Endocytic proteins with prion-like domains form viscoelastic condensates that enable membrane remodeling.

Membrane invagination and vesicle formation are key steps in endocytosis and cellular trafficking. Here, we show that endocytic coat proteins with prion-like domains (PLDs) form hemispherical puncta in the budding yeast, Saccharomyces cerevisiae These puncta have the hallmarks of biomolecular condensates and organize proteins at the membrane for actin-dependent endocytosis. They also enable membrane remodeling to drive actin-independent endocytosis. The puncta, which we refer to as endocytic condensates, form and dissolve reversibly in response to changes in temperature and solution conditions. We find that endocytic condensates are organized around dynamic protein-protein interaction networks, which involve interactions among PLDs with high glutamine contents. The endocytic coat protein Sla1 is at the hub of the protein-protein interaction network. Using active rheology, we inferred the material properties of endocytic condensates. These experiments show that endocytic condensates are akin to viscoelastic materials. We use these characterizations to estimate the interfacial tension between endocytic condensates and their surroundings. We then adapt the physics of contact mechanics, specifically modifications of Hertz theory, to develop a quantitative framework for describing how interfacial tensions among condensates, the membrane, and the cytosol can deform the plasma membrane to enable actin-independent endocytosis.

Fuzzy logic based classification and assessment of pathological voice signals.

In this paper an efficient fuzzy wavelet packet (WP) based feature extraction method and fuzzy logic based disorder assessment technique were used to investigate voice signals of patients suffering from unilateral vocal fold paralysis (UVFP). Mother wavelet function of tenth order Daubechies (d10) was employed to decompose signals in 5 levels. Next, WP coefficients were used to measure energy and Shannon entropy features at different spectral sub-bands. Consequently, using fuzzy c-means method, signals were clustered into 2 classes. The amount of fuzzy membership of pathological and normal signals in their corresponding clusters was considered as a measure to quantify the discrimination ability of features. A classification accuracy of 100 percent was achieved using an artificial neural network classifier. Finally, fuzzy c-means clustering method was used as a way of voice pathology assessment. Accordingly, fuzzy membership function based health index is proposed.