Introduction to Scientific Programming with Python

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies

The effect of a failure and submaximal blood flow restriction resistance exercise protocol on changes in muscle size, strength and swelling

Masteroppgave i idrettsvitenskap - Universitetet i Agder 2016Introduction: Blood flow restricted resistance exercise (BFRRE) can induce rapid increases in muscle size, strength and swelling. No previous research has investigated the importance of conducting BFRRE to voluntary failure and few studies has been carried out to investigate associations between swelling and muscle size. Therefore, the aim of the present study was twofold (1) compare changes in muscle size and strength between a failure (FA) and submaximal (SU) BFRRE protocol (2) investigate associations between swelling and muscle size. Methods: Seventeen untrained men had their legs randomized to FA and SU protocols. The intervention consisted of two training periods including seven BFRRE sessions within five days (separated with 10 days’ rest) using unilateral knee extension at 20% of one repetition maximum (1RM) (30 s rest between sets). Swelling and muscle size was measured with ultrasound, whereas strength was measured as 1RM and maximal voluntary contraction (MVC). Results: Cross-sectional area (CSA) of rectus femoris increased significantly in both groups compared to baseline (FA: 7.9 ± 7.6%; p < 0.001 and SU: 9.1 ± 10.8%; p = 0.003), where no differences in muscle size were observed between groups. Strength (1RM) increased significantly in both groups (FA: 9±8%; p < 0.001 and SU: 11±7%; p < 0.001) at 24 days’ post intervention, whereas no group differences were found. Swelling increased CSA of rectus femoris (12.0±9.72%, p<0.001) compared to ultrasound measurement obtained right before BFRRE. Conclusion: FA and SU induced similar gains in muscle size and strength. Acute swelling increased, whereas no associations was observed between swelling and muscle size Keywords: ultrasound, blood flow restriction resistance exercise, concentric failure, submaximal, muscle thickness, cross-sectional area, swellin

Physiological accuracy in simulating refractory cardiac tissue: the volume-averaged bidomain model vs. the cell-based EMI model

The refractory period of cardiac tissue can be quantitatively described using strength-interval (SI) curves. The information captured in SI curves is pertinent to the design of anti-arrhythmic devices including pacemakers and implantable cardioverter defibrillators. As computational cardiac modelling becomes more prevalent, it is feasible to consider the generation of computationally derived SI curves as a supplement or precursor to curves that are experimentally derived. It is beneficial, therefore, to examine the profiles of the SI curves produced by different cardiac tissue models to determine whether some models capture the refractory period more accurately than others. In this study, we compare the unipolar SI curves of two tissue models: the current state-of-the-art bidomain model and the recently developed extracellular-membrane-intracellular (EMI) model. The EMI model's resolution of individual cell structure makes it a more detailed model than the bidomain model, which forgoes the structure of individual cardiac cells in favour of treating them homogeneously as a continuum. We find that the resulting SI curves elucidate differences between the models, including that the behaviour of the EMI model is noticeably closer to the refractory behaviour of experimental data compared to that of the bidomain model. These results hold implications for future computational pacemaker simulations and shed light on the predicted refractory properties of cardiac tissue from each model.Comment: 30 pages, 12 figures, 3 table

Abnormal Tissue Zone Detection and Average Active Stress Estimation in Patients with LV Dysfunction

Detection of regional ventricular dysfunction is a challenging problem. This study presents an efficient method based on ultrasound (US) imaging and finite element (FE) analysis, for detecting akinetic and dyskinetic regions in the left ventricle (LV). The underlying hypothesis is that the contraction of a healthy LV is approximately homogeneous. Therefore, any deviations between the image-based measured deformation and a homogeneous contraction FE model should correspond to a pathological region. The method was first successfully applied to synthetic data simulating an acute ischemia; it demonstrated that the pathological areas were revealed with a higher contrast than those observed directly in the deformation maps. The technique was then applied to a cohort of eight left bundle branch block (LBBB) patients. For this group, the heterogeneities were significantly less pronounced than those revealed for the synthetic cases but the method was still able to identify the abnormal regions of the LV. This study indicated the potential clinical utility of the method by its simplicity in a patient-specific context and its ability to quickly identify various heterogeneities in LV function. Further studies are required to determine the model accuracy in other pathologies and to investigate its robustness to noise and image artifacts

Computational Modeling of Electrophysiology and Pharmacotherapy of Atrial Fibrillation: Recent Advances and Future Challenges

The pathophysiology of atrial fibrillation (AF) is broad, with components related to the unique and diverse cellular electrophysiology of atrial myocytes, structural complexity, and heterogeneity of atrial tissue, and pronounced disease-associated remodeling of both cells and tissue. A major challenge for rational design of AF therapy, particularly pharmacotherapy, is integrating these multiscale characteristics to identify approaches that are both efficacious and independent of ventricular contraindications. Computational modeling has long been touted as a basis for achieving such integration in a rapid, economical, and scalable manner. However, computational pipelines for AF-specific drug screening are in their infancy, and while the field is progressing quite rapidly, major challenges remain before computational approaches can fill the role of workhorse in rational design of AF pharmacotherapies. In this review, we briefly detail the unique aspects of AF pathophysiology that determine requirements for compounds targeting AF rhythm control, with emphasis on delimiting mechanisms that promote AF triggers from those providing substrate or supporting reentry. We then describe modeling approaches that have been used to assess the outcomes of drugs acting on established AF targets, as well as on novel promising targets including the ultra-rapidly activating delayed rectifier potassium current, the acetylcholine-activated potassium current and the small conductance calcium-activated potassium channel. Finally, we describe how heterogeneity and variability are being incorporated into AF-specific models, and how these approaches are yielding novel insights into the basic physiology of disease, as well as aiding identification of the important molecular players in the complex AF etiology