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-`pulse` - Cardiac mechanics solver in [FEniCSx](https://github.com/finsberg/fenicsx-pulse) and [FEnICS](https://github.com/finsberg/pulse)
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-`beat` - Cardiac electrophysiology solver in [FEniCSx](https://github.com/finsberg/fenicsx-beat) and [FEnICS](https://github.com/finsberg/fenics-beat)
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-`ldrb` - Library for creating rule-based fiber orientations in [FEniCSx](https://github.com/finsberg/fenicsx-ldrb) and [FEniCS](https://github.com/finsberg/ldrb)
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## Other
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- General ODE translator [gotranx](https://github.com/finsberg/gotranx) {cite}`
author = {Lynn C. Lunsonga and Mohammad Fatehi and Wentong Long and Amy J. Barr and Brittany Gruber and Arkapravo Chattopadhyay and Khaled Barakat and Andrew G. Edwards and Peter E. Light},
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keywords = {Empagliflozin, Long QT syndrome type 3, Late sodium current, Nav1.5, Arrhythmia, Cardioprotection},
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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.}
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}
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@unpublished{poulain2022,
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title = {{Multi-compartmental model of glymphatic clearance of solutes in brain tissue}},
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author = {Poulain, Alexandre and Riseth, J{{\o}}rgen and Vinje, Vegard},
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# Repositories
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# Papers with code
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A list of repositories used in research in the Scientific Computing Department follows. A link to relevant publications/preprints is referenced.
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## 2024
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-[A software benchmark for cardiac elastodynamics](https://github.com/finsberg/cardiac_benchmark) {cite}`arostica2025117485`
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-[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`
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-[An electrodiffusive network model with multicompartmental neurons and synaptic connections](https://github.com/martejulie/electrodiffusive-network-model) {cite}`saetra2024`
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-[Automatic motion estimation with applications to hiPSC-CMs](https://github.com/ComputationalPhysiology/automatic-motion-estimation) {cite}`10.1088/2057-1976/ad7268`
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## 2023
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-[Medical image registration using optimal control of a linear hyperbolic transport equation with a DG discretization](https://github.com/JohannesHaubner/mapMRI) {cite}`zapf2023medical`
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-[ffian: Fluid Flow In Astrocyte Networks](https://github.com/martejulie/fluid-flow-in-astrocyte-networks) {cite}`sætra2023`
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-[A novel density based approach for topology optimization of Stokes flow](https://github.com/JohannesHaubner/TopOpt) {cite}`haubner2023`
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## 2022
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-[Multi-compartmental model of glymphatic clearance of solutes in brain tissue](https://github.com/jorgenriseth/multicompartment-solute-transport) {cite}`poulain2022`
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-[Geometrically reduced modelling of pulsatile flow in perivascular networks](https://github.com/cdaversin/geometrically-reduced-PVS-flow) {cite}`daversin2022`
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-[Nitsche method for solving Navier-Stokes with slip boundary conditions](https://github.com/IngeborgGjerde/nitsche-method-for-navier-stokes-with-slip) {cite}`gjerde2022`
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-[A novel density based approach for topology optimization of Stokes flow](https://github.com/JohannesHaubner/TopOpt) {cite}`haubner2021`
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-[An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain](https://github.com/CINPLA/edNEGmodel_analysis) {cite}`saetra2021`
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-[HAZniCS - Software Components for Multiphysics Problems](https://github.com/anabudisa/HAZniCS-examples) {cite}`budisa2022`
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-[Medical image registration using optimal control of a linear hyperbolic transport equation with a DG discretization](https://github.com/JohannesHaubner/mapMRI) {cite}`zapf2023medical`
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-[ffian: Fluid Flow In Astrocyte Networks](https://github.com/martejulie/fluid-flow-in-astrocyte-networks) {cite}`sætra2023`
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-[An electrodiffusive network model with multicompartmental neurons and synaptic connections](https://github.com/martejulie/electrodiffusive-network-model) {cite}`saetra2024`
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-[Automatic motion estimation with applications to hiPSC-CMs](https://github.com/ComputationalPhysiology/automatic-motion-estimation)`cite`{10.1088/2057-1976/ad7268}
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-[A software benchmark for cardiac elastodynamics](https://github.com/finsberg/cardiac_benchmark) {cite}`arostica2025117485`
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## 2021
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-[An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain](https://github.com/CINPLA/edNEGmodel_analysis) {cite}`saetra2021`
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## Missing a repository?
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If you repository is missing from the list, go to [Add new repository](https://github.com/scientificcomputing/scientificcomputing.github.io/issues/new?assignees=&labels=new-repo&template=repository.yml&title=%5BAdd+repo%5D%3A+)
There are a few very good online guides on reproducible research.
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Perhaps the most comprehensive one is[The Turing way](https://the-turing-way.netlify.app/welcome.html) from the [Alan Turing institute](https://github.com/alan-turing-institute). This page contains information and guidelines about most of the things you need to know about reproducible research and we encourage everyone to skim through this site. They also have a [template repo](https://github.com/alan-turing-institute/reproducible-project-template) that might be relevant.
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-[The Turing way](https://the-turing-way.netlify.app/welcome.html) from the [Alan Turing institute](https://github.com/alan-turing-institute). This page contains information and guidelines about most of the things you need to know about reproducible research and we encourage everyone to skim through this site. They also have a [template repo](https://github.com/alan-turing-institute/reproducible-project-template) that might be relevant.
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Another relevant project is [Papers with code](https://github.com/paperswithcode) which is probably more targeted machine learning projects. The also have a pretty comprehensive guide at https://github.com/paperswithcode/releasing-research-code.
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-[Scientific Python](https://learn.scientific-python.org/development/) is a guide on how to develop scientific software in Python. This guide is a bit more technical than the Turing way and is more focused on the software development part.
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-[CodeRefinery](https://coderefinery.org/) is a project that aims to teach researchers how to write better code. They have a lot of good resources and workshops that you can attend.
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-[Sigma2 training](https://www.sigma2.no/training-and-events) is a Norwegian project that provides training in high performance computing and data management. They have links to a lot of good resources.
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-[Papers with code](https://github.com/paperswithcode) which is probably more targeted machine learning projects. The also have a pretty comprehensive guide at https://github.com/paperswithcode/releasing-research-code.
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Getting inspiration from other project is often a good way. Papers with code also has a [web page](https://paperswithcode.com) where you can search for papers with code. For example you, could try to [search for FEniCS](https://paperswithcode.com/search?q_meta=&q_type=&q=FEniCS) and you will get a list of projects where FEniCS is mentioned.
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