Interactive Jupyter Notebooks Research Articles

Representing DNA for machine learning algorithms: A primer on one-hot, binary, and integer encodings.

This short paper presents an educational approach to teaching three popular methods for encoding DNA sequences: one-hot encoding, binary encoding, and integer encoding. Aimed at bioinformatics and computational biology students, our learning intervention focuses on developing practical skills in implementing these essential techniques for efficient representation and analysis of genetic data. The primary goal of this study is to enhance students' understanding and practical application of DNA encoding methods, which are crucial for various computational analyses in bioinformatics. Our intervention consists of three key components: (1) a conceptual framework that contextualizes these encoding methods within broader bioinformatics applications, (2) an interactive Jupyter Notebook with Python code examples (https://github.com/yashmgupta/Representing-DNA/tree/main), and (3) a user-friendly Streamlit application for visualizing encoded sequences (https://dnaencoding.streamlit.app/) that also enables students to input their own DNA sequences and visualize the different encoding methods, further enhancing their understanding and practical experience. By combining conceptual overview with practical coding and visualization tools, our approach provides a comprehensive foundation for students to leverage these key DNA sequence encoding methods in their future work. This study contributes to bioinformatics education by offering effective, hands-on learning resources that bridge the gap between theoretical knowledge and practical application in DNA sequence analysis, preparing students for advanced research and data analysis projects in the field.

Melodia: A Python Library for Protein Structure Analysis.

Analysing protein structure similarities is an important step in protein engineering and drug discovery. Methodologies that are more advanced than simple RMSD are available but often require extensive mathematical or computational knowledge for implementation. Grouping and optimising such tools in an efficient open-source library increases accessibility and encourages the adoption of more advanced metrics. Melodia is a Python library with a complete set of components devised for describing, comparing and analysing the shape of protein structures using differential geometry of three-dimensional curves and knot theory. It can generate robust geometric descriptors for thousands of shapes in just a few minutes. Those descriptors are more sensitive to structural feature variation than RMSD deviation. Melodia also incorporates sequence structural annotation and three-dimensional visualisations. Melodia is an open-source Python library freely available on https://github.com/rwmontalvao/Melodia_py, along with interactive Jupyter Notebook tutorials. Supplementary data are available at Bioinformatics online.

Using interactive Jupyter Notebooks and BioConda for FAIR and reproducible biomolecular simulation workflows.

Interactive Jupyter Notebooks in combination with Conda environments can be used to generate FAIR (Findable, Accessible, Interoperable and Reusable/Reproducible) biomolecular simulation workflows. The interactive programming code accompanied by documentation and the possibility to inspect intermediate results with versatile graphical charts and data visualization is very helpful, especially in iterative processes, where parameters might be adjusted to a particular system of interest. This work presents a collection of FAIR notebooks covering various areas of the biomolecular simulation field, such as molecular dynamics (MD), protein-ligand docking, molecular checking/modeling, molecular interactions, and free energy perturbations. Workflows can be launched with myBinder or easily installed in a local system. The collection of notebooks aims to provide a compilation of demonstration workflows, and it is continuously updated and expanded with examples using new methodologies and tools.

The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python

Abstract. Plankton community modeling is a critical tool for understanding the processes that shape marine ecosystems and their impacts on global biogeochemical cycles. These models can be of variable ecological, physiological, and physical complexity. Many published models are either not publicly available or implemented in static and inflexible code, thus hampering adoption, collaboration, and reproducibility of results. Here we present Phydra, an open-source library for plankton community modeling, and Xarray-simlab-ODE (XSO), a modular framework for efficient, flexible, and reproducible model development based on ordinary differential equations. Both tools are written in Python. Phydra provides pre-built models and model components that can be modified and assembled to develop plankton community models of various levels of ecological complexity. The components can be created, adapted, and modified using standard variable types provided by the XSO framework. XSO is embedded in the Python scientific ecosystem and is integrated with tools for data analysis and visualization. To demonstrate the range of applicability and how Phydra and XSO can be used to develop and execute models, we present three applications: (1) a highly simplified nutrient–phytoplankton (NP) model in a chemostat setting, (2) a nutrient–phytoplankton–zooplankton–detritus (NPZD) model in a zero-dimensional pelagic ocean setting, and (3) a size-structured plankton community model that resolves 50 phytoplankton and 50 zooplankton size classes with functional traits determined by allometric relationships. The applications presented here are available as interactive Jupyter notebooks and can be used by the scientific community to build, modify, and run plankton community models based on differential equations for a diverse range of scientific pursuits.

