Machine learning and topological data analysis identify unique features of human papillae in 3D scans.

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 ([Formula: see text]), 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 evidence demonstrating that tongue papillae can serve as a unique identifier, and inspires a new research direction for food preferences and oral diagnostics.

Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data.

Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.

3D Biomimetic Tongue-Emulating Surfaces for Tribological Applications.

Oral friction on the tongue surface plays a pivotal role in mechanics of food transport, speech, sensing, and hedonic responses. The highly specialized biophysical features of the human tongue such as micropapillae-dense topology, optimum wettability, and deformability present architectural challenges in designing artificial tongue surfaces, and the absence of such a biomimetic surface impedes the fundamental understanding of tongue-food/fluid interaction. Herein, we fabricate for the first time, a 3D soft biomimetic surface that replicates the topography and wettability of a real human tongue. The 3D-printed fabrication contains a Poisson point process-based (random) papillae distribution and is employed to micromold soft silicone surfaces with wettability modifications. We demonstrate the unprecedented capability of these surfaces to replicate the theoretically defined and simulated collision probability of papillae and to closely resemble the tribological performances of human tongue masks. These de novo biomimetic surfaces pave the way for accurate quantification of mechanical interactions in the soft oral mucosa.