Group A streptococcus (GAS) is an important human pathogen, and its invasion via blood vessels is critically important in serious events such as bacteremia or multiorgan failure. Although GAS was identified as an extracellular bacterium, the internalization of GAS into nonphagocytic cells may provide a strategy to escape from immune surveillance and antibiotic killing. However, GAS has also been reported to induce autophagy and is efficiently killed within lysosome-fused autophagosomes in epithelial cells. In this study, we show that GAS can replicate in endothelial cells and that streptolysin O is required for GAS growth. Bacterial replication can be suppressed by altering GAS gene expression in an acidic medium before internalization into endothelial cells. The inhibitory effect on GAS replication can be reversed by treatment with bafilomycin A1, a specific inhibitor of vacuolar-type H+-ATPase. Compared with epithelial cells in which acidification causes autophagy-mediated clearance of GAS, there was a defect in acidification of GAS-containing vesicles in endothelial cells. Consequently, endothelial cells fail to maintain low pH in GAS-containing autophagosomes, thereby permitting GAS replication inside LAMP-1- and LC3-positive vesicles. Furthermore, treatment of epithelial cells with bafilomycin A1 resulted in defective GAS clearance by autophagy, with subsequent bacterial growth intracellularly. Therefore, low pH is a key factor for autophagy-mediated suppression of GAS growth inside epithelial cells, while defective acidification of GAS-containing vesicles results in bacterial growth in endothelial cells.

Abstract-Recent years, a new SAR concept based on Multi-Input Multi-Output (MIMO) configuration has demonstrated the potential advantages to simultaneously improve the performance of Synthetic Aperture Radar (SAR) imaging and ground moving target detection by utilizing multiple antennas both at transmission and reception. However, the precise signal model, as well as the effect of ground moving target in image domain and the approaches for moving target indication based on MIMO SAR system are rarely investigated. Our paper has three main contributions. Firstly, we present a detailed signal model for stationary scene and moving target based on a colocated MIMO SAR configuration, and analyze the motion effect of the moving target. Secondly, we provide an algorithm of phase compensation to combine the multiple virtual channel data in order to enhance the image quality. Thirdly, an adaptive optimal approach is applied for clutter suppression, then the velocity of the moving target is estimated via Delay-and-Sum (DAS) beamforming approach. Finally, several numerical experiments are provided to illustrate the derivation and analysis in this paper.

Abstract-In this paper, we focus on target detection and system configuration optimization of Multiple-input Multiple-output (MIMO) radar in low-grazing angle, where the multipath effects are very abundant. The performance of detection can be improved via utilizing the multipath echoes. First, the reflection coefficient, considering the curved earth effect, is derived. Then, the general signal model for MIMO radar is introduced for low-grazing angle. Using the NeymanPearson sense, the detector of MIMO radar with multipath is analyzed. We use the deflection coefficient as a criterion of system configuration both for MIMO radar and phased-array radar. The simulation results show that the performance can be enhanced markedly when the multipath effects are considered, and the optimal configuration of phased-array radar is with the same number of transmitters as that of receivers, however, the optimal configuration of MIMO radar depends on the signal-to-noise ratio (SNR).

Abstract-In this paper, we investigate an expectation-maximization (EM) maximum likelihood (ML) algorithm of direction finding (DF) for bistatic multiple-input multiple-output (MIMO) radar, where it is shown that the DF problem can be described as a special case of ML estimation with incomplete data. First, we introduce the signal and the noise models, and derive the ML estimations of the direction parameters. Considering the computational complexity, we make use of the EM algorithm to compute the ML algorithm, referred to EM ML algorithm, which can be applied to the arbitrary antenna geometry and realize the auto-pairing between direction-of-departures (DODs) and direction-of-arrivals (DOAs). Then the initialization is considered. In addition, both the convergence and the Cramer-Rao bound (CRB) analysis are derived. Finally, simulation results demonstrate the potential and asymptotic efficiency of this approach for MIMO radar systems.

Abstract-This paper focuses on the fluctuating target detection in low-grazing angle using Multiple-input Multiple-output (MIMO) radar systems with widely separated antennas, where the multipath effects are very abundant. The performance of detection can be improved via utilizing the multipath echoes, which is equivalent to improve the signal-to-noise ratio (SNR) by using multipath echoes. First, the reflection coefficient considering the curved earth effect is derived. Then, the general signal model for MIMO radar is introduced for fluctuating target in low-grazing angle. Using the Neyman-Pearson sense, the detectors of fluctuating targets, i.e., Swerling 1-4, with multipath are analyzed. Finally, the simulation results show that the performance can be enhanced markedly when the multipath effects are considered.