@article {28301,
	title = {TCRPower: Quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences},
	journal = {Briefings in Bioinformatics},
	volume = {23},
	year = {2022},
	month = {01/2022},
	pages = {bbab566},
	publisher = {Oxford Academic},
	abstract = {T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases, and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to mis-diagnosis if diagnostically-relevant TCRs remain undetected. To address this issue, we developed TCRPower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth, and read cut-off. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones as spike-in TCRs. We sequenced the spike-in TCRs from T-cell clones, together with TCRs from peripheral blood, using a 5{\textquoteright} RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cut-off, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0\%, TCR β-chain 92.4\%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease relevant TCRs for diagnostic applications.\&nbsp;},
	keywords = {Adaptive Immune Receptor Repertoire sequencing, Bulk T-cell receptor sequencing, computational model, Spike-in standards, T-cell receptor, TCRpower},
	doi = {https://doi.org/10.1093/bib/bbab566},
	author = {Dahal-Koirala, Shiva and Balaban, Gabriel and Ralf Stefan {Neumann} and Scheffer, Lonneke and Knut Erik Aslak {Lundin} and Greiff, Victor and Ludvig Magne {Sollid} and Qiao, Shuo-Wang and Sandve, Geir-Kjetil}
}
@article {28310,
	title = {The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires},
	journal = {Nature Machine Intelligence},
	volume = {3},
	year = {2021},
	month = {Nov/2021},
	pages = {936 - 944},
	publisher = {Nature Publishing},
	doi = {10.1038/s42256-021-00413-z},
	url = {https://www.nature.com/articles/s42256-021-00413-zhttps://www.nature.com/articles/s42256-021-00413-z.pdfhttps://www.nature.com/articles/s42256-021-00413-z.pdfhttps://www.nature.com/articles/s42256-021-00413-z},
	author = {Pavlovi{\'c}, Milena and Scheffer, Lonneke and Motwani, Keshav and Kanduri, Chakravarthi and Kompova, Radmila and Vazov, Nikolay and Waagan, Knut and Fabian L. M. {Bernal} and Alexandre Almeida {Costa} and Corrie, Brian and Akbar, Rahmad and Ghadi S. Al {Hajj} and Balaban, Gabriel and Todd M. {Brusko} and Chernigovskaya, Maria and Christley, Scott and Lindsay G. {Cowell} and Frank, Robert and Grytten, Ivar and Gundersen, Sveinung and Ingrid Hob{\ae}k {Haff} and Hovig, Eivind and Hsieh, Ping-Han and Klambauer, G{\"u}nter and Marieke L. {Kuijjer} and Lund-Andersen, Christin and Martini, Antonio and Minotto, Thomas and Pensar, Johan and Rand, Knut and Riccardi, Enrico and Philippe A. {Robert} and Rocha, Artur and Slabodkin, Andrei and Snapkov, Igor and Ludvig M. {Sollid} and Titov, Dmytro and C{\'e}dric R. {Weber} and Widrich, Michael and Yaari, Gur and Greiff, Victor and Geir Kjetil {Sandve}}
}
@article {27947,
	title = {Late-Gadolinium Enhancement Interface Area and Electrophysiological Simulations Predict Arrhythmic Events in Patients With Nonischemic Dilated Cardiomyopathy},
	journal = {JACC: Clinical Electrophysiology},
	volume = {7},
	year = {2021},
	month = {Jan-02-2021},
	pages = {238 - 249},
	publisher = {Elsevier},
	issn = {2405500X},
	doi = {10.1016/j.jacep.2020.08.036},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2405500X20308240},
	author = {Balaban, Gabriel and Brian P. {Halliday} and Porter, Bradley and Bai, Wenjia and Nyg{\r a}ard, St{\r a}le and Owen, Ruth and Hatipoglu, Suzan and Nuno Dias {Ferreira} and Izgi, Cemil and Tayal, Upasana and Corden, Ben and Ware, James and Dudley J. {Pennell} and Rueckert, Daniel and Plank, Gernot and Christopher A. {Rinaldi} and Sanjay K. {Prasad} and Martin J. {Bishop}}
}
@article {28258,
	title = {Left ventricular shape predicts arrhythmic risk in fibrotic dilated cardiomyopathy},
	journal = {EP Europace},
	year = {2021},
	month = {12/2021},
	pages = {1-11},
	publisher = {European Society of Cardiology},
