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Tissue morphogenesis requires the collective, coordinated motion and deformation of a large number of cells. Vertex model simulations for tissue mechanics have been developed to bridge the scales between force generation at the cellular level and tissue deformation and flows. We review here various formulations of vertex models that have been proposed for describing tissues in two and three dimensions. We discuss a generic formulation using a virtual work differential, and we review applications of vertex models to biological morphogenetic processes. We also highlight recent efforts to obtain continuum theories of tissue mechanics, which are effective, coarse-grained descriptions of vertex models. This article is part of the themed issue 'Systems morphodynamics: understanding the development of tissue hardware'.

Purpose Fused deposition modelling (FDM) is the most economical additive manufacturing technique. The purpose of this paper is to describe a detailed review of this technique. Total 211 research papers published during the past 26 years, that is, from the year 1994 to 2019 are critically reviewed. Based on the literature review, research gaps are identified and the scope for future work is discussed. Design/methodology/approach Literature review in the domain of FDM is categorized into five sections – (i) process parameter optimization, (ii) environmental factors affecting the quality of printed parts, (iii) post-production finishing techniques to improve quality of parts, (iv) numerical simulation of process and (iv) recent advances in FDM. Summary of major research work in FDM is presented in tabular form. Findings Based on literature review, research gaps are identified and scope of future work in FDM along with roadmap is discussed. Research limitations/implications In the present paper, literature related to chemical, electric and magnetic properties of FDM parts made up of various filament feedstock materials is not reviewed. Originality/value This is a comprehensive literature review in the domain of FDM focused on identifying the direction for future work to enhance the acceptability of FDM printed parts in industries.

Light simulations hold great potential for advancing optical techniques in neuroscience. They facilitate the in-silico refinement of optical stimulator designs and enable simulations of optical recordings from computational brain models, aiding neuroscience in forming a mechanistic understanding of brain circuitry. However, many published light models are inaccessible due to unavailable source code and documentation or are impractical due to excessive computational demands. To address these challenges, we replicate and enhance the efficient and accurate light simulation model by Yona et al. [1], which was previously available only in compiled form accompanied by sparse documentation. In this work, we resolve ambiguities in the original model, correct errors that caused discrepancies between simulations and published results, improve computational efficiency by an order of magnitude, and open-source all the resulting code and detailed documentation. These enhancements enable simulations of cortical volumes exceeding 1   mm 3 to run in seconds on standard laptop hardware. Our model software provides an accessible, adaptable, and rapid light simulation tool, which adheres to FAIR principles to ensure future-proof and broad utility for the neuroscience community.

Numerical analysis methods including finite element analysis (FEA), computational fluid dynamics (CFD), and fluid–structure interaction (FSI) analysis have been used to study the biomechanics of human tissues and organs, as well as tissue-medical device interactions, and treatment strategies. However, for patient-specific computational analysis, complex procedures are usually required to set-up the models, and long computing time is needed to perform the simulation, preventing fast feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed deep neural networks (DNNs) to directly estimate the steady-state distributions of pressure and flow velocity inside the thoracic aorta. After training on hemodynamic data from CFD simulations, the DNNs take as input a shape of the aorta and directly output the hemodynamic distributions in one second. The trained DNNs are capable of predicting the velocity magnitude field with an average error of 1.9608% and the pressure field with an average error of 1.4269%. This study demonstrates the feasibility and great potential of using DNNs as a fast and accurate surrogate model for hemodynamic analysis of large blood vessels.

Blood perfusion in tumors can be significantly lower than that in the surrounding normal tissue owing to the leakiness and/or compression of tumor blood vessels. Impaired perfusion reduces oxygen supply and results in a hypoxic microenvironment. Hypoxia promotes tumor progression and immunosuppression, and enhances the invasive and metastatic potential of cancer cells. Furthermore, poor perfusion lowers the delivery of systemically administered drugs. Therapeutic strategies to improve perfusion include reduction in vascular permeability by vascular normalization and vascular decompression by alleviating physical forces (solid stress) inside tumors. Both strategies have shown promise, but guidelines on how to use these strategies optimally are lacking. To this end, we developed a mathematical model to guide the optimal use of these strategies. The model accounts for vascular, transvascular, and interstitial fluid and drug transport as well as the diameter and permeability of tumor vessels. Model simulations reveal an optimal perfusion region when vessels are uncompressed, but not very leaky. Within this region, intratumoral distribution of drugs is optimized, particularly for drugs 10 nm in diameter or smaller and of low binding affinity. Therefore, treatments should modify vessel diameter and/or permeability such that perfusion is optimal. Vascular normalization is more effective for hyperpermeable but largely uncompressed vessels (e.g., glioblastomas), whereas solid stress alleviation is more beneficial for compressed but less-permeable vessels (e.g., pancreatic ductal adenocarcinomas). In the case of tumors with hyperpermeable and compressed vessels (e.g., subset of mammary carcinomas), the two strategies need to be combined for improved treatment outcomes.