Quantitative Analysis of Paleomagnetic Sampling Strategies

AbstractSampling strategies used in paleomagnetic studies play a crucial role in dictating the accuracy of our estimates of properties of the ancient geomagnetic field. However, there has been little quantitative analysis of optimal paleomagnetic sampling strategies and the community has instead defaulted to traditional practices that vary between laboratories. In this paper, we quantitatively evaluate the accuracy of alternative paleomagnetic sampling strategies through numerical experiments and an associated analytical framework. Our findings demonstrate a strong correspondence between the accuracy of an estimated paleopole position and the number of sites or independent readings of the time‐varying paleomagnetic field, whereas larger numbers of in‐site samples have a dwindling effect. This remains true even when a large proportion of the sample directions are spurious. This approach can be readily achieved in sedimentary sequences by distributing samples stratigraphically, considering each sample as an individual site. However, where the number of potential independent sites is inherently limited the collection of additional in‐site samples can improve the accuracy of the paleopole estimate (although with diminishing returns with increasing samples per site). Where an estimate of the magnitude of paleosecular variation is sought, multiple in‐site samples should be taken, but the optimal number is dependent on the expected fraction of outliers. The use of filters based on angular distance helps the accuracy of paleopole estimation, but leads to inaccurate estimates of paleosecular variation. We provide both analytical formulas and a series of interactive Jupyter notebooks allowing optimal sampling strategies to be developed from user‐informed expectations.

Moisture absorption of 3D-printed PETG plastic samples

Introduction. The additive manufacturing method implies the emergence of emergent properties in the final product, not inherent in the original elements of the system individually. Performance properties of products obtained by FDM-printing are defined not only by the material properties, but also by printing parameters — nozzle and table temperature, layer thickness, printing speed, the direction of laying layers, their relative positioning, etc. Thus, when designing 3D-printed polymer products with the required characteristics one should consider the material – printing parameters system together. The results of the study of the effect of sorption characteristics of 3D-printed PETG plastic samples made by FDM-printing on their elastic-strength properties are presented.
 
 Materials and methods. Three groups of FDM-printed PETG 3D specimens were studied. Collection, preprocessing, analysis, statistical processing and visualization of the data were performed using Python programming language in an interactive Jupyter Notebook development environment.
 
 Results. It was found that the moisture content of 3D-printed polymer samples could be conventionally divided into the superstructural and microstructural levels. A comparison of moisture content limits in different moisture saturation conditions shows that the former exceeds the latter by 2 to 6 times depending on the specimen printing parameters. Moisture content of superstructure level has no statistically significant (for α = 0.01) effect on the ultimate tensile strength of the samples, regardless of the printing parameters of the samples. The moisture sorbed by the level of substructure presumably can act as a stress concentrator preventing the free flow of specimens beyond the ultimate tensile strength, which is reflected in the reduction of elongation at rupture.
 
 Conclusions. The obtained results allow taking into account the influence of moisture state on the elastic-strength properties of 3D printed articles and structures on the basis of PETG-plastics. This, in its turn, contributes to more accurate prediction of their behavior under real operating conditions.

Teaching Algorithms Design Approaches via Interactive Jupyter Notebooks

Interactive learning environments play a crucial role in facilitating effective education. Traditional approaches to teaching algorithm design techniques often lack interactivity, resulting in a limited learning experience. In this article, we propose the utilization of Jupyter Notebooks, renowned for their versatility in combining code, visualizations, and explanations, as a powerful tool for enhancing understanding and promoting an engaging learning experience in teaching algorithm design techniques. We provide a comprehensive structure for a Jupyter Notebook page, encompassing problem descriptions, brute force solutions, algorithm design techniques, application areas, and references, to present a thorough solution for computer science problems using these design techniques. Our study includes experiments conducted with computer science students, demonstrating the practical application of Jupyter Notebooks in algorithm design education. Furthermore, we share sample Jupyter Notebooks for popular algorithm design techniques, including Divide & Conquer, Greedy, Dynamic Programming, and Backtracking. Importantly, we emphasize the significance of interactive comparisons between brute force solutions and algorithm design techniques, which foster valuable learning opportunities by providing insights into performance improvements, complexity analysis, validation, optimization strategies, trade-offs, and a deeper understanding of algorithmic principles. In conclusion, we propose the integration of Jupyter Notebooks as a potent tool for teaching algorithm design approaches, empowering learners to actively engage with the material, visualize complex concepts, and collaborate effectively. By incorporating Jupyter Notebooks into algorithm design education, instructors can enhance students' comprehension, cultivate critical thinking skills, and facilitate a profound understanding of algorithmic principles and optimization strategies.

