h and produce excellent fit between measured and simulated volume and strain data through the entire cardiac cycle. By applying the framework using data obtained from 3 healthy human biventricles, we extracted clinically important quantities as well as explored the role of fiber angles on heart function. Our results show that steep fiber angles at the endocardium and epicardium are required to produce simulated motion compatible with measured strain and volume data. We also find that the contraction and subsequent systolic stresses in the right ventricle are significantly lower than that in the left ventricle. Variability of the estimated quantities with respect to both patient data and modeling choices are also found to be low. Because of its high efficiency, this framework may be applicable to modeling of patient specific cardiac mechanics for diagnostic purposes.},
+  year          = {2018}
+}
 @article{laughlin2023smart,
   doi           = {10.21105/joss.05580},
   year          = {2023},
@@ -125,6 +155,15 @@ @article{LUNSONGA202599
   keywords      = {Empagliflozin, Long QT syndrome type 3, Late sodium current, Nav1.5, Arrhythmia, Cardioprotection},
   abstract      = {Background Sodium/glucose cotransporter 2 inhibitors (SGLT2is) like empagliflozin have demonstrated cardioprotective effects in patients with or without diabetes. SGLT2is have been shown to selectively inhibit the late component of cardiac sodium current (late INa). Induction of late INa is the primary mechanism in the pathophysiology of congenital long QT syndrome type 3 (LQT3) gain-of-function mutations in the SCN5A gene encoding Nav1.5. We investigated empagliflozin's effect on late INa in thirteen known LQT3 mutations located in distinct regions of the channel. Methods The whole-cell patch-clamp technique was used to investigate the effect of empagliflozin on late INa in recombinantly expressed Nav1.5 channels containing different LQT3 mutations. Molecular modeling of human Nav1.5 and simulations in a mathematical model of human ventricular myocytes were used to extrapolate our experimental results to excitation-contraction coupling. Results Empagliflozin selectively inhibited late INa in LQT3 mutations in the inactivation gate region of Nav1.5, without affecting peak current or channel kinetics. In contrast, empagliflozin inhibited both peak and late INa in mutations in the S4 voltage-sensing regions, altered channel gating, and slowed recovery from inactivation. Empagliflozin had no effect on late/peak INa or channel kinetics in channels with mutations in the putative empagliflozin binding region. Simulation results predict that empagliflozin may have a desirable therapeutic effect in LQT3 mutations in the inactivation gate region. Conclusions Empagliflozin selectively inhibits late INa, without affecting channel kinetics, in LQT3 mutations in the inactivation gate region. Empagliflozin may thus be a promising precision medicine approach for patients with specific LQT3 mutations.}
 }
+@article{odeigah2024computational,
+  title         = {A computational study of right ventricular mechanics in a rat model of pulmonary arterial hypertension},
+  author        = {Odeigah, Oscar O and Kwan, Ethan D and Garcia, Kristen M and Finsberg, Henrik and Valdez-Jasso, Daniela and Sundnes, Joakim},
+  journal       = {Frontiers in Physiology},
+  volume        = {15},
+  pages         = {1360389},
+  year          = {2024},
+  publisher     = {Frontiers Media SA}
+}
 @unpublished{poulain2022,
   title         = {{Multi-compartmental model of glymphatic clearance of solutes in brain tissue}},
   author        = {Poulain, Alexandre and Riseth, J{{\o}}rgen and Vinje, Vegard},

docs/repositories.md

@@ -1,19 +1,22 @@
 # Papers with code
 A list of repositories used in research in the Scientific Computing Department follows. A link to relevant publications/preprints is referenced.
 
+## 2025
+- [A software benchmark for cardiac elastodynamics](https://github.com/finsberg/cardiac_benchmark) {cite}`arostica2025117485`
 
 ## 2024
-- [A software benchmark for cardiac elastodynamics](https://github.com/finsberg/cardiac_benchmark) {cite}`arostica2025117485`
 - [The sodium/glucose cotransporter 2 inhibitor Empagliflozin inhibits long QT 3 late sodium currents in a mutation specific manner](https://github.com/andygedwards/LQT3-SGLT2i) {cite}`LUNSONGA202599`
 - [An electrodiffusive network model with multicompartmental neurons and synaptic connections](https://github.com/martejulie/electrodiffusive-network-model) {cite}`saetra2024`
 - [Automatic motion estimation with applications to hiPSC-CMs](https://github.com/ComputationalPhysiology/automatic-motion-estimation) {cite}`10.1088/2057-1976/ad7268`
+- [A computational study of right ventricular mechanics in a rat model of pulmonary arterial hypertension](https://github.com/oscarodeigah/rv_pah_project) {cite}`odeigah2024computational`
 
 
 ## 2023
 
 - [Medical image registration using optimal control of a linear hyperbolic transport equation with a DG discretization](https://github.com/JohannesHaubner/mapMRI) {cite}`zapf2023medical`
 - [ffian: Fluid Flow In Astrocyte Networks](https://github.com/martejulie/fluid-flow-in-astrocyte-networks) {cite}`sætra2023`
 - [A novel density based approach for topology optimization of Stokes flow](https://github.com/JohannesHaubner/TopOpt) {cite}`haubner2023`
+- [The directional flow generated by peristalsis in perivascular networks—Theoretical and numerical reduced-order descriptions](https://github.com/scientificcomputing/perivascular-peristalsis) {cite}`10.1063/5.0160334`
 
 
 ## 2022
@@ -25,6 +28,8 @@ A list of repositories used in research in the Scientific Computing Department f
 ## 2021
 - [An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain](https://github.com/CINPLA/edNEGmodel_analysis) {cite}`saetra2021`
 
+## 2018
+- [Efficient estimation of personalized biventricular mechanical function employing gradient-based optimization](https://bitbucket.org/finsberg/efficient-estimation-of-personalized-biventricular-mechanical/src/master/) {cite}`https://doi.org/10.1002/cnm.2982`
 
 
 ## Missing a repository?