Seed imbibition is a very important process in plant biology by which, thanks to a simple water income, a dry seed may turn into a developing organism. In natural conditions, this process occurs in the soil, e.g., with difficult access for a direct observation. Monitoring the seed imbibition with non-invasive imaging techniques is therefore an important and possibly challenging task if one tries to perform it in natural conditions. In this report, we describe a set of four different imaging techniques that enable to addressing this task either in 3D or in 2D. For each technique, the following items are proposed. A detailed experimental protocol is provided to acquire images of the imbibition process. With the illustration of real data, the significance of the physical quantities measured in terms of their relation to the income of water in the seed is presented. Complete image analysis pipelines are then proposed to extract dynamic information on the imbibition process from such monitoring experiments. A final discussion compares the advantages and current limitations of each technique in addition to elements concerning the associated throughput and cost. These are criteria especially relevant in the field of plant phenotyping where large populations of plants are imaged to produce quantitatively significative traits after image processing.
The task fundamental to quantum communication and coding is considered which consists of detecting between two possible states of a noisy qubit, with a performance assessed by the overall probability of detection error. The detection process operates in the presence of decoherence represented by a quantum thermal noise at an arbitrary temperature. With uneven prior probabilities of the two states, as the noise temperature is increased, non-monotonic evolutions are reported for the performance, which does not uniformly degrade. Regimes are found where higher noise temperatures are more favourable to detection, with relation to stochastic resonance effects where noise reveals beneficial to information processing.
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