Automated detection of propagating cracks in RC beams without shear reinforcement based on DIC-controlled modeling of damage localization

In spite of an extensive research in the past few decades, design of beams without shear reinforcement is still considered to be a challenging task. With the goal of developing realistic and simple engineering models, many researchers have contributed to the development of analytical and numerical approaches that have improved the understanding of the mechanical background of shear fracture. Still, some assumptions in these approaches, such as the rigid body kinematics which relies on the assumption of a uniquely identifiable center of rotation, have raised critical questions in the scientific community that need further consideration. In this paper, an automated crack detection scheme based on DIC-controlled modeling of damage localization is developed and presented. This scheme enables an automatic evaluation of opening and sliding profiles along the crack ligament, the center of rotation between two neighboring concrete teeth, and the moment–rotation curve directly from the DIC measurements of the surface displacement. To demonstrate the feasibility and the features of the developed scheme, verification studies are performed using tests performed on longitudinally reinforced concrete beams. The description of the mathematical concepts behind the developed crack detection scheme is accompanied with the explanation of the implementation method using modern Python packages for scientific computing, providing code snippets that show a straightforward translation of the mathematics into an executable code. Furthermore, a publicly available implementation of the developed scheme is provided in the form of an interactive Jupyter notebook.

Coupled sliding–decohesion–compression model for a consistent description of monotonic and fatigue behavior of material interfaces

A thermodynamically consistent model proposed in this work introduces a generative symbolic framework for computational components, which can capture the inelastic behavior of interfaces with 3D kinematics in response to monotonic, cyclic, and fatigue loading histories. It contributes to a long list of existing cohesive zone models by introducing novel forms of potentials for free energy, threshold function, and dissipation to deliver the following model properties: (i) the evolution equations derived from the potentials are linked in such a way that damage develops along with the cumulative measure of plastic sliding, (ii) the convex, continuous, and smooth shape of the threshold function introduced in the effective stress domain enables a general and efficient time integration that automatically handles unloading and reloading, and (iii) the non-associative flow potential includes a scalable degree of coupling between normal and tangential damage evolution. The studies performed in the paper demonstrate that these model ingredients lead to a consistent coverage of arbitrary non-proportional loading scenarios in tangential and normal directions. For monotonic loading, the presented examples illustrate the ability of the model to reproduce the shear dilatancy, pressure sensitivity including the vertex effect, stiffness recovery in compression, and abrasion due to sliding. For cyclic fatigue loading, the studies show that the model reflects the link between the hysteretic behavior and the fatigue damage evolution. This link constitutes the basis for a physically sound description of fatigue degradation along material interfaces. It manifests itself in the presented energy breakdown with distinguished fractions of energy release due to damage and plasticity ascribed to normal and tangential directions. Two calibration and validation examples using pull-out tests exposed to monotonic and cyclic loading are provided to show the applicability of the model for the characterization of the bond between steel bars and concrete. A publicly available implementation using a computer algebra system (CAS) is provided in the form of an interactive Jupyter notebook to document the reproducibility of the results.

Introduction to in silico synthesis of polymers via PySIMM [Article v1.0

Pysimm is a framework for molecular simulations of polymers and polymer-based nanostructures, which enables their direct chemical synthesis and preparation. Pysimm facilitates the understanding of novel, amorphous, processable materials for a broad range of applications, including heterogeneous catalysts, adsorbents and gas storage materials, as well as protein-polymer conjugates. This tutorial provides a detailed guide on the construction of atomistic and united-atom models of polymers using Pysimm: an open-source Python Application Programming Interface for atomistic molecular simulations. The API complements and simplifies the work of widely known molecular simulation software, such as LAMMPS, CASSANDRA, NAMD and Amber. Readers should be familiar with the basic concepts of atomistic molecular simulations, as well as the basic knowledge of Python programming language, before attempting to follow this tutorial. This work is separated into 3 main sections. First, the process of building an atomic-level model of a polymer chain from its repetitive units is described. The second section shows how to work with existing forcefields, and how Pysimm can automatically read, recognize, and assign appropriate Force Field parameters to a molecule. The final section discusses how to use Pysimm to construct polymer chains with pre-specified tacticity. The section is also available in the form of an interactive Jupyter notebook tutorial outlining simple guidelines to construct polymer models.

Biobox: a toolbox for biomolecular modelling.

