Evaluating the Viscoelastic Properties of Tissue from Laser Speckle Fluctuations

Most pathological conditions such as atherosclerosis, cancer, neurodegenerative, and orthopedic disorders are accompanied with alterations in tissue viscoelasticity. Laser Speckle Rheology (LSR) is a novel optical technology that provides the invaluable potential for mechanical assessment of tissue in situ. In LSR, the specimen is illuminated with coherent light and the time constant of speckle fluctuations, τ, is measured using a high speed camera. Prior work indicates that τ is closely correlated with tissue microstructure and composition. Here, we investigate the relationship between LSR measurements of τ and sample mechanical properties defined by the viscoelastic modulus, G*. Phantoms and tissue samples over a broad range of viscoelastic properties are evaluated using LSR and conventional mechanical testing. Results demonstrate a strong correlation between τ and |G*| for both phantom (r = 0.79, p <0.0001) and tissue (r = 0.88, p<0.0001) specimens, establishing the unique capability of LSR in characterizing tissue viscoelasticity

Assessing the potential of an algorithm based on mean climatic data to predict wheat yield

peer reviewedThe real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de micro-capteurs en vue d’optimiser l’apport en engrais azoté