High-quality gynecologist and midwife training is particularly relevant to limit medical complications and reduce maternal and fetal morbimortalities. Physical and virtual training simulators have been developed. However, physical simulators offer a simplified model and limited visualization of the childbirth process, while virtual simulators still lack a realistic interactive system and are generally limited to imposed predefined gestures. Objective performance assessment based on the simulation numerical outcomes is still not at hand. In the present work, we developed a virtual childbirth simulator based on the Mixed-Reality (MR) technology coupled with HyperMSM (Hyperelastic Mass-Spring Model) formulation for real-time soft-tissue deformations, providing intuitive user interaction with the virtual physical model and a quantitative assessment to enhance the trainee’s gestures. Microsoft HoloLens 2 was used and the MR simulator was developed including a complete holographic obstetric model. A maternal pelvis system model of a pregnant woman (including the pelvis bone, the pelvic floor muscles, the birth canal, the uterus, and the fetus) was generated, and HyperMSM formulation was applied to simulate the soft tissue deformations. To induce realistic reactions to free gestures, the virtual replicas of the user’s detected hands were introduced into the physical simulation and were associated with a contact model between the hands and the HyperMSM models. The gesture of pulling any part of the virtual models with two hands was also implemented. Two labor scenarios were implemented within the MR childbirth simulator: physiological labor and forceps-assisted labor. A scoring system for the performance assessment was included based on real-time biofeedback. As results, our developed MR simulation application was developed in real-time with a refresh rate of 30–50 FPS on the HoloLens device. HyperMSM model was validated using FE outcomes: high correlation coefficients of [0.97–0.99] and weighted root mean square relative errors of 9.8% and 8.3% were obtained for the soft tissue displacement and energy density respectively. Experimental tests showed that the implemented free-user interaction system allows to apply the correct maneuvers (in particular the “Viennese” maneuvers) during the labor process, and is capable to induce a truthful reaction of the model. Obtained results confirm also the possibility of using our simulation’s outcomes to objectively evaluate the trainee’s performance with a reduction of 39% for the perineal strain energy density and 5.6 mm for the vertical vaginal diameter when the “Viennese” technique is applied. This present study provides, for the first time, an interactive childbirth simulator with an MR immersive experience with direct free-hand interaction, real-time soft-tissue deformation feedback, and an objective performance assessment based on numerical outcomes. This offers a new perspective for enhancing next-generation training-based obstetric teaching. The used models of the maternal pelvic system and the fetus will be enhanced, and more delivery scenarios (e.g. instrumental delivery, breech delivery, shoulder dystocia) will be designed and integrated. The third stage of labor will be also investigated to include the delivery of the placenta, and the clamping and cutting of the umbilical cord.

Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular, using a simple, consistent interface. While Tissue Forge is designed to simplify solving problems in complex subcellular, cellular and tissue biophysics, it supports applications ranging from classic molecular dynamics to agent-based multicellular systems with dynamic populations. Tissue Forge users can build and interact with models and simulations in real-time and change simulation details during execution, or execute simulations off-screen and/or remotely in high-performance computing environments. Tissue Forge provides a growing library of built-in model components along with support for user-specified models during the development and application of custom, agent-based models. Tissue Forge includes an extensive Python API for model and simulation specification via Python scripts, an IPython console and a Jupyter Notebook, as well as C and C++ APIs for integrated applications with other software tools. Tissue Forge supports installations on 64-bit Windows, Linux and MacOS systems and is available for local installation via conda.

Significance: The Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resulting in high computational burdens. Aim: We aim to develop an effective image denoising technique using deep learning (DL) to dramatically improve the low-photon MC simulation result quality, equivalently bringing further acceleration to the MC method. Approach: We developed a cascade-network combining DnCNN with UNet, while extending a range of established image denoising neural-network architectures, including DnCNN, UNet, DRUNet, and deep residual-learning for denoising MC renderings (ResMCNet), in handling three-dimensional MC data and compared their performances against model-based denoising algorithms. We also developed a simple yet effective approach to creating synthetic datasets that can be used to train DL-based MC denoisers. Results: Overall, DL-based image denoising algorithms exhibit significantly higher image quality improvements over traditional model-based denoising algorithms. Among the tested DL denoisers, our cascade network yields a 14 to 19 dB improvement in signal-to-noise ratio, which is equivalent to simulating 25  ×   to 78  ×   more photons. Other DL-based methods yielded similar results, with our method performing noticeably better with low-photon inputs and ResMCNet along with DRUNet performing better with high-photon inputs. Our cascade network achieved the highest quality when denoising complex domains, including brain and mouse atlases. Conclusions: Incorporating state-of-the-art DL denoising techniques can equivalently reduce the computation time of MC simulations by one to two orders of magnitude. Our open-source MC denoising codes and data can be freely accessed at http://mcx.space/.

Accurate prediction of an individual’s phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 individuals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis ( n ≈ 700, 000) on height and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R 2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p  value thresholding. Various approaches are being used for polygenic prediction including Bayesian multiple regression methods that require access to individual-level genotype data. Here, the authors extend BayesR to utilise GWAS summary statistics (SBayesR) and show that it outperforms other summary statistic-based methods.