The rapid development in nanoparticle production and application during the past decade requires an easy, rapid, and predictive screening method for nanoparticles toxicity assay. In this study, the toxicological effects and the source of toxicity of copper nanoparticles (CuNPs) are investigated based on a stress-responsive bacterial biosensor array. According to the responses of the biosensing strains, it is found that CuNPs induce not only oxidative stress in E. coli, but also protein damage, DNA damage, and cell membrane damage, and ultimately cause cell growth inhibition. Through enzyme detoxification analysis, the toxicological effects of CuNPs are traced to H(2)O(2) generation from CuNPs. Rapid copper release from CuNPs and Cu(I) production are observed. The oxidation of the released Cu(I) has a close relation to H(2)O(2) production, as tris-(hydroxypropyltriazolylmethyl) amine, the specific Cu(I) chelator, can largely protect the cells from the toxicity of CuNPs. In addition, the TEM study shows that CuNPs can be adsorbed and incepted fast by the cells. Comparatively, copper microparticles are relatively stable in the system and practically non-toxic, which indicates the importance of toxic estimation of materials at the nanoscale. In addition, the Cu(II) ion can induce protein damage, membrane damage, and slight DNA damage only at a relatively high concentration. The current study reveals the preliminary mechanism of toxicity of CuNPs, and suggests that the stress-responsive bacterial biosensor array can be used as a simple and promising tool for rapid screening in vitro toxicity of nanoparticles and studying the primary mechanism of the toxicity.

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With the increasing scale of cloud computing applications of next-generation embedded systems, a major challenge that domain scientists are facing is how to efficiently store and analyze the vast volume of output data. Compression can reduce the amount of data that needs to be transferred and stored. However, most of the large datasets are in floating-point format, which exhibits high entropy. As a result, existing lossless compressors can not provide enough performance for such applications. To address this problem, we propose a total variation reduction method for improving the compression ratio of lossless compressors (namely, FPC + and FPZIP + ), which employs a median-based hyperplane to precondition the data. In particular, we first try to exploit the space-filling curve (SFC), a well-known technique to preserve data locality for a multi-dimensional dataset. We show and explain why a raw SFC, such as Hilbert and Z-order curves, cannot improve the compression ratio. Then, we explore the opportunity and theoretical feasibility of the proposed total variation reduction based algorithm. The experiment results show the effectiveness of the proposed method. The compression ratios are improved up to 48.2% (20.6% on average) for FPZIP and 42.4% (18.4% on average) for FPC. Moreover, through observing the time composition of the proposed method, it is found that the median finding holds a high percentage of the execution time. Hence, we further introduce an approximate median finding algorithm, providing a linear-time overhead reduction scheme. The experiment results clearly demonstrate that this algorithm reduces execution time by an average of 56.7% and 40.7% compared to FPC + and FPZIP + , respectively.

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