Diffusion and localization of proteins in the plasma membrane of Saccharomyces cerevisiae

Yeast (Saccharomyces cerevisiae) plasma membrane is unique, as diffusion is 3-4 orders of magnitude slower than in other known membranes. In this thesis, we study diffusion and localization of proteins in the yeast plasma membrane. In the yeast plasma membrane, proteins are not uniformly distributed, many of them show localization to distinct domains. MCC/eisosomes are one type of such domains. They are very stable compartments that do not change for longer periods of time than cell cycle of the yeast cells. We have developed a new method of immobilization of the cells allowing us to study them in more detail. Using that method, we looked at the diffusion and localization of proteins in relation to the MCC/eisosomes. Some proteins (like Can1p) are immobilized in the MCC/eisosomes, can diffuse through the MCC/eisosomes without immobilization (like Nha1), or are excluded from them (like Pma1p). However, the structure does not have an effect on diffusion coefficients of any of the groups. Additionally, we have discovered, that proteins can be excluded from the MCC/eisosomes due to large cytoplasmic domains close to the plasma membrane. We also tried to determine the cause(s) of the slow diffusion in the yeast plasma membrane, and while we think lipid composition of the membrane has major impact on it, we are still far from any conclusions

Improved DeepFake Detection Using Whisper Features

With a recent influx of voice generation methods, the threat introduced by audio DeepFake (DF) is ever-increasing. Several different detection methods have been presented as a countermeasure. Many methods are based on so-called front-ends, which, by transforming the raw audio, emphasize features crucial for assessing the genuineness of the audio sample. Our contribution contains investigating the influence of the state-of-the-art Whisper automatic speech recognition model as a DF detection front-end. We compare various combinations of Whisper and well-established front-ends by training 3 detection models (LCNN, SpecRNet, and MesoNet) on a widely used ASVspoof 2021 DF dataset and later evaluating them on the DF In-The-Wild dataset. We show that using Whisper-based features improves the detection for each model and outperforms recent results on the In-The-Wild dataset by reducing Equal Error Rate by 21%.Comment: Accepted to INTERSPEECH 202