Predicting stress, strain and deformation fields in materials and structures with graph neural networks.

Developing accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-based paradigm. Although impressive achievements have been reached, efficiently predicting complex physical phenomena in materials and structures remains a challenge.

Dynamic Measurements Using FDM 3D-Printed Embedded Strain Sensors.

3D-printing technology is opening up new possibilities for the co-printing of sensory elements. While quasi-static research has shown promise, the dynamic performance has yet to be researched. This study researched smart 3D structures with embedded and printed sensory elements.

Structure and Functional Properties of the Active Form of the Proteolytic Complex, ClpP1P2, from Mycobacterium tuberculosis.

The ClpP protease complex and its regulatory ATPases, ClpC1 and ClpX, inMycobacterium tuberculosis(Mtb) are essential and, therefore, promising drug targets. TheMtbClpP protease consists of two heptameric rings, one composed of ClpP1 and the other of ClpP2 subunits. Formation of the enzymatically active ClpP1P2 complex requires binding of N-blocked dipeptide activators.

Mitochondrial ClpP activity is required for cisplatin resistance in human cells.

In human cells ClpP and ClpX are imported into the mitochondrial matrix, where they interact to form the ATP-dependent protease ClpXP and play a role in the mitochondrial unfolded protein response. We find that reducing the levels of mitochondrial ClpP or ClpX renders human cancer cells more sensitive to cisplatin, a widely used anti-cancer drug. Conversely, overexpression of HClpP desensitizes cells to cisplatin.