Summary. We consider the generic problem of performing sequential Bayesian inference in a state space model with observation process y, state process x and fixed parameter θ. An idealized approach would be to apply the iterated batch importance sampling algorithm of Chopin. This is a sequential Monte Carlo algorithm in the θ‐dimension, that samples values of θ, reweights iteratively these values by using the likelihood increments and rejuvenates the θ‐particles through a resampling step and a Markov chain Monte Carlo update step. In state space models these likelihood increments are intractable in most cases, but they may be unbiasedly estimated by a particle filter in the x‐dimension, for any fixed θ. This motivates the SMC2 algorithm that is proposed in the paper: a sequential Monte Carlo algorithm, defined in the θ‐dimension, which propagates and resamples many particle filters in the x‐dimension. The filters in the x‐dimension are an example of the random weight particle filter. In contrast, the particle Markov chain Monte Carlo framework that has been developed by Andrieu and colleagues allows us to design appropriate Markov chain Monte Carlo rejuvenation steps. Thus, the θ‐particles target the correct posterior distribution at each iteration t, despite the intractability of the likelihood increments. We explore the applicability of our algorithm in both sequential and non‐sequential applications and consider various degrees of freedom, as for example increasing dynamically the number of x‐particles. We contrast our approach with various competing methods, both conceptually and empirically through a detailed simulation study, and based on particularly challenging examples.

SummaryCrop yield has been greatly enhanced during the last century. However, most elite cultivars are adapted to temperate climates and are not well suited to more stressful conditions. In the context of climate change, stress resistance is a major concern. To overcome these difficulties, scientists may help breeders by providing genetic markers associated with stress resistance. However, multistress resistance cannot be obtained from the simple addition of single stress resistance traits. In the field, stresses are unpredictable and several may occur at once. Consequently, the use of single stress resistance traits is often inadequate. Although it has been historically linked with the heat stress response, the heat‐shock protein (HSP)/chaperone network is a major component of multiple stress responses. Among the HSP/chaperone ‘client proteins’, many are primary metabolism enzymes and signal transduction components with essential roles for the proper functioning of a cell. HSPs/chaperones are controlled by the action of diverse heat‐shock factors, which are recruited under stress conditions. In this review, we give an overview of the regulation of the HSP/chaperone network with a focus on Arabidopsis thaliana. We illustrate the role of HSPs/chaperones in regulating diverse signalling pathways and discuss several basic principles that should be considered for engineering multiple stress resistance in crops through the HSP/chaperone network.

Plant nucleotide-binding leucine-rich repeat receptors (NLRs) regulate immunity and cell death. In Arabidopsis, a subfamily of “helper” NLRs are required by many “sensor” NLRs. Active NRG1.1 oligomerized, was enriched in plasma membrane puncta and conferred cytoplasmic Ca2+ influx in plant and human cells. NRG1.1-dependent Ca2+ influx and cell death were sensitive to Ca2+ channel blockers and were suppressed by mutations impacting oligomerization or plasma membrane enrichment. Ca2+ influx and cell death mediated by NRG1.1 and ACTIVATED DISEASE RESISTANCE 1 (ADR1), another “helper” NLR, required conserved negatively charged N-terminal residues. Whole-cell voltage-clamp recordings demonstrate that Arabidopsis “helper” NLRs form Ca2+-permeable cation channels to directly regulate cytoplasmic Ca2+ levels and consequent cell death. Thus, “helper” NLRs transduce cell death signals directly.

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