	abstract = {Aims: Remodelling of the left ventricular (LV) shape is one of the hallmarks of non-ischaemic dilated cardiomyopathy (DCM) and may contribute to ventricular arrhythmias and sudden cardiac death. We sought to investigate a novel three dimensional (3D) shape analysis approach to quantify LV remodelling for arrhythmia prediction in DCM....................................................................................................................................................................................................Methods and resultsWe created 3D LV shape models from end-diastolic cardiac magnetic resonance images of 156 patients with DCM and late gadolinium enhancement (LGE). Using the shape models, principle component analysis, and Cox-Lasso re-gression, we derived a prognostic LV arrhythmic shape (LVAS) score which identified patients who reached a com-posite arrhythmic endpoint of sudden cardiac death, aborted sudden cardiac death, and sustained ventricular tachy-cardia. We also extracted geometrical metrics to look for potential prognostic markers. During a follow-up period of up to 16 years (median 7.7, interquartile range: 3.9), 25 patients met the arrhythmic endpoint. The optimally prognostic LV shape for predicting the time-to arrhythmic event was a paraboloidal longitudinal profile, with a relatively wide base. The corresponding LVAS was associated with arrhythmic events in univariate Cox regression (hazard ratio = 2.0 per quartile; 95\% confidence interval: 1.3{\textendash}2.9), in univariate Cox regression with propensity score adjustment, and in three multivariate models; with LV ejection fraction, New York Heart Association Class III/IV (Model 1), implantable cardioverter-defibrillator receipt (Model 2), and cardiac resynchronization therapy (Model 3)....................................................................................................................................................................................................ConclusionBiomarkers of LV shape remodelling in DCM can help to identify the patients at greatest risk of lethal ventricular arrhythmias.\&nbsp;},
	author = {Balaban, Gabriel and Brian P. {Halliday} and Hammersley, Daniel and Christopher A {Rinaldi} and Sanjay K {Prasad} and Martin J. {Bishop} and Lamata, Pablo}
}
@article {28044,
	title = {T cell receptor repertoire as a potential diagnostic marker for celiac disease},
	journal = {Clinical Immunology},
	volume = {222},
	year = {2021},
	month = {01/2021},
	publisher = {Elsevier},
	abstract = {An individual{\textquoteright}s T cell repertoire is skewed towards some specificities as a result of past antigen exposure and subsequent clonal expansion. Identifying T cell receptor signatures associated with a disease is challenging due to the overall complexity of antigens and polymorphic HLA allotypes. In celiac disease, the antigen epitopes are well characterised and the specific HLA-DQ2-restricted T-cell repertoire associated with the disease has been explored in depth. By investigating T cell receptor repertoires of unsorted lamina propria T cells from 15 individuals, we provide the first proof-of-concept study showing that it could be possible to infer disease state by matching against a priori known disease-associated T cell receptor sequences.},
	author = {Yao, Ying and Zia, Asima and Ralf Stefan {Neumann} and Pavlovic, Milena and Balaban, Gabriel and Knut EA {Lundin} and Geir Kjetil {Sandve} and Qiao, Shuo-Wang}
}
@article {27949,
	title = {Ten simple rules for quick and dirty scientific programming},
	journal = {PLOS Computational Biology},
	volume = {17},
	year = {2021},
	month = {Mar-11-2021},
	pages = {e1008549},
	publisher = {PLOS},
	url = {https://dx.plos.org/10.1371/journal.pcbi.1008549},
	author = {Balaban, Gabriel and Grytten, Ivar and Knut Dagestad {Rand} and Scheffer, Lonneke and Geir Kjetil {Sandve}}
}
@article {27948,
