The tongue surface houses a range of papillae that are integral to the mechanics and chemistry of taste and textural sensation. Although gustatory function of papillae is well investigated, the uniqueness of papillae within and across individuals remains elusive. Here, we present the first machine learning framework on 3D microscopic scans of human papillae (n = 2092), uncovering the uniqueness of geometric and topological features of papillae. The finer differences in shapes of papillae are investigated computationally based on a number of features derived from discrete differential geometry and computational topology. Interpretable machine learning techniques show that persistent homology features of the papillae shape are the most effective in predicting the biological variables. Models trained on these features with small volumes of data samples predict the type of papillae with an accuracy of 85%. The papillae type classification models can map the spatial arrangement of filiform and fungiform papillae on a surface. Remarkably, the papillae are found to be distinctive across individuals and an individual can be identified with an accuracy of 48% among the 15 participants from a single papillae. Collectively, this is the first unprecedented evidence demonstrating that tongue papillae can serve as a unique identifier inspiring new research direction for food preferences and oral diagnostics.

The white rigid back to the fingertip is covered with the black flexible 3D-printed skin. Machines can beat the world's best chess player, but they cannot handle a chess piece as well as an infant. This lack of robot dexterity is partly because artificial grippers lack the fine tactile sense of the human fingertip, which is used to guide our hands as we pick up and handle objects. Two papers published in the Journal of the Royal Society Interface give the first in-depth comparison of an artificial fingertip with neural recordings of the human sense of touch. The research was led by Professor of Robotics & AI (Artificial Intelligence), Nathan Lepora, from the University of Bristol's Department of Engineering Maths and based at the Bristol Robotics Laboratory.

There are many reasons to admire the octopus, including its ability to instantly pop up tiny protrusions of various shapes from its skin to match the texture of its background. This technique, combined with other camouflage tricks such as changing its color, allow an octopus to blend into to almost anything--even boats. Those protrusions, called dermal papillae, were the bio-inspiration behind a new elastic material that can morph into various shapes, and could provide a shape-shifting surface for soft robots. Researchers from Cornell University in New York and the Marine Biological Laboratory in Massachusetts decided to build a material based on muscle groups that control papillae along the surface of an octopus tentacle. The material consists of a fiber mesh that simulates an octopus's erector muscles, which contract to squeeze a protrusion into shape.