MotivationThe implementation of biomolecular modelling methods and analyses can be cumbersome, often carried out with in-house software reimplementing common tasks, and requiring the integration of diverse software libraries.ResultsWe present Biobox, a Python-based toolbox facilitating the implementation of biomolecular modelling methods.Availability and implementationBiobox is freely available on https://github.com/degiacom/biobox, along with its API and interactive Jupyter notebook tutorials.

An open-source package with interactive Jupyter Notebooks to enhance the accessibility of reservoir operations simulation and optimisation

In this paper we present the interactive Reservoir Operations Notebooks and Software (iRONS) toolbox for reservoir modelling and optimisation. The toolbox is meant to serve the research and professional community in hydrology and water resource management and contribute to bridge the gaps between them. iRONS is composed of a package of Python core functions and a set of interactive Jupyter Notebooks. Core functions implement typical reservoir modelling tasks and the interactive Jupyter Notebooks illustrate, with practical examples, the key functionalities of iRONS. We describe our development philosophy, the key features of iRONS, and report some results of evaluating the effectiveness of interactive Jupyter Notebooks for training and knowledge transfer. The paper may be of interest also beyond the water resources management field, as an example of how Jupyter Notebooks and interactive visualisation help improving the documentation and sharing of open-source code and the communication of underpinning methodologies.

An Interactive Jupyter Notebook for Teaching the Foundational Mathematical Basis of Compartmental Pharmacokinetics

Pharmacokinetics is often taught as two different lessons, the foundational mathematical basis and the clinical application. Due to the lack of hands-on exercises, pharmacokinetics can be difficult to teach and students report struggling with the topic. Therefore, I have developed an interactive Jupyter notebook that demonstrates the mathematical basis behind one- and two-compartment pharmacokinetic models. This notebook uses a combination of “markup” cells, which display relevant equations and text, and “code” cells, which hold Python code that executes, models, and graphs the various pharmacokinetic equations. Students benefit from exposure to this aspect of computer science because the code remains visible. With the code pre-written, students are able to see the implementations of programming in drug pharmacokinetic modeling without having to construct the code themselves. This notebook includes both the differential and algebraic versions of one- and two- compartment models. The code also generates interactive graphs where students can use sliders for each parameter (half-life, inter-compartmental distribution rates, and dose of drug) to explore the effects of the equations parameters on the output of drug concentration over time. The conversion between macro constants and micro constants is also demonstrated and explained. All software used is freely available, so students can download the notebook onto their personal computers. No computer science experience is needed to execute this tool. However, instructors can easily adapt the notebook to their particular needs, adding or removing cells as desired. I successfully used this notebook in my undergraduate upper-division biology elective, “Foundations of Pharmacology”, and in a guest lectured Master's level course on the practical application of pharmacokinetics. Overall, this tool will be useful for teaching the mathematical basis of basic pharmacokinetics while allowing students to gain useful exposure to computer data science.

Cloud enabling educational platforms with corc

In this paper, it is shown how teaching platforms at educational institutions can utilize cloud platforms to scale a particular service, or gain access to compute instances with accelerator capability such as GPUs. Specifically at the University of Copenhagen (UCPH), it is demonstrated how the internal JupyterHub service, named Data Analysis Gateway (DAG), could utilize compute resources in the Oracle Cloud Infrastructure (OCI). This is achieved by utilizing the introduced Cloud Orchestrator (corc) framework, in conjunction with the novel JupyterHub spawner named MultipleSpawner. Through this combination, we are able to dynamically orchestrate, authenticate, configure, and access interactive Jupyter Notebooks in the OCI with user defined hardware capabilities. These capabilities include settings such as the minimum amount of CPU cores, memory and GPUs the particular orchestrated resources must have. This enables teachers and students at educational institutions such as UCPH to gain easy access to the required capabilities for a particular course. In addition, we lay out how this groundwork, will enable us to establish a Grid of Clouds between multiple trusted institutions. This enables the exchange of surplus computational resources that could be employed across their organisational boundaries.

Three-dimensional paganica fault morphology obtained from hypocenter clustering (L'Aquila 2009 seismic sequence, Central Italy)

In seismic modelling, fault planes are normally assumed to be flat due to the lack of data which can constrain fault morphology. However, incorporating 3D fault morphology is important for modelling several phenomena, for example calculating mainshock induced stress changes. We utilize a data-analytical method to unveil the 3D rupture morphology of faults using unsupervised clustering techniques applied to earthquake hypocenters in seismic sequences. We apply this method to the 2009 L'Aquila seismic sequence which involved a MW 6.1 mainshock on April 6th. We use a dataset of about 50,000 relocated events, mostly microearthquakes, reaching magnitude of completeness equal to 0.7. Clustering distinguishes the earthquakes as occurring in three main clusters along with other minor fault segments. We then represent the morphology of the main Paganica fault system (responsible for the largest mainshock) using splines. This method shows promise as a step toward robustly and quickly obtaining 3D rupture morphologies where earthquake sequences have been monitored. The 3D model is presented interactively online, and the processing is presented in an interactive Jupyter Notebook (https://bit.ly/2MnCFdj).