	title = {3D Electrophysiological Modeling of Interstitial Fibrosis Networks and Their Role in Ventricular Arrhythmias in Non-Ischemic Cardiomyopathy},
	journal = {IEEE Transactions on Biomedical Engineering},
	volume = {67},
	year = {2020},
	month = {Jan-11-2020},
	pages = {3125 - 3133},
	publisher = { IEEE},
	issn = {0018-9294},
	doi = {10.1109/TBME.1010.1109/TBME.2020.2976924},
	author = {Balaban, Gabriel and Caroline Mendonca {Costa} and Porter, Bradley and Halliday, Brian and Christopher A. {Rinaldi} and Prasad, Sanjay and Plank, Gernot and Tevfik F {Ismail} and Martin J. {Bishop}}
}
@article {28045,
	title = {A computational investigation into rate-dependant vectorcardiogram changes due to specific fibrosis patterns in non-isch{\ae}mic dilated cardiomyopathy},
	journal = {Computers in Biology and Medicine},
	volume = {123},
	year = {2020},
	month = {Jan-08-2020},
	pages = {103895},
	publisher = {Elsevier},
	issn = {00104825},
	doi = {10.1016/j.compbiomed.2020.103895},
	author = {Philip M. {Gemmell} and Gillette, Karli and Balaban, Gabriel and Rajani, Ronak and Edward J. {Vigmond} and Plank, Gernot and Martin J. {Bishop}}
}
@article {27946,
	title = {Scar shape analysis and simulated electrical instabilities in a non-ischemic dilated cardiomyopathy patient cohort},
	journal = {PLOS Computational Biology},
	year = {2019},
	month = {Apr-10-2021},
	publisher = {PLOS},
	url = {https://dx.plos.org/10.1371/journal.pcbi.100742},
	author = {Balaban, Gabriel and Brian P. {Halliday} and Bai, Wenjia and Porter, Bradley and Malvuccio, Carlotta and Lamata, Pablo and Christopher A. {Rinaldi} and Plank, Gernot and Rueckert, Daniel and Sanjay K. {Prasad} and Martin J. {Bishop}},
	editor = {Andrew D. {McCulloch}}
}
@misc {26382,
	title = {Adjoint Based Data Assimilation for Quantification of Dynamic Mechanical Behavior of the Heart},
	howpublished = {World Congress of Biomechanics, New York, USA},
	year = {2018},
	author = {Wall, Samuel and Finsberg, Henrik and Balaban, Gabriel and Sundnes, Joakim and Lik Chuan {Lee}}
}
@misc {26381,
	title = {Adjoint based data assimilation for quantifying mechanical properties in clinical cardiac mechanics},
	howpublished = {Computer Methods in Biomechanics and Bioengineering, Lisbon, Portugal},
	year = {2018},
	author = {Wall, Samuel and Finsberg, Henrik and Balaban, Gabriel and Lik Chuan {Lee}}
}
@article {27945,
	title = {Fibrosis Microstructure Modulates Reentry in Non-ischemic Dilated Cardiomyopathy: Insights From Imaged Guided 2D Computational Modeling},
	journal = {Frontiers in Physiology},
	number = {1832},
	year = {2018},
	month = {Jul-12-2019},
	publisher = {Frontiers},
	url = {https://www.frontiersin.org/article/10.3389/fphys.2018.01832/},
	author = {Balaban, Gabriel and Brian P. {Halliday} and Caroline Mendonca {Costa} and Bai, Wenjia and Porter, Bradley and Christopher A. {Rinaldi} and Plank, Gernot and Rueckert, Daniel and Sanjay K. {Prasad} and Martin J. {Bishop}}
}
@article {25968,
	title = {In vivo estimation of elastic heterogeneity in an infarcted human heart},
	journal = {Biomechanics and Modeling in Mechanobiology},
	volume = {17},
	year = {2018},
	month = {May-05-2019},
	pages = {1317{\textendash}1329},
	publisher = {Springer},
	address = {Berlin Heidelberg},
	abstract = {In myocardial infarction, muscle tissue of the heart is damaged as a result of ceased or severely impaired blood flow. Survivors have an increased risk of further complications, possibly leading to heart failure. Material properties play an important role in determining post-infarction outcome. Due to spatial variation in scarring, material properties can be expected to vary throughout the tissue of a heart after an infarction. In this study we propose a data assimilation technique that can efficiently estimate heterogeneous elastic material properties in a personalized model of cardiac mechanics. The proposed data assimilation is tested on a clinical dataset consisting of regional left ventricular strains and in vivo pressures during atrial systole from a human with a myocardial infarction. Good matches to regional strains are obtained, and simulated equi-biaxial tests are carried out to demonstrate regional heterogeneities in stress{\textendash}strain relationships. A synthetic data test shows a good match of estimated versus ground truth material parameter fields in the presence of no to low levels of noise. This study is the first to apply adjoint-based data assimilation to the important problem of estimating cardiac elastic heterogeneities in 3-D from medical images.},
