Ideal free streamline flow over a curved obstacle

AbstractIn the classical two-dimensional model the description of Helmholtz's (Kirchhoff) flow is a problem of complex analysis which can be solved analytically only for a few simple bodies or polygonal contours, using the Schwarz-Christoffel map. This paper presents a practical method for computing flows over arbitrary obstacles whose boundaries may be piecewise smooth curves, while the impinging flow may be an unbounded flow, a jet, or a semi-infinite stream, i.e. the ocean

On the behaviour of lung tissue under tension and compression

Lung injuries are common among those who suffer an impact or trauma. The relative severity of injuries up to physical tearing of tissue have been documented in clinical studies. However, the specific details of energy required to cause visible damage to the lung parenchyma are lacking. Furthermore, the limitations of lung tissue under simple mechanical loading are also not well documented. This study aimed to collect mechanical test data from freshly excised lung, obtained from both Sprague-Dawley rats and New Zealand White rabbits. Compression and tension tests were conducted at three different strain rates: 0.25, 2.5 and 25 min−1. This study aimed to characterise the quasi-static behaviour of the bulk tissue prior to extending to higher rates. A nonlinear viscoelastic analytical model was applied to the data to describe their behaviour. Results exhibited asymmetry in terms of differences between tension and compression. The rabbit tissue also appeared to exhibit stronger viscous behaviour than the rat tissue. As a narrow strain rate band is explored here, no conclusions are being drawn currently regarding the rate sensitivity of rat tissue. However, this study does highlight both the clear differences between the two tissue types and the important role that composition and microstructure can play in mechanical response

Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations

[EN] The development of accurate real-time models of the biomechanical behavior of different organs and tissues still poses a challenge in the field of biomechanical engineering. In the case of the liver, specifically, such a model would constitute a great leap forward in the implementation of complex applications such as surgical simulators, computed-assisted surgery or guided tumor irradiation. In this work, a relatively novel approach for developing such a model is presented. It consists in the use of a machine learning algorithm, which provides real-time inference, trained on tens of thousands of simulations of the biomechanical behavior of the liver carried out by the finite element method on more than 100 different liver geometries. Considering a target accuracy threshold of 3 mm for the Euclidean Error, four different scenarios were modeled and assessed: a single liver with an arbitrary force applied (99.96% of samples within the accepted error range), a single liver with two simultaneous forces applied (99.84% samples in range), a single liver with different material properties and an arbitrary force applied (98.46% samples in range), and a much more general model capable of modeling the behavior of any liver with an arbitrary force applied (99.01% samples in range for the median liver). The results show that the Machine Learning models perform extremely well on all the scenarios, managing to keep the Mean Euclidean Error under 1 mm in all cases. Furthermore, the proposed model achieves working frequencies above 100Hz on modest hardware (with frequencies above 1000Hz being easily achievable on more powerful GPUs) thus fulfilling the real-time requirements. These results constitute a remarkable improvement in this field and may involve a prompt implementation in clinical practice.This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through research projects TIN2014-52033-R, also supported by European FEDER funds.Pellicer-Valero, OJ.; Rupérez Moreno, MJ.; Martinez-Sanchis, S.; Martín-Guerrero, JD. (2020). Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations. Expert Systems with Applications. 143:1-12. https://doi.org/10.1016/j.eswa.2019.113083S112143Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., & Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. arXiv:1605.08695.Brunon, A., Bruyère-Garnier, K., & Coret, M. (2010). Mechanical characterization of liver capsule through uniaxial quasi-static tensile tests until failure. Journal of Biomechanics, 43(11), 2221-2227. doi:10.1016/j.jbiomech.2010.03.038Chinesta, F., Leygue, A., Bordeu, F., Aguado, J. V., Cueto, E., Gonzalez, D., … Huerta, A. (2013). PGD-Based Computational Vademecum for Efficient Design, Optimization and Control. Archives of Computational Methods in Engineering, 20(1), 31-59. doi:10.1007/s11831-013-9080-xClifford, M. A., Banovac, F., Levy, E., & Cleary, K. (2002). Assessment of Hepatic Motion Secondary to Respiration for Computer Assisted Interventions. Computer Aided Surgery, 7(5), 291-299. doi:10.3109/10929080209146038Cotin, S., Delingette, H., & Ayache, N. (2000). A hybrid elastic model for real-time cutting, deformations, and force feedback for surgery training and simulation. The Visual Computer, 16(8), 437-452. doi:10.1007/pl00007215Duysak, A., Zhang, J. J., & Ilankovan, V. (2003). 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The International Journal of Medical Robotics and Computer Assisted Surgery, 9(4), e52-e60. doi:10.1002/rcs.1493Lister, K., Gao, Z., & Desai, J. P. (2010). Development of In Vivo Constitutive Models for Liver: Application to Surgical Simulation. Annals of Biomedical Engineering, 39(3), 1060-1073. doi:10.1007/s10439-010-0227-8Lorente, D., Martínez-Martínez, F., Rupérez, M. J., Lago, M. A., Martínez-Sober, M., Escandell-Montero, P., … Martín-Guerrero, J. D. (2017). A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning. Expert Systems with Applications, 71, 342-357. doi:10.1016/j.eswa.2016.11.037Maas, S. A., Ellis, B. J., Ateshian, G. A., & Weiss, J. A. (2012). FEBio: Finite Elements for Biomechanics. Journal of Biomechanical Engineering, 134(1). doi:10.1115/1.4005694Myronenko, A., & Xubo Song. (2010). Point Set Registration: Coherent Point Drift. 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Inborn errors of OAS-RNase L in SARS-CoV-2-related multisystem inflammatory syndrome in children

Multisystem inflammatory syndrome in children (MIS-C) is a rare and severe condition that follows benign COVID-19. We report autosomal recessive deficiencies of OAS1, OAS2, or RNASEL in five unrelated children with MIS-C. The cytosolic double-stranded RNA (dsRNA)-sensing OAS1 and OAS2 generate 2'-5'-linked oligoadenylates (2-5A) that activate the single-stranded RNA-degrading ribonuclease L (RNase L). Monocytic cell lines and primary myeloid cells with OAS1, OAS2, or RNase L deficiencies produce excessive amounts of inflammatory cytokines upon dsRNA or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stimulation. Exogenous 2-5A suppresses cytokine production in OAS1-deficient but not RNase L-deficient cells. Cytokine production in RNase L-deficient cells is impaired by MDA5 or RIG-I deficiency and abolished by mitochondrial antiviral-signaling protein (MAVS) deficiency. Recessive OAS-RNase L deficiencies in these patients unleash the production of SARS-CoV-2-triggered, MAVS-mediated inflammatory cytokines by mononuclear phagocytes, thereby underlying MIS-C

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men