Glycine rich segments adopt polyproline II helices: Implications for biomolecular condensate formation.

Many intrinsically disordered proteins contain Gly-rich regions which are generally assumed to be disordered. Such regions often form biomolecular condensates which play essential roles in organizing cellular processes. However, the bases of their formation and stability are still not completely understood. Based on NMR studies of the Gly-rich H. harveyi "snow flea" antifreeze protein, we recently proposed that Gly-rich sequences, such as the third "RGG" region of Fused in Sarcoma (FUS) protein, may adopt polyproline II helices whose association might stabilize condensates. Here, this hypothesis is tested with a polypeptide corresponding to the third RGG region of FUS. NMR spectroscopy and molecular dynamics simulations suggest that significant populations of polyproline II helix are present. These findings are corroborated in a model peptide Ac-RGGYGGRGGWGGRGGY-NH2, where a peak characteristic of polyproline II helix is observed using CD spectroscopy. Its intensity suggests a polyproline II population of 40%. This result is supported by data from FTIR and NMR spectroscopies. In the latter, NOE correlations are observed between the Tyr and Arg, and Arg and Trp side chain hydrogens, confirming that side chains spaced three residues apart are close in space. Taken together, the data are consistent with a polyproline II helix, which is bent to optimize interactions between guanidinium and aromatic moieties, in equilibrium with a statistical coil ensemble. These results lend credence to the hypothesis that Gly-rich segments of disordered proteins may form polyproline II helices which help stabilize biomolecular condensates.

Ventricular Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning.

BACKGROUND AND OBJECTIVE: To safely select the proper therapy for Ventricullar Fibrillation (VF) is essential to distinct it correctly from Ventricular Tachycardia (VT) and other rhythms. Provided that the required therapy would not be the same, an erroneous detection might lead to serious injuries to the patient or even cause Ventricular Fibrillation (VF). The main novelty of this paper is the use of time-frequency (t-f) representation images as the direct input to the classifier. We hypothesize that this method allow to improve classification results as it allows to eliminate the typical feature selection and extraction stage, and its corresponding loss of information. METHODS: The standard AHA and MIT-BIH databases were used for evaluation and comparison with other authors. Previous to t-f Pseudo Wigner-Ville (PWV) calculation, only a basic preprocessing for denoising and signal alignment is necessary. In order to check the validity of the method independently of the classifier, four different classifiers are used: Logistic Regression with L2 Regularization (L2 RLR), Adaptive Neural Network Classifier (ANNC), Support Vector Machine (SSVM), and Bagging classifier (BAGG). RESULTS: The main classification results for VF detection (including flutter episodes) are 95.56% sensitivity and 98.8% specificity, 88.80% sensitivity and 99.5% specificity for ventricular tachycardia (VT), 98.98% sensitivity and 97.7% specificity for normal sinus, and 96.87% sensitivity and 99.55% specificity for other rhythms. CONCLUSION: Results shows that using t-f data representations to feed classifiers provide superior performance values than the feature selection strategies used in previous works. It opens the door to be used in any other detection applications.

Sistema hardware de diagnostico de fibrilación ventricular en tiempo real mediante métodos de analisis tiempo-frecuencia / System hardware diagnostic of ventricular fibrillation in real time using methods of time-frequency analysis

Nowadays our society has to face different diseases related to cardiac pathologies which become more and more common. This is due to the daily life habits increasing the risk of suffering cardiac problems such as heart attack which could lead to death...