	issn = {1617-7959},
	doi = {10.1007/s10237-018-1028-5},
	url = {http://link.springer.com/10.1007/s10237-018-1028-5},
	author = {Balaban, Gabriel and Finsberg, Henrik and Simon W {Funke} and Trine F. {H{\r a}land} and Hopp, Einar and Sundnes, Joakim and Wall, Samuel and Marie E. {Rognes}}
}
@phdthesis {25008,
	title = {Adjoint Data Assimilation Methods for Cardiac Mechanics},
	volume = {PhD},
	year = {2017},
	month = {05.01.2017},
	pages = {119},
	school = {University of Oslo},
	address = {University of Oslo},
	author = {Balaban, Gabriel}
}
@article {25635,
	title = {Estimating cardiac contraction through high resolution data assimilation of a personalized mechanical model},
	journal = {Journal of Computational Science},
	volume = {24},
	year = {2017},
	pages = {85-90},
	publisher = {Elsevier},
	keywords = {Adjoint Method, Cardiac Mechanics, Contractility, Data assimilation, PDE-constrained optimization},
	issn = {1877-7503},
	doi = {10.1016/j.jocs.2017.07.013},
	url = {http://www.sciencedirect.com/science/article/pii/S1877750317308190},
	author = {Finsberg, Henrik and Balaban, Gabriel and Ross, Stian and Trine F {H{\r a}land} and Hans Henrik {Odland} and Sundnes, Joakim and Wall, Samuel}
}
@article {24721,
	title = {High resolution data assimilation of cardiac mechanics},
	journal = {International journal for numerical methods in biomedical engineering},
	volume = {33},
	number = {11},
	year = {2017},
	month = {04/2017},
	pages = {e2863},
	publisher = {John Wiley \& Sons, Ltd},
	doi = {10.1002/cnm.2863},
	author = {Balaban, Gabriel and Finsberg, Henrik and Hans Henrik {Odland} and Marie E. {Rognes} and Ross, Stian and Sundnes, Joakim and Wall, Samuel}
}
@article {24028,
	title = {Adjoint Multi-Start Based Estimation of Cardiac Hyperelastic Material Parameters using Shear Data},
	journal = {Biomechanics and Modeling in Mechanobiology},
	year = {2016},
	month = {23.03.2013},
	pages = {1-13},
	publisher = {Springer},
	author = {Balaban, Gabriel and Martin Sandve {Aln{\ae}s} and Sundnes, Joakim and Marie E. {Rognes}}
}
@inproceedings {24567,
	title = {Optimization of a Spatially Varying Cardiac Contraction parameter using the Adjoint Method},
	journal = {FEniCS{\textquoteright}16},
	year = {2016},
	abstract = {A cardiac computational model is constrained using clinical measurements such as pressure, volume and regional strain. The problem is formulated as a PDE-constrained optimisation problem where the objective functional represents the misfit between measured and simulated data. The control parameter for the active phase is a spatially varying contraction parameter defined at every vertex in the mesh. This makes gradient calculations using the adjoint approach computationally advantageous over standard finite difference approximations.},
	keywords = {Adjoint Method, Cardiac Mechanics, parameter estimation, PDE-constrained optimization},
	author = {Finsberg, Henrik and Balaban, Gabriel and Marie E. {Rognes} and Sundnes, Joakim and Wall, Samuel}
}
@misc {24417,
	title = {Patient Specific Modeling of Cardiac Mechanics using the Active Strain Formulation},
	howpublished = {Geilo Winter School},
	year = {2016},
	author = {Finsberg, Henrik and Balaban, Gabriel and Wall, Samuel and Sundnes, Joakim and Marie E. {Rognes}}
}
@inproceedings {24991,
	title = {Personalized Cardiac Mechanical Model using a High Resolution Contraction Field},
