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.

Do cyanobacterial lipids contain fatty acids longer than 18 carbon atoms?

Fatty acids of twelve species of cyanobacteria grown under different photoautotrophic conditions were studied and their composition was compared with literature data of many other species. We have come to the conclusion that the lipids of cyanobacteria do not contain fatty acids with a chain longer than 18 carbon atoms. In our opinion, omission of an analytical procedure, i.e. purification of fatty acid methyl esters before gas chromatography, leads to incorrect interpretation of the results. Absence or presence of fatty acids was suggested as a useful taxonomic marker and a proper diagnostic indicator in the commercial application of cyanobacterial biomass.