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Microstructural features and mechanical properties are closely related in all soft biological tissues. Both yet exhibit considerable inter-individual differences and are affected by factors such as aging and disease and its progression. Histological analysis, modern in situ imaging, and biomechanical testing have deepened our understanding of these complex interrelations, yet two key questions remain: (1) Given the specific microstructure, can one predict the macroscopic mechanical properties without mechanical testing? (2) Can one quantify individual contributions of the different microstructural features to the macroscopic mechanical properties in an automated, systematic and largely unbiased way? Here we propose a bidirectional deep learning architecture to address these two questions. Our architecture uses data from standard histological analyses, two-photon microscopy and biaxial biomechanical testing. Its capabilities are demonstrated by predicting with high accuracy () the evolving mechanical properties of the murine aorta during maturation and aging. Moreover, our architecture reveals that the extracellular matrix composition and organization are the most prominent factors governing the macroscopic mechanical properties of the tissues studied herein.

U ovom radu razvijen je numerički materijalni model koji može opisati transmuralne promjene u pojedinim slojevima stijenke krvne žile. Modelirani su rast i promjena strukture (G&R) intime, medije i adventicije. Do sada razvijeni modeli omogućuju uvid većinom u geometrijske parametre (npr. promjer, debljina stijenke i sl.) dok postojeći model daje uvid u promjenu kuta otvaranja i osnog opterećenja, što su vrlo bitne veličina pri eksperimentalnom pristupu.

Arteries exhibit a remarkable ability to adapt in response to sustained alterations in hemodynamic loading as well as in response to disease, injury, and clinical treatment. A better understanding of such adaptations will be aided greatly by formulating, testing, and refining appropriate theoretical frameworks for modeling the biomechanics and associated mechanobiology. The goal of this brief review is to highlight some recent developments in the use of a constrained mixture theory of arterial growth and remodeling, with particular attention to the requisite constitutive relations, and to highlight future directions of needed research.

Measurements of hydrodynamic conditions and vertical suspended sediment concentration profiles, C-profiles, have been obtained above rippled sandy beds in a large wave flume. Measured values of wave height and period, water depth and sediment properties are used in well-known formulae to predict sediment dynamics and bed shear stresses due to waves. These data are then used in an existing convective model and a new model to predict C-profiles. Measured C-profiles and C-profiles predicted by the new model are shown to agree well for a range of grain sizes and wave conditions. Grain-scale bed roughness, defined using data from the model, is found to vary with the wave mobility number. Values for the wave mixing coefficient derived using the new model are tested and are found to agree well with published values and with theory.