(Homo)glutathione Deficiency Impairs Root-knot Nematode Development in Medicago truncatula

Root-knot nematodes (RKN) are obligatory plant parasitic worms that establish and maintain an intimate relationship with their host plants. During a compatible interaction, RKN induce the redifferentiation of root cells into multinucleate and hypertrophied giant cells essential for nematode growth and reproduction. These metabolically active feeding cells constitute the exclusive source of nutrients for the nematode. Detailed analysis of glutathione (GSH) and homoglutathione (hGSH) metabolism demonstrated the importance of these compounds for the success of nematode infection in Medicago truncatula. We reported quantification of GSH and hGSH and gene expression analysis showing that (h)GSH metabolism in neoformed gall organs differs from that in uninfected roots. Depletion of (h)GSH content impaired nematode egg mass formation and modified the sex ratio. In addition, gene expression and metabolomic analyses showed a substantial modification of starch and γ-aminobutyrate metabolism and of malate and glucose content in (h)GSH-depleted galls. Interestingly, these modifications did not occur in (h)GSH-depleted roots. These various results suggest that (h)GSH have a key role in the regulation of giant cell metabolism. The discovery of these specific plant regulatory elements could lead to the development of new pest management strategies against nematodes

A self-organizing state space model and simplex initial distribution search

Self-organizing state space model, Monte Carlo particle filter, Parameter estimation, Simplex Nelder-Mead algorithm,

Falsification of Cyber-Physical Systems with Constrained Signal Spaces

International audienceFalsification has garnered much interest recently as a way to validate complex CPS designs with respect to a specification expressed via temporal logics. Using their quantitative semantics, the falsification problem can be formulated as robustness minimization problem. To make this infinite-dimensional problem tractable, a common approach is to restrict to classes of signals that can be defined using a finite number of parameters, such as piecewise-constant or piecewise-linear signals with fixed time intervals). A major drawback of this approach is that when the input signals must satisfy non-trivial temporal constraints, encoding these constraints into bounded domains for parameters can be difficult. In this work, to better capture temporal constraints on the input signal space, we use timed automata (TA) and make use of a transformation that allows sampling TA traces by sampling points in the unit box. We exploit this transformation to efficiently encode constrained CPS signals in the robustness minimization problem. This transformation also allows us to define an effective coverage measure of the constrained signal space so as to provide quantitative guarantees when no falsifying behaviour is found. In addition, this coverage is used to improve the black-box optimisation performance by detecting situations where the search is stuck near a local optimum. The approach is demonstrated on a ∆Σ modulator and a model of car automatic transmission subject to constraints describing usual driving patterns