Numerical Approximation of Port-Hamiltonian Systems for Hyperbolic or Parabolic PDEs with Boundary Control

We consider the design of structure-preserving discretization methods for the solution of systems of boundary controlled Partial Differential Equations (PDEs) thanks to the port-Hamiltonian formalism. We first provide a novel general structure of infinite-dimensional port-Hamiltonian systems (pHs) for which the Partitioned Finite Element Method (PFEM) straightforwardly applies. The proposed strategy is applied to abstract multidimensional linear hyperbolic and parabolic systems of PDEs. Then we show that instructional model problems based on the wave equation, Mindlin equation and heat equation fit within this unified framework. Secondly, we introduce the ongoing project SCRIMP (Simulation and Control of Interactions in Multi-Physics) developed for the numerical simulation of infinite-dimensional pHs. SCRIMP notably relies on the FEniCS open-source computing platform for the finite element spatial discretization. Finally, we illustrate how to solve the considered model problems within this framework by carefully explaining the methodology. As additional support, companion interactive Jupyter notebooks are available.

Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning

Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. The analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx

Computational Verification of Large Logical Models-Application to the Prediction of T Cell Response to Checkpoint Inhibitors.

At the crossroad between biology and mathematical modeling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient dynamical analysis methods. Here, we propose the use of two approaches for the development and analysis of complex cellular network models. A first approach, called "model verification" and inspired by unitary testing in software development, enables the formalization and automated verification of validation criteria for whole models or selected sub-parts. When combined with efficient analysis methods, this approach is suitable for continuous testing, thereby greatly facilitating model development. A second approach, called "value propagation," enables efficient analytical computation of the impact of specific environmental or genetic conditions on the dynamical behavior of some models. We apply these two approaches to the delineation and the analysis of a comprehensive model for T cell activation, taking into account CTLA4 and PD-1 checkpoint inhibitory pathways. While model verification greatly eases the delineation of logical rules complying with a set of dynamical specifications, propagation provides interesting insights into the different potential of CTLA4 and PD-1 immunotherapies. Both methods are implemented and made available in the all-inclusive CoLoMoTo Docker image, while the different steps of the model analysis are fully reported in two companion interactive jupyter notebooks, thereby ensuring the reproduction of our results.

Sun tracking by heliostats with arbitrary orientation of primary and secondary axes

The tracking of heliostats with arbitrary orientation of the primary and secondary axes is considered, and the explicit formulas are derived to find the tracking angles for a given position of the sun. It is shown that the inverse kinematics problem leads to a quadratic equation and has two solutions in general case. The solutions can be obtained in vector, matrix or quaternionic form. The advantages of the particular forms and possible applications are discussed. The corrections due to offset between tracking axes and mirror facets are also considered. The paper is accompanied with a Python library, which shows how to implement the tracking algorithms, and an interactive Jupyter notebook.

Estimating variances in time series kriging using convex optimization and empirical BLUPs

We propose a two-stage estimation method of variance components in time series models known as FDSLRMs, whose observations can be described by a linear mixed model (LMM). We based estimating variances, fundamental quantities in a time series forecasting approach called kriging, on the empirical (plug-in) best linear unbiased predictions of unobservable random components in FDSLRM. The method, providing invariant non-negative quadratic estimators, can be used for any absolutely continuous probability distribution of time series data. As a result of applying the convex optimization and the LMM methodology, we resolved two problems $-$ theoretical existence and equivalence between least squares estimators, non-negative (M)DOOLSE, and maximum likelihood estimators, (RE)MLE, as possible starting points of our method and a practical lack of computational implementation for FDSLRM. As for computing (RE)MLE in the case of $ n $ observed time series values, we also discovered a new algorithm of order $\mathcal{O}(n)$, which at the default precision is $10^7$ times more accurate and $n^2$ times faster than the best current Python(or R)-based computational packages, namely CVXPY, CVXR, nlme, sommer and mixed. We illustrate our results on three real data sets $-$ electricity consumption, tourism and cyber security $-$ which are easily available, reproducible, sharable and modifiable in the form of interactive Jupyter notebooks.