Modulation of Rab GTPase function by a protein phosphocholine transferase.

The intracellular pathogen Legionella pneumophila modulates the activity of host GTPases to direct the transport and assembly of the membrane-bound compartment in which it resides. In vitro studies have indicated that the Legionella protein DrrA post-translationally modifies the GTPase Rab1 by a process called AMPylation. Here we used mass spectrometry to investigate post-translational modifications to Rab1 that occur during infection of host cells by Legionella. Consistent with in vitro studies, DrrA-mediated AMPylation of a conserved tyrosine residue in the switch II region of Rab1 was detected during infection. In addition, a modification to an adjacent serine residue in Rab1 was discovered, which was independent of DrrA. The Legionella effector protein AnkX was required for this modification. Biochemical studies determined that AnkX directly mediates the covalent attachment of a phosphocholine moiety to Rab1. This phosphocholine transferase activity used CDP-choline as a substrate and required a conserved histidine residue located in the FIC domain of the AnkX protein. During infection, AnkX modified both Rab1 and Rab35, which explains how this protein modulates membrane transport through both the endocytic and exocytic pathways of the host cell. Thus, phosphocholination of Rab GTPases represents a mechanism by which bacterial FIC-domain-containing proteins can alter host-cell functions

MST-Based Semi-Supervised Clustering Using M-Labeled Objects

Most of the existing semi-supervised clustering algorithms depend on pairwise constraints, and they usually use lots of priori knowledge to improve their accuracies. In this paper, we use another semi-supervised method called label propagation to help detect clusters. We propose two new semi-supervised algorithms named K-SSMST and M-SSMST. Both of them aim to discover clusters of diverse density and arbitrary shape. Based on Minimum Spanning Tree's algorithm variant, K-SSMST can automatically find natural clusters in a dataset by using K labeled data objects where K is the number of clusters. M-SSMST can detect new clusters with insufficient semi-supervised information. Our algorithms have been tested on various artificial and UCI datasets. The results demonstrate that the algorithm's accuracy is better than other supervised and semi-supervised approaches

Data Arrangement With Rotation Transformation for Fully Polarimetric Synthetic Aperture Radar

This letter proposes a data arrangement for fully polarimetric synthetic aperture radar (PolSAR). It is an essential novel method in the use of the rotation transformation in data interpretation. The key point of the proposal is employing a single pixel-based and selective rotation transformation for each pixel before the speckle filtering. The experimental results with ALOS2-PALSAR2 data show that the proposed data arrangement has much higher performance in recognizing double-bounce scattering in the man-made target area. At the same time, it is effective in avoiding the overestimation of double-bounce and/or surface scattering in natural target areas

Deterministic End-to-End Transmission to Optimize the Network Efficiency and Quality of Service: A Paradigm Shift in 6G

Toward end-to-end mobile service provision with optimized network efficiency and quality of service, tremendous efforts have been devoted in upgrading mobile applications, transport and internet networks, and wireless communication networks for many years. However, the inherent loose coordination between different layers in the end-to-end communication networks leads to unreliable data transmission with uncontrollable packet delay and packet error rate, and a terrible waste of network resources incurred for data re-transmission. In an attempt to shed some lights on how to tackle these challenges, design methodologies and some solutions for deterministic end-to-end transmission for 6G and beyond are presented, which will bring a paradigm shift to the end-to-end wireless communication networks.Comment: 5 pages, 2 figure