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Translocation of multiwalled carbon nanotubes (MWCNTs) from the lung to the pleural cavity, deposition of the fibers in the pleural tissue, induction of pleural fibrosis, and mesothelial proliferation have been found in rodents administered MWCNTs by different pulmonary exposure methods. However, whether the translocation and deposition and the subsequent pleural inflammation are associated with the pleural lesions is unclear. In the present study, male F344 rats were given 250 μg of two types of MWCNTs, with crocidolite as a positive control, 2 times/week for 4 weeks by intratracheal spraying. At 24 h and at 3 months after the last spraying, the rats were sacrificed for histological examination of the lung and chest wall; pleural cavity lavage was also collected at sacrifice for observation of pleural inflammatory reactions. The results indicated that intratracheally sprayed MWCNTs, like crocidolite fibers, translocated into the pleural cavity, deposited in the pleura, and induced persistent infiltration of immune cells into the pleural cavity, persistent pleural fibrosis, and mesothelial proliferation. The number of MWCNT fibers detected in the pleural cavity lavage was parallel to the number of infiltrating immune cells, which were mainly composed of macrophages. Analysis of cytokines in the fluids of the pleural cavity lavages by suspension array indicated that levels of IL-2, IL-18, and IP-10 were significantly increased both at 24 h and at 3 months after the last spraying. In vitro proliferation assays revealed that a mixture of IL-2, IL-18, and IP-10, but not any of these cytokines alone, promoted cell proliferation of human fibroblasts and mesothelial cells. These results suggest that translocated and deposited MWCNTs induce subsequent pleural inflammation and that increased IL-2, IL-18, and IP-10 synergistically promote the development of pleural fibrosis and mesothelial proliferation.

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Target recognition performance can be affected by radar waveform parameters. In this paper, we established rigorous relationship between target recognition efficiency and the parameters of a repeatedly transmitted waveform. It is based on Kullback-Leibler Information Number of single observation (KLIN s ), which measures the dissimilarity between targets depicted by a range-velocity double spread density function in frequency domain. We considered two signal models which are different in the coherence of the observations. The method we proposed takes advantage of the methodology of sequential hypothesis test, and then the recognition performance in terms of correct classification rate is expressed by Receiver Operating Characteristic (ROC). Simulation results about the parameters of LFM signal show the validity of the method.

The optimal waveform for extended target recognition is directly affected by the target impulse response, which is sensitive to the target aspect. Hence, the variation of target aspect needs to be considered when the target is moving. Aiming at this problem, a new framework of cognitive radar is proposed. It predicts the new aspect via least square support vector machines (LSSVM) by using the prior knowledge of target aspect, and then obtains the optimal waveform based on not only the updated prior probabilities of the target hypothesis but also the updated TIR in a circular of interrogation. Simulations part shows the loss of recognition efficiency for a moving target when treated as static by the method in previous literature, and proves the validity of the proposed method.

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