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Articles by L Bao
Total Records ( 2 ) for L Bao
  Y Zhang , L Bao , H Zhu , B Huang and H. Zhang

Leptospirosis renal disease is one of the common clinical manifestations of leptospirosis, including acute renal failure and tubulointerstitial nephritis. Outer membrane protein A-like protein Loa22 is a lipoprotein from Leptospira interrogans and has been suggested to be a corresponding virulence factor. However, the role of Loa22 in leptospiral nephropathy is not yet understood. In the present study, we constructed a vector and artificially expressed Loa22 in Escherichia coli BL21(DE)pLysS cells. After extensive purification, along with a GST tag protein control, Loa22 protein was used to test the cytotoxicity in cultured rat proximal tubule cells (NRK52E) and examine its effects on the induction of inflammatory responses. Using morphological examination, 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-5-[(phenylamino)carbonyl]-2H-tetrazoium hydrixide absorbance, lactate dehydrogenase assays and an analysis of apoptosis via flow cytometry, it was found that Loa22 protein mediates a direct cytotoxic effect on NRK52E cells in a dose-dependent manner. Using real-time PCR, western blotting and immunofluorescence, it was found that Loa22 protein upregulates the expression of toll-like receptor 2 (TLR2), induces nitric oxide synthase and promotes the production of nitric oxide (NO) and monocyte chemoattractant protein-1 (MCP-1) by NRK52E cells. Additionally, using a TLR2 blocking antibody, it was found that enhanced NO and MCP-1 production by NRK52E cells after Loa22 stimulation requires the activation of TLR2. Collectively, our data suggested that Loa22 is a critical virulence factor of L. interrogans and is involved in the leptospiral nephropathy through mediating direct cytotoxicity and enhancing inflammatory responses.

  Y Zhang , L Bao , S. H Yang , M Welling and D. Wu

In wireless sensor networks (WSNs), localization has many important applications, among which wireless sensor retrieval bears special importance for cost saving, data analysis and security purposes. Localization for sensor retrieval is especially challenging due to the fact that the number and locations of these sensors are both unknown. In this paper, we propose two probabilistic localization algorithms that iteratively identify the locations of multiple wireless sensors in WSNs, one of which calculates location information offline, and the other online. In both algorithms, we implement a two-step localization process — the first step is called Grid-LEGMM (grid location estimation based on the Gaussian mixture model), a coarse-grain location search using grids by choosing the proper number and locations of the wireless sensors that maximize a likelihood estimation, and the second step is called EM-LEGMM (expectation maximization based on the Gaussian mixture model), which uses the EM-method to refine the results of Grid-LEGMM. An additional step in the online localization algorithm is a credit-based filtering mechanism that removes spurious sensor locations. The performance of both offline and online localization algorithms are analyzed using the Cramer–Rao lower bound (CRLB), and evaluated using simulations and real testbed experiments.

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