Bayesian approach to Gaussian process regression with uncertain inputs

Conventional Gaussian process regression exclusively assumes the existence of noise in the output data of model observations. In many scientific and engineering applications, however, the input locations of observational data may also be compromised with uncertainties owing to modeling assumptions, measurement errors, etc. In this work, we propose a Bayesian method that integrates the variability of input data into Gaussian process regression. Considering two types of observables -- noise-corrupted outputs with fixed inputs and those with prior-distribution-defined uncertain inputs, a posterior distribution is estimated via a Bayesian framework to infer the uncertain data locations. Thereafter, such quantified uncertainties of inputs are incorporated into Gaussian process predictions by means of marginalization. The effectiveness of this new regression technique is demonstrated through several numerical examples, in which a consistently good performance of generalization is observed, while a substantial reduction in the predictive uncertainties is achieved by the Bayesian inference of uncertain inputs

Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots

Parametric reduced-order modelling often serves as a surrogate method for hemodynamics simulations to improve the computational efficiency in many-query scenarios or to perform real-time simulations. However, the snapshots of the method require to be collected from the same discretisation, which is a straightforward process for physical parameters, but becomes challenging for geometrical problems, especially for those domains featuring unparameterised and unique shapes, e.g. patient-specific geometries. In this work, a data-driven surrogate model is proposed for the efficient prediction of blood flow simulations on similar but distinct domains. The proposed surrogate model leverages group surface registration to parameterise those shapes and formulates corresponding hemodynamics information into geometry-informed snapshots by the diffeomorphisms constructed between a reference domain and original domains. A non-intrusive reduced-order model for geometrical parameters is subsequently constructed using proper orthogonal decomposition, and a radial basis function interpolator is trained for predicting the reduced coefficients of the reduced-order model based on compressed geometrical parameters of the shape. Two examples of blood flowing through a stenosis and a bifurcation are presented and analysed. The proposed surrogate model demonstrates its accuracy and efficiency in hemodynamics prediction and shows its potential application toward real-time simulation or uncertainty quantification for complex patient-specific scenarios

Inverse uncertainty quantification of a mechanical model of arterial tissue with surrogate modeling

Disorders of coronary arteries lead to severe health problems such as atherosclerosis, angina, heart attack and even death. Considering the clinical significance of coronary arteries, an efficient computer model is a vital step towards tissue engineering, enhancing the research of coronary diseases, and developing medical treatment and interventional tools. In this work, we apply inverse uncertainty quantification to a microscale agent-based arterial tissue model, a component of the 3D multiscale model of in-stent restenosis (ISR3D). IUQ provides calibration of the arterial tissue model to achieve realistic mechanical behaviour in line with the experimental data measured from the tissue's macroscopic behaviour. Bayesian calibration with bias term correction is applied as an IUQ technique to reduce the uncertainty of unknown polynomial coefficients of the attractive force function and achieve agreement with the experimental data based on the uniaxial strain tests of arterial tissue. Due to the high computational costs of the ISR3D model, the Gaussian process (GP) regression surrogate model is introduced to ensure the feasibility of the IUQ computations. The result is an IUQ methodology to calibrate a model with uncertain parameters and a microscale agent-based model of arterial tissue, which produces mechanical behaviour in line with the experimental data

Medicinal chemistry strategies towards the development of non-covalent SARS-CoV-2 Mpro inhibitors

The main protease (Mpro) of SARS-CoV-2 is an attractive target in anti-COVID-19 therapy for its high conservation and major role in the virus life cycle. The covalent Mpro inhibitor nirmatrelvir (in combination with ritonavir, a pharmacokinetic enhancer) and the non-covalent inhibitor ensitrelvir have shown efficacy in clinical trials and have been approved for therapeutic use. Effective antiviral drugs are needed to fight the pandemic, while non-covalent Mpro inhibitors could be promising alternatives due to their high selectivity and favorable druggability. Numerous non-covalent Mpro inhibitors with desirable properties have been developed based on available crystal structures of Mpro. In this article, we describe medicinal chemistry strategies applied for the discovery and optimization of non-covalent Mpro inhibitors, followed by a general overview and critical analysis of the available information. Prospective viewpoints and insights into current strategies for the development of non-covalent Mpro inhibitors are also discussed.We gratefully acknowledge financial support from Major Basic Research Project of Shandong Provincial Natural Science Foundation (ZR2021ZD17, China), Science Foundation for Outstanding Young Scholars of Shandong Province (ZR2020JQ31, China), Foreign Cultural and Educational Experts Project (GXL20200015001, China), Guangdong Basic and Applied Basic Research Foundation (2021A1515110740, China), China Postdoctoral Science Foundation (2021M702003). This work was supported in part by the Ministry of Science and Innovation of Spain through grant PID2019-104176RB-I00/AEI/10.13039/501100011033 awarded to Luis Menéndez-Arias; An institutional grant of the Fundación Ramón Areces (Madrid, Spain) to the CBMSO is also acknowledged.Peer reviewe

Advances in reforming and partial oxidation of hydrocarbons for hydrogen production and fuel cell applications

One of the most attractive routes for the production of hydrogen or syngas for use in fuel cell applications is the reforming and partial oxidation of hydrocarbons. The use of hydrocarbons in high temperature fuel cells is achieved through either external or internal reforming. Reforming and partial oxidation catalysis to convert hydrocarbons to hydrogen rich syngas plays an important role in fuel processing technology. The current research in the area of reforming and partial oxidation of methane, methanol and ethanol includes catalysts for reforming and oxidation, methods of catalyst synthesis, and the effective utilization of fuel for both external and internal reforming processes. In this paper the recent progress in these areas of research is reviewed along with the reforming of liquid hydrocarbons, from this an overview of the current best performing catalysts for the reforming and partial oxidizing of hydrocarbons for hydrogen production is summarized