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Abstract Delayed immunological rejection after human lung transplantation causes chronic lung allograft dysfunction, which is associated with high mortality. Delayed rejection may be attributable to indirect alloantigen presentation by host antigen-presenting cells; however, its pathophysiology is not fully understood. The mitogen-activated protein kinase pathway is activated in T cells upon stimulation, and we previously showed that the MEK inhibitor, trametinib, suppresses graft-versus-host disease after murine bone marrow transplantation. We investigated whether trametinib suppresses graft rejection after two types of rat lung transplantation and analyzed its immunological mode of action. Major histocompatibility complex–mismatched transplantation from brown Norway rats into Lewis rats and minor histocompatibility antigen–mismatched transplantation from Fischer 344 rats into Lewis rats were performed. Cyclosporine (CsA) and/or trametinib were administered alone or consecutively. Acute and delayed rejection, lymphocyte infiltration, and pulmonary function were evaluated. Administration of trametinib after CsA suppressed delayed rejection, reduced inflammatory cell infiltration and fibrosis within the graft, and preserved pulmonary functions at Day 28. Trametinib suppressed functional differentiation of T and B cells in the periphery but preserved thymic T cell differentiation. Donor B cells within the graft disappeared by Day 14, indicating that delayed graft rejection at Day 28 was mainly due to indirect presentation by host antigen-presenting cells. Finally, trametinib administration without CsA preconditioning suppressed rejection after minor histocompatibility antigen–mismatched transplantation. Trametinib attenuates delayed rejection upon major histocompatibility complex–mismatched transplantation by suppressing indirect presentation and is a promising candidate to treat chronic lung allograft dysfunction in humans.

Transplantation is often the last resort for end-stage organ failures, e.g., kidney, liver, heart, lung, and pancreas. The shortage of donor organs is the main limiting factor for successful transplantation in humans. Except living donations, other alternatives are needed, e.g., xenotransplantation of pig organs. However, immune rejection remains the major challenge to overcome in xenotransplantation. There are three different xenogeneic types of rejections, based on the responses and mechanisms involved. It includes hyperacute rejection (HAR), delayed xenograft rejection (DXR) and chronic rejection. DXR, sometimes involves acute humoral xenograft rejection (AHR) and cellular xenograft rejection (CXR), which cannot be strictly distinguished from each other in pathological process. In this review, we comprehensively discussed the mechanism of these immunological rejections and summarized the strategies for preventing them, such as generation of gene knock out donors by different genome editing tools and the use of immunosuppressive regimens. We also addressed organ-specific barriers and challenges needed to pave the way for clinical xenotransplantation. Taken together, this information will benefit the current immunological research in the field of xenotransplantation.

Bayesian model updating has received considerable attention and has been extensively used in structural damage detection. It provides a rigorous statistical framework for realizing structural system identification and characterizing uncertainties associated with modeling and measurements. The Markov Chain Monte Carlo (MCMC) is a promising tool for inferring the posterior distribution of model parameters to avoid the intractable evaluation of multi-dimensional integration. However, the efficacy of most MCMC techniques suffers from the curse of parameter dimension, which restricts the application of Bayesian model updating to the damage detection of large-scale systems. In addition, there are several MCMC techniques that require users to properly choose application-specific models, based on the understanding of algorithm mechanisms and limitations. As seen in the literature, there is a lack of comprehensive work that investigates the performances of various MCMC algorithms in their application of structural damage detection. In this study, the Differential Evolutionary Adaptive Metropolis (DREAM), a multi-chain MCMC, is explored and adapted to Bayesian model updating. This paper illustrates how DREAM is used for model updating with many uncertainty parameters (i.e., 40 parameters). Furthermore, the study provides a tutorial to users who may be less experienced with Bayesian model updating and MCMC. Two advanced single-chain MCMC algorithms, namely, the Delayed Rejection Adaptive Metropolis (DRAM) and Transitional Markov Chain Monte Carlo (TMCMC), and DREAM are elaborately introduced to allow practitioners to understand better the concepts and practical implementations. Their performances in model updating and damage detection are compared through three different engineering applications with increased complexity, e.g., a forty-story shear building, a two-span continuous steel beam, and a large-scale steel pedestrian bridge.