	journal = {VPH16 Translating VPH to the Clinic},
	year = {2016},
	author = {Finsberg, Henrik and Balaban, Gabriel and Sundnes, Joakim and Marie E. {Rognes} and Hans Henrik {Odland} and Ross, Stian and Wall, Samuel}
}
@inproceedings {23665,
	title = {A Computational Method for Determining Left Ventricular Diastolic Fiber Strain Relative to a Zero Pressure Geometry},
	journal = {USART Workshop},
	year = {2015},
	month = {09/2015},
	abstract = {Abnormal cardiac fiber stretching has been shown to berelated to the formation of arrhythmias. In this work weestimate this stretching relative to a pressure free state usinga finite element model that has been calibrated to strain andpressure measurements in late diastole.},
	keywords = {Cardiac Mechanics, Fiber Strain, Optimization},
	author = {Balaban, Gabriel and Wall, Samuel}
}
@misc {23855,
	title = {Dyssynchronous Left Ventricular Stress Estimation},
	howpublished = {Workshop on Advanced Numerical Techniques in Biomedical Computing: Simula Research Laboratory},
	year = {2015},
	abstract = {The heart is a muscular pump that moves approximatly 8,000 litres per day through the human body. It manages to this by a combination of contraction/relaxation along muscle fibers and elastic recoil. These two effects are indistinguishable in a medical image, but can be estimated using a computational model.In our work we use adjoint based optimization techniques in order to determine muscle contraction and elastic recoil in a patient specific left ventricular geometry based on echocardiography and pressure catheter data. Possible applications of the technique include mechanical stress estimation and hypothesis testing in cardiac resynchronization therapy research.},
	author = {Balaban, Gabriel},
	editor = {Finsberg, Henrik and Marie E. {Rognes} and Sundnes, Joakim and Wall, Samuel and Hans Henrik {Odland} and Ross, Stian}
}
@misc {23581,
	title = {Identifying the Parameters of the Heart: Variational Data Assimilation in Cardiac Mechanics Using Dolfin-Adjoint},
	howpublished = {FEniCS {\textquoteright}15, Imperial College London, UK},
	year = {2015},
	type = {Featured presentation},
	author = {Marie E. {Rognes} and Balaban, Gabriel and Sundnes, Joakim and Martin Sandve {Aln{\ae}s}}
}
@misc {24422,
	title = {Optimal Elasticity and Contraction in the Cardiac Cycle},
	howpublished = {Oslo, Simula Research Laboratory},
	year = {2015},
	month = {03/12/2015},
	author = {Balaban, Gabriel}
}
@misc {23474,
	title = {Patient Constrained Ventricular Stress Mapping},
	howpublished = {Lugano Switzerland},
	year = {2015},
	type = {Conference Presentation at MALT 2015},
	abstract = {Abnormal stresses are hypothesized to be a key driver in remodelling processes associated with heart failureHowever, it is currently impossible to measure stresses safely in vivo in a human heart. This necessitates the use of computational modelsin cardiac stress calculation.A key step to making simulated stresses useful for clinical practice is patient specificity. This means that the simulated stresses shouldcome from a computational model that has been calibrated to behave in the same way as a patient{\textquoteright}s heart.Doing this typically involves first creating a patient specific geometry, and then using available clinical data to personalize the mechanics of a computational model.One source of mechanical data that is currently available is dynamic left ventricular strain, which can beobtained cheaply and efficiently using 4D echocardiography methods. In our study, we combine such strain datawith left ventricular pressure and volume measurements in order to match simulated bi-ventricular mechanics to those observed in the ventricles of a patient.We formulate this matching as a mathematical optimization problem in which the least squares difference betweensimulated and measured strains and volumes is minimized. This minimization is carried out using a gradient based optimization algorithm and an automaticallyderived adjoint equation. As a result, we obtain patient specific stress maps that can be usedto improve the treatment of cardiac conditions in which stress plays a significant role.},
