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For a Bayesian, the task to define the likelihood can be as perplexing as the task to define the prior. We focus on situations when the parameter of interest has been emancipated from the likelihood and is linked to data directly through a loss function. We survey existing work on both Bayesian parametric inference with Gibbs posteriors and Bayesian non-parametric inference. We then highlight recent bootstrap computational approaches to approximating loss-driven posteriors. In particular, we focus on implicit bootstrap distributions defined through an underlying push-forward mapping. We investigate independent, identically distributed (iid) samplers from approximate posteriors that pass random bootstrap weights through a trained generative network. After training the deep-learning mapping, the simulation cost of such iid samplers is negligible. We compare the performance of these deep bootstrap samplers with exact bootstrap as well as MCMC on several examples (including support vector machines or quantile regression). We also provide theoretical insights into bootstrap posteriors by drawing upon connections to model mis-specification. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
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[This corrects the article DOI: 10.3389/fpls.2022.976684.].
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Agropyron mongolicum (A. mongolicum) is an excellent gramineous forage with extreme drought tolerance, which lives in arid and semiarid desert areas. However, the mechanism that underlies the response of microRNAs (miRNAs) and their targets in A. mongolicum to drought stress is not well understood. In this study, we analyzed the transcriptome, small RNAome (specifically the miRNAome) and degradome to generate a comprehensive resource that focused on identifying key regulatory miRNA-target circuits under drought stress. The most extended transcript in each collection is known as the UniGene, and a total of 41,792 UniGenes and 1,104 miRNAs were identified, and 99 differentially expressed miRNAs negatively regulated 1,474 differentially expressed target genes. Among them, eight miRNAs were unique to A. mongolicum, and there were 36 target genes. A weighted gene co-expression network analysis identified five hub genes. The miRNAs of five hub genes were screened with an integration analysis of the degradome and sRNAs, such as osa-miR444a-3p.2-MADS47, bdi-miR408-5p_1ss19TA-CCX1, tae-miR9774_L-2R-1_1ss11GT-carC, ata-miR169a-3p-PAO2, and bdi-miR528-p3_2ss15TG20CA-HOX24. The functional annotations revealed that they were involved in mediating the brassinosteroid signal pathway, transporting and exchanging sodium and potassium ions and regulating the oxidation-reduction process, hydrolase activity, plant response to water deprivation, abscisic acid (ABA) and the ABA-activated signaling pathway to regulate drought stress. Five hub genes were discovered, which could play central roles in the regulation of drought-responsive genes. These results show that the combined analysis of miRNA, the transcriptome and degradation group provides a useful platform to investigate the molecular mechanism of drought resistance in A. mongolicum and could provide new insights into the genetic engineering of Poaceae crops in the future.
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Based on the sequence of an expressed sequence tag, the full-length cDNA of 1,008 nucleotides was cloned from Ammopiptanthus mongolicus by rapid amplification of cDNA ends. It was designated as AmDHN, encoding a protein of 183 amino acids. The calculated molecular weight of the AmDHN protein is 18.4 k Da, and theoretical isoelectric point is 5.78. The AmDHN localized in nucleus. Under normal growth conditions, differential expression of AmDHN exhibited that the expression was the highest in seeds and the lowest in flowers. AmDHN could be induced by NaCl, PEG6000, ABA and drought treatments. Salt and drought resistances of transgenic plants with overexpression of AmDHN are improved. Taken together, these results demonstrated that AmDHN could regulate the expression of abiotic-responsive genes and plays important roles in modulating the tolerance of plants to abiotic stresses.