This study proposes an approach for the uncertainty quantification at each stage of a single hydrological process of water level predictions based on different sources of mean areal precipitation (MAP) forecasts by using an adaptive Bayesian Markov chain Monte Carlo (MCMC) approach. The MAP forecasts are derived from the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) system and a long short-term memory (LSTM) network. The predicted water levels at two stations in the Gangnam catchment, Seoul, South Korea, are processed with a coupled 1D/2D urban hydrological model (1D/2D-UHM) forced by MAPLE MAP forecasts and LSTM-corrected MAP forecasts of five heavy rainfall events. The proposed Bayesian approach using the delayed rejection and adaptive Metropolis (DRAM) algorithm was compared with the Metropolis-Hastings (MH) algorithm in the uncertainty estimation of Weibull distribution parameters. The uncertainty contributions of the stages and sources in the related process were analyzed, including quantitative precipitation estimation (QPE) inputs, MAP inputs and 1D/2D-UHM. The results indicate that the uncertainty contribution of the MAPLE MAP forecasting is the highest in the 3-hour forecasting time. The uncertainty contribution of the QPE input for MAPLE MAP forecasting is the smallest and that of two sources, including the LSTM-corrected MAP source, and MAP and the coupled model is more significant than that of the QPE input. This research showed that the adaptive Bayesian MCMC method using the DRAM algorithm might be a robust option in quantitative uncertainty analyses of a single hydrological process, especially for urban flood management.

In the process of identifying groundwater pollution sources, in order to solve the problem that the monitoring data of monitoring wells was insufficient or the correlation between monitoring data and model parameters was weak, a monitoring well optimization method based on Bayesian formula and information entropy was proposed. Two-dimensional phreatic groundwater solute transport model was built and solved by using GMS software. To reduce the computational load of calling the numerical model repeatedly in the optimization design of the monitoring schemes and the identification process of the pollution sources, the Kriging method was used to establish the surrogate model of the numerical model. Under the condition of single well monitoring and determined monitoring frequency, with the target of optimization of monitoring position number D and monitoring time interval ∆t, both the single-objective monitoring scheme with the minimum information entropy of the model parameter posterior distribution and the multi-objective monitoring scheme with the minimum information entropy and the shortest monitoring time were optimized respectively. According to the above-optimized monitoring schemes, the delayed rejection adaptive Metropolis algorithm was used to identify the pollution source parameters. The case study results showed that under the condition of pre-set single well monitoring with monitoring frequency of 10 times, the single-objective optimized monitoring scheme was D = 37 and Δt = 20 days. Under this monitoring scheme, the mean errors of inversion pollution source parameters α = (XS, YS, T1, T2, QS) were 0.09%, 0.4%, 4.72%, 2.43%, and 9.29%, respectively. The multi-objective optimized monitoring scheme was D = 37 and Δt = 2 days. Under this monitoring scheme, the mean errors of the inversion parameters α = (XS, YS, T1, T2, QS) were 12.76%, 3.77%, 5.13%, 1.36%, and 7.68%, respectively. Compared with the monitoring scheme based on the single-objective optimization, although the inversion mean error of the five parameters based on the multi-objective optimized monitoring scheme increased by 2.75%, the monitoring time significantly reduced from 180 to 18 days.

