Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for further cellular enhancements toward 5G. Compared to orthogonal multiple access (OMA) such as orthogonal frequency-division multiple access (OFDMA), large performance gains were confirmed via system-level simulations. However, NOMA link-level simulations and the design of the receiver remain of great importance to validate NOMA performance gains. In this paper, we evaluate downlink NOMA link-level performance with multiple receiver designs and propose a novel NOMA transmitter and receiver design, where the signals of multi-users are jointly modulated at transmitter side and detected at receiver side. The predominant advantage of the proposed scheme is that at receiver side interference cancellation to the interference signal is not needed, thus low complexity is achieved. The performances of codewordlevel SIC, symbol-level SIC and the proposed receiver are evaluated and compared with ideal SIC. Simulation results show that compared with ideal SIC, downlink NOMA link-level performance depends on actual receiver design and the difference in the power ratio split between the cell edge user and cell center user. In particular, it is shown that codeword-level SIC and the proposed receiver can both provide a good performance even when the power ratio difference between the cell center user and cell edge user is small and with real channel estimation.

A novel transformation system, in which neither a nonphysiological concentration of Ca2+ and temperature shifts nor electronic shocks were required, was developed to determine whether Escherichia coli is naturally transformable. In the new protocol, E. coli was cultured normally to the stationary phase and then cultured statically at 37 degrees C in Luria-Bertani broth. After static culture, transformation occurred in bacteria spread on Luria-Bertani plates. The protein synthesis inhibitor chloramphenicol inhibited this transformation process. The need for protein synthesis in plated bacteria suggests that the transformation of E. coli in this new system is regulated physiologically.

Hepatitis C virus (HCV) infection is a leading cause of chronic liver diseases and hepatocellular carcinoma (HCC) and Golgi protein 73 (GP73) is a serum biomarker for liver diseases and HCC. However, the mechanism underlying GP73 regulates HCV infection is largely unknown. Here, we revealed that GP73 acts as a novel negative regulator of host innate immunity to facilitate HCV infection. GP73 expression is activated and correlated with interferon-beta (IFN-β) production during HCV infection in patients’ serum, primary human hepatocytes (PHHs) and human hepatoma cells through mitochondrial antiviral signaling protein (MAVS), TNF receptor-associated factor 6 (TRAF6) and mitogen-activated protein kinase kinase/extracellular regulated protein kinase (MEK/ERK) pathway. Detailed studies revealed that HCV infection activates MAVS that in turn recruits TRAF6 via TRAF-interacting-motifs (TIMs), and TRAF6 subsequently directly recruits GP73 to MAVS via coiled-coil domain. After binding with MAVS and TRAF6, GP73 promotes MAVS and TRAF6 degradation through proteasome-dependent pathway. Moreover, GP73 attenuates IFN-β promoter, IFN-stimulated response element (ISRE) and nuclear factor κB (NF-κB) promoter and down-regulates IFN-β, IFN-λ1, interleukin-6 (IL-6) and IFN-stimulated gene 56 (ISG56), leading to the repression of host innate immunity. Finally, knock-down of GP73 down-regulates HCV infection and replication in Huh7-MAVSR cells and primary human hepatocytes (PHHs), but such repression is rescued by GP73m4 (a mutant GP73 resists to GP73-shRNA#4) in Huh7-MAVSR cells, suggesting that GP73 facilitates HCV infection. Taken together, we demonstrated that GP73 acts as a negative regulator of innate immunity to facilitate HCV infection by interacting with MAVS/TRAF6 and promoting MAVS/TRAF6 degradation. This study provides new insights into the mechanism of HCV infection and pathogenesis, and suggests that GP73 is a new potential antiviral target in the prevention and treatment of HCV associated diseases.

Network intrusion detection system (NIDS) is a commonly used tool to detect attacks and protect networks, while one of its general limitations is the false positive issue. On the basis of our comparative experiments and analysis for the characteristics of the particle swarm optimization (PSO) and Xgboost, this paper proposes the PSO-Xgboost model given its overall higher classification accuracy than other alternative models such like Xgboost, Random Forest, Bagging and Adaboost. Firstly, a classification model based on Xgboost is constructed, and then PSO is used to adaptively search for the optimal structure of Xgboost. The benchmark NSL-KDD dataset is used to evaluate the proposed model. Our experimental results demonstrate that PSO-Xgboost model outperforms other comparative models in precision, recall, macro-average (macro) and mean average precision (mAP), especially when identifying minority groups of attacks like U2R and R2L. This work also provides experimental arguments for the application of swarm intelligence in NIDS.

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