Mojtaba Barzegari

The Open and Reproducible Musculoskeletal Imaging Research (ORMIR) community is a scientific community dedicated to promoting openness and reproducibility in musculoskeletal imaging, image processing, and computational modelling. In this perspective paper, we outline the motivations for conducting transparent research and provide practical guidelines to implement it. We start with defining open and reproducible research and describing the benefits and challenges of working transparently. Next, we redefine the outputs of a computational research study as—ideally—a combination of data, code, and a publication, recommend a folder and file structure that reflects these three study outcomes, and describe how to maintain and update such a structure during the study and at study publication. Finally, we emphasize that working in an open and reproducible manner is a learning process and the best way to acquire the necessary competencies is simply to start. Lay summary: The ORMIR community promotes openness and reproducibility in musculoskeletal imaging research. In this perspective paper, we explain why transparency matters and recommend how to conduct a computational study in an open and reproducible manner focusing on its three outputs: data, code, and publication. Finally, we highlight that the best way to learn these practices is simply to start.

Porous electrodes are ubiquitous to electrochemical technologies for energy conversion and storage, where they play a set of critical roles in the performance and cost of the systems. While progress on electrode design has been mostly driven by experimentation which is time- and resource-intensive, predictive design algorithms such as topology optimization have the potential to accelerate and guide the design of porous electrodes. By setting a performance target (e.g. maximizing electrochemical power output, reducing pumping power), the computational framework iterates mathematically over multiple electrode structures to satisfy the target, finding the optimal structure in a predictive manner. Here, we present a high-performance topology optimization framework, integrated with multi-physics computational models of transport processes, to design optimal porous electrodes in two- or three-dimensional space for use in electrochemical flow cells. We find that the algorithms computes electrode geometries that enhance the electrochemical and hydraulic performance by up to 29% and 98%, respectively. The resulting optimized designs were translated into cellular architectures using triply periodic minimal surface (TPMS) structures and fabricated using stereolithography 3D printing to demonstrate the manufacturability of the generated structures. We hope that such framework can inspire manufacturing of porous electrodes and the method can be extended to other electrochemical systems.

In biomaterial-based bone tissue engineering, optimizing scaffold structure and composition remains an active field of research. Additive manufacturing has enabled the production of custom designs in a variety of materials. This study aims to improve the design of calcium-phosphate-based additively manufactured scaffolds, the material of choice in oral bone regeneration, by using a combination of in silico and in vitro tools. Computer models are increasingly used to assist in design optimization by providing a rational way of merging different requirements into a single design. The starting point for this study was an in-house developed in silico model describing the in vitro formation of neotissue, i.e., cells and the extracellular matrix they produced. The level set method was applied to simulate the interface between the neotissue and the void space inside the scaffold pores. In order to calibrate the model, a custom disk-shaped scaffold was produced with prismatic canals of different geometries (circle, hexagon, square, triangle) and inner diameters (0.5 mm, 0.7 mm, 1 mm, 2 mm). The disks were produced with three biomaterials (hydroxyapatite, tricalcium phosphate, and a blend of both). After seeding with skeletal progenitor cells and a cell culture for up to 21 days, the extent of neotissue growth in the disks’ canals was analyzed using fluorescence microscopy. The results clearly demonstrated that in the presence of calcium-phosphate-based materials, the curvature-based growth principle was maintained. Bayesian optimization was used to determine the model parameters for the different biomaterials used. Subsequently, the calibrated model was used to predict neotissue growth in a 3D gyroid structure. The predicted results were in line with the experimentally obtained ones, demonstrating the potential of the calibrated model to be used as a tool in the design and optimization of 3D-printed calcium-phosphate-based biomaterials for bone regeneration.

Advances in additive manufacturing technologies are leading to an increased interest in the design of intricate 3D geometries for applications ranging from aerospace to biomedical engineering. In this paper, we present ASLI (A Simple Lattice Infiller), a cross-platform tool for the generation of cellular solid structures that allows users to provide implicitly defined lattice infills to 3D objects by specifying the desired local unit cell type, size and feature. It is written in C++ and relies on the open-source libraries Mmg and CGAL to handle the implicit domain discretisation. Although developed to design lattice infills for skeletal tissue engineering applications, ASLI can be used for any application that requires the user to provide lattice infills to 3D objects. Its capabilities are shown through a series of examples that demonstrate complex designs can easily be accomplished. The code is published under an open-source license and is available for download at github.com/tpms-lattice/ASLI.

The recent growth of single-cell transcriptomics has turned single-cell RNA sequencing (scRNA-seq) into a near-routine experiment. Breakthroughs in improving scalability have led to the creation of organism-wide transcriptomic datasets, aiming to comprehensively profile the cell types and states within an organism throughout its lifecycle. To date, however, the skeleton remains a majorly underrepresented organ system in organism-wide atlases. Considering how the skeleton not only serves as the central framework of the vertebrate body but is also the home of the hematopoietic niche and a central player in major metabolic and homeostatic processes, this presents a major deficit in current reference atlas projects. To address this issue, we integrated ten separate scRNA-seq datasets containing limb skeletal cells and their developmental precursors, generating an atlas of 133 332 cells. This limb skeletal cell atlas describes cells across the mesenchymal lineage from the induction of the limb to the adult bone and encompasses 39 different cell states. Furthermore, expanding the repertoire of available time points and cell types within a single dataset allowed for more complete analyses of cell-cell communication or in silico perturbation studies. Taken together, we present a missing piece in the current atlas mapping efforts, which will be of value to researchers in the fields of skeletal biology, hematopoiesis, metabolism and regenerative medicine.

A combination of reaction–diffusion models with moving-boundary problems yields a system in which the diffusion (spreading and penetration) and reaction (transformation) evolve the system’s state and geometry over time. These systems can be used in a wide range of engineering applications. In this study, as an example of such a system, the degradation of metallic materials is investigated. A mathematical model is constructed of the diffusion-reaction processes and the movement of corrosion front of a magnesium block floating in a chemical solution. The corresponding parallelized computational model is implemented using the finite element method, and the weak and strong-scaling behaviors of the model are evaluated to analyze the performance and efficiency of the employed high-performance computing techniques.

Pancreatic islets (islets of Langerhans) transplantation is one of the best treatment approaches for the most severe forms of type 1 diabetes, but this needs a proper preparation of the islets in a culturing process. The cultured and encapsulated islets are highly sensitive to hypoxia, a situation in which they lose their vasculature due to the lack of supplied oxygen and cannot