The main aim of this paper is to present a Bayesian analysis of Multivariate GARCH(l, m) (M-GARCH) models including estimation of the coefficient parameters as well as the model order, by combining a set of existing MCMC algorithms in the literature. The proposed algorithm focuses on the BEKK formulation of the multivariate GARCH model. The estimation procedure will be designed as a custom MCMC with embedded Reversible Jump MCMC (RJMCMC) and Delayed Rejection Metropolis-Hastings (DRMH) steps implemented using the statistical software R. The RJMCMC steps allow three variants of BEKK models (constant, diagonal and full) to be indexed and this index included as a parameter to be estimated. The proposed MCMC algorithms are validated using extensive simulation experiments followed by a case study using bivariate data derived from the daily share prices for BHP Group Limited, Rio Tinto Group, and Fortescue Metals Group Limited on the ASX over from September 2013 to December 2021.

Estimation of low or high conditional quantiles is called for in many applications, but commonly encountered data sparsity at the tails of distributions makes this a challenging task. We develop a Bayesian joint-quantile regression method to borrow information across tail quantiles through a linear approximation of quantile coefficients. Motivated by a working likelihood linked to the asymmetric Laplace distributions, we propose a new Bayesian estimator for high quantiles by using a delayed rejection and adaptive Metropolis and Gibbs algorithm. We demonstrate through numerical studies that the proposed estimator is generally more stable and efficient than conventional methods for estimating tail quantiles, especially at small and modest sample sizes.

Numerous biological processes, many impacting on human health, rely on collective cell movement. We develop nine candidate models, based on advection-diffusion partial differential equations, to describe various alternative mechanisms that may drive cell movement. The parameters of these models were inferred from one-dimensional projections of laboratory observations of Dictyostelium discoideum cells by sampling from the posterior distribution using the delayed rejection adaptive Metropolis algorithm. The best model was selected by using the widely applicable information criterion. We conclude that cell movement in our study system was driven both by a self-generated gradient in an attractant that the cells could deplete locally, and by chemical interactions between the cells.

The global emergence of Batrachochytrium dendrobatidis (Bd) has caused the extinction of hundreds of amphibian species worldwide. It has become increasingly important to be able to precisely predict time to Bd arrival in a population. The data analyzed herein present a unique challenge in terms of modeling because there is a strong spatial component to Bd arrival time and the traditional proportional hazards assumption is grossly violated. To address these concerns, we develop a novel marginal Bayesian nonparametric survival model for spatially correlated right‐censored data. This class of models assumes that the logarithm of survival times marginally follow a mixture of normal densities with a linear‐dependent Dirichlet process prior as the random mixing measure, and their joint distribution is induced by a Gaussian copula model with a spatial correlation structure. To invert high‐dimensional spatial correlation matrices, we adopt a full‐scale approximation that can capture both large‐ and small‐scale spatial dependence. An efficient Markov chain Monte Carlo algorithm with delayed rejection is proposed for posterior computation, and an R package spBayesSurv is provided to fit the model. This approach is first evaluated through simulations, then applied to threatened frog populations in Sequoia‐Kings Canyon National Park.

Leaf-cutting ants (LCAs) are dominant herbivores of the Neotropics, as well as economically important pests. Their foraging ecology and patterns/mechanisms of food selection have received considerable attention. Recently, it has been documented that LCAs exhibit a delayed rejection of previously accepted food plants following treatment with a fungicide that makes the plants unsuitable as substrate for their symbiotic fungus. Here, we investigated whether LCAs similarly reject plants with induced chemical defenses, by combining analysis of volatile emissions with dual-choice bioassays that used LCA subcolonies (Atta sexdens L.). On seven consecutive days, foraging ants were given the choice between leaf disks from untreated control plants and test plants of Vitis vinifera ssp. vinifera L. treated with the phytohormone jasmonic acid (JA) to mimic herbivore attack. Chemical analysis revealed the emission of a characteristic set of herbivore-induced volatile organic compounds (VOC) from JA-induced plants. Dual-choice experiments indicated that workers did not show any preference initially, but that they avoided JA-treated plants from day five onwards. Our finding that A. sexdens foragers learn to avoid VOC-emitting plants, which are likely detrimental to their symbiotic fungus, represents the first evidence for avoidance learning in attine ants toward plants with induced defenses.