	author = {Balaban, Gabriel and Finsberg, Henrik}
}
@inproceedings {23720,
	title = {Personalization of a Cardiac Compuational Model using Clinical Measurements},
	journal = {28th Nordic Seminar on Computational Mechanics},
	volume = {28},
	year = {2015},
	month = {10/2015},
	pages = {47-50},
	publisher = {Proceedings of NSCM-28},
	address = {Tallin, Estonia},
	abstract = {Important features in cardiac mechanics that cannot easily be measured in the clinic, can be computed using a computational model that is calibrated to behave in the same way as a patient{\textquoteright}s heart. To construct such a model, clinical measurements such as strain, volume and cavity pressure are used to personalize the mechanics of a cardiac computational model. The problem is formulated as a PDE-constrained optimization problem where the minimization functional represents the misfit between the measured and simulated data. The target parameters are material parameters and a spatially varying contraction parameter. The minimization is carried out using a gradient based optimization algorithm and an automatically derived adjoint equation. The method has been tested on synthetic data, and is able to reproduce a prescribed contraction pattern on the left ventricle.},
	author = {Finsberg, Henrik and Balaban, Gabriel and Sundnes, Joakim and Marie E. {Rognes} and Wall, Samuel}
}
@inproceedings {Simula.simula.3088,
	title = {Least Squares Fitting of a Cardiac Hyperelasticity Model Using an Automatically Derived Adjoint Equation},
	journal = {Proceedings of NSCM-27: the 27th Nordic Seminar on Computational Mechanics},
	volume = {27},
	year = {2014},
	month = {October},
	pages = {272-275},
	publisher = {KTH Stockholm Royal Institute of Technology},
	type = {Workshop},
	address = {Drottning Kristinas v{\"a}g 53},
	abstract = {A Cardiac hyperelasticity model is successfully calibrated using an adjoint based least squares minimization and synthetic data.},
	keywords = {Conference},
	author = {Balaban, Gabriel and Sundnes, Joakim and Martin Sandve {Aln{\ae}s} and Marie E. {Rognes}},
	editor = {Eriksson, Anders and Kulachenko, Artem and Mihaescu, Mihai and Tibert, Gunnar}
}
@misc {Simula.simula.3030,
	title = {Passive Cardiac Mechanics Calibration Using Reduced Order Unscented Kalman Filtering},
	howpublished = {Presentation at the Uppsala-Simula Scientific Computing Seminar},
	year = {2014},
	month = {September},
	abstract = {In this talk I demonstrate how reduced order unscented Kalman filtering can be used to identify the parameters of a cardiac hyperelasticity model using data from a shearing experiment.},
	author = {Balaban, Gabriel}
}
@misc {Simula.simula.1966,
	title = {Automatic Differentiation of a Fluid-Structure Interaction Problem},
	howpublished = {FEniCS{\textquoteright}13 workshop, Cambridge},
	year = {2013},
	month = {March},
	abstract = {At the FEniCS 13 workshop I presented how to solve a Fluid-Structure interaction problem using automatic differentiation and FEniCS.},
	keywords = {Conference, Internal Seminar, University of Oslo, Workshop},
	author = {Balaban, Gabriel}
}
@misc {Simula.simula.2564,
	title = {Automatic Differentiation of a Fluid-Structure Interaction Problem},
	howpublished = {Visit to Biomedical Simulation Lab, University of Toronto},
	year = {2013},
	month = {August},
	author = {Balaban, Gabriel}
}
@inproceedings {Simula.simula.1786,
	title = {A Newton Method for Fluid-Structure Interaction Using Full Jacobians Based on Automatic Form Differentiation},
	journal = {6th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS 2012},
	year = {2012},
	type = {1},
	author = {Balaban, Gabriel and Logg, Anders and Marie E. {Rognes}},
	editor = {Eberhardsteiner, J.}
}
