Pathogen-derived mechanical cues potentiate the spatio-temporal implementation of plant defense.

BACKGROUND: The ongoing adaptation of plants to their environment is the basis for their survival. In this adaptation, mechanoperception of gravity and local curvature plays a role of prime importance in finely regulating growth and ensuring a dynamic balance preventing buckling. However, the abiotic environment is not the exclusive cause of mechanical stimuli. Biotic interactions between plants and microorganisms also involve physical forces and potentially mechanoperception. Whether pathogens trigger mechanoperception in plants and the impact of mechanotransduction on the regulation of plant defense remains however elusive. RESULTS: Here, we found that the perception of pathogen-derived mechanical cues by microtubules potentiates the spatio-temporal implementation of plant immunity to fungus. By combining biomechanics modeling and image analysis of the post-invasion stage, we reveal that fungal colonization releases plant cell wall-born tension locally, causing fluctuations of tensile stress in walls of healthy cells distant from the infection site. In healthy cells, the pathogen-derived mechanical cues guide the reorganization of mechanosensing cortical microtubules (CMT). The anisotropic patterning of CMTs is required for the regulation of immunity-related genes in distal cells. The CMT-mediated mechanotransduction of pathogen-derived cues increases Arabidopsis disease resistance by 40% when challenged with the fungus Sclerotinia sclerotiorum. CONCLUSIONS: CMT anisotropic patterning triggered by pathogen-derived mechanical cues activates the implementation of early plant defense in cells distant from the infection site. We propose that the mechano-signaling triggered immunity (MTI) complements the molecular signals involved in pattern and effector-triggered immunity.

A Data Fusion Method for Non-Destructive Testing by Means of Artificial Neural Networks.

In the aeronautics sector, aircraft parts are inspected during manufacture, assembly and service, to detect defects eventually present. Defects can be of different types, sizes and orientations, appearing in materials presenting a complex structure. Among the different inspection techniques, Non Destructive Testing (NDT) presents several advantages as they are noninvasive and cost effective. Within the NDT methods, Ultrasonic (US) waves are widely used to detect and characterize defects. However, due the so-called blind zone, they cannot be easily employed for defects close to the surface being inspected. On the other hand, another NDT technique such Eddy Current (EC) can be used only for detecting flaws close to the surface, due to the presence of the EC skin effect. The work presented in this article aims to combine the use of these two NDT methods, exploiting their complementary advantages. To reach this goal, a data fusion method is developed, by using Machine Learning techniques such as Artificial Neural Networks (ANNs). A simulated training database involving simulations of US and EC signals propagating in an Aluminum block in the presence of Side Drill Holes (SDHs) has been implemented, to train the ANNs. Measurements have been then performed on an Aluminum block, presenting tree different SDHs at specific depths. The trained ANNs were used to characterize the different real SDHs, providing an experimental validation. Eventually, particular attention has been addressed to the estimation errors corresponding to each flaw. Experimental results will show that depths and radii estimations error were confined on average within a range of 4%, recording a peak of 11% for the second SDHs.

An Inverse Approach for Ultrasonic Imaging From Full Matrix Capture Data: Application to Resolution Enhancement in NDT.

In the context of nondestructive testing (NDT), this article proposes an inverse problem approach for the reconstruction of high-resolution ultrasonic images from full matrix capture (FMC) data sets. We build a linear model that links the FMC data, i.e., the signals collected from all transmitter-receiver pairs of an ultrasonic array, to the discretized reflectivity map of the inspected object. In particular, this model includes the ultrasonic waveform corresponding to the response of transducers. Despite a large amount of data, the inversion problem is ill-posed. Therefore, a regularization strategy is proposed, where the reconstructed image is defined as the minimizer of a penalized least-squares cost function. A mixed penalization function is considered, which simultaneously enhances the sparsity of the image (in NDT, the reflectivity map is mostly zero except at the flaw locations) and its spatial smoothness (flaws may have some spatial extension). The proposed method is shown to outperform two well-known imaging methods: the total focusing method (TFM) and Excitelet. Numerical simulations with two close reflectors show that the proposed method improves the resolution limit defined by the Rayleigh criterion by a factor of four. Such high-resolution imaging capability is confirmed by experimental results obtained with side-drilled holes in an aluminum sample.

Estimating the material parameters of an inhomogeneous poroelastic plate from ultrasonic measurements in water.

The estimation of poroelastic material parameters based on ultrasound measurements is considered. The acoustical characterisation of poroelastic materials based on various measurements is typically carried out by minimising a cost functional of model residuals, such as the least squares functional. With a limited number of unknown parameters, least squares type approaches can provide both reliable parameter and error estimates. With an increasing number of parameters, both the least squares parameter estimates and, in particular, the error estimates often become unreliable. In this paper, the estimation of the material parameters of an inhomogeneous poroelastic (Biot) plate in the Bayesian framework for inverse problems is considered. Reflection and transmission measurements are performed and 11 poroelastic parameters, as well as 4 measurement setup-related nuisance parameters, are estimated. A Markov chain Monte Carlo algorithm is employed for the computational inference to assess the actual uncertainty of the estimated parameters. The results suggest that the proposed approach for poroelastic material characterisation can reveal the heterogeneities in the object, and yield reliable parameter and uncertainty estimates.