Effects of theacrine on the astringency of EGCG by affecting salivary protein - EGCG interactions through different molecular mechanisms.

(-)-Epigallocatechin gallate (EGCG) and theacrine are involved in imparting tea with its astringent and bitter tastes. This study investigated the effect of theacrine on the astringency of EGCG and its molecular mechanism. Sensory evaluation was used to study the astringent intensities of EGCG solutions in the presence and absence of various concentrations of theacrine. The results indicated a considerable increase in the astringency values of EGCG-theacrine solutions compared with those of EGCG solutions alone. Furthermore, dynamic light scattering (DLS) and molecular dynamics simulations (MD) were to explore the interaction mechanisms. The results revealed that theacrine increased the particle size of EGCG-proline-rich proteins (PRPs) aggregates with that of EGCG and PRPs alone. MD revealed that theacrine potentially acted as a bridge between EGCG and PRPs, promoting their interaction and intensifying the EGCG's astringency. However, theacrine could not bridge two or more mucins owing to the substantial spatial structure of mucin.

Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning.

An acoustic transmitter can be located by having multiple static microphones. These microphones are synchronized and measure the time differences of arrival (TDoA). Usually, the positions of the microphones are assumed to be known in advance. However, in practice, this means they have to be manually measured, which is a cumbersome job and is prone to errors. In this paper, we present two novel approaches which do not require manual measurement of the receiver positions. The first method uses an inertial measurement unit (IMU), in addition to the acoustic transmitter, to estimate the positions of the receivers. By using an IMU as an additional source of information, the non-convex optimizers are less likely to fall into local minima. Consequently, the success rate is increased and measurements with large errors have less influence on the final estimation. The second method we present in this paper consists of using machine learning to learn the TDoA signatures of certain regions of the localization area. By doing this, the target can be located without knowing where the microphones are and whether the received signals are in line-of-sight or not. We use an artificial neural network and random forest classification for this purpose.