To clarify the effects of lung function following exposure to diesel engine exhaust (DEE), we recruited 137 diesel engine testing workers exposed to DEE and 127 non-DEE-exposed workers as study subjects. We performed lung function tests and measured cytokinesis-block micronucleus (CBMN) cytome index and levels of urinary polycyclic aromatic hydrocarbons (PAHs) metabolites. There was a significant decrease of forced expiratory volume in 1 second (FEV1), ratio of forced expiratory volume in 1 second to forced vital capacity (FEV1/ FVC), maximal mid expiratory flow curve (MMF), forced expiratory flow at 50% of FVC (FEF50%), and forced expiratory flow at 75% of FVC (FEF75%) in the DEE-exposed workers than non-DEE-exposed workers (all p<0.05). Among all study subjects, the decreases of FEF75% were associated with the increasing levels of PAHs metabolites (p<0.05), and there were negative correlations between FEV1, FEV1/FVC, MMF, FEF50%, and FEF75% with CBMN cytome index (all p<0.05). Our results show that long-term exposure to DEE can induce lung function decline which shows mainly obstructive changes and influence of small airways function. The decreased lung function is associated with internal dosage of DEE exposure, and accompany with the increasing CBMN cytome index.

CC16, as an immunosuppressive protein, and CC16/SP-D can be used as sensitive and noninvasive biomarkers for lung injury. Smoking should be banned in chromium workplaces.

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How to determine the camera's next best view is a challenging problem in vision field. A next best view approach is proposed based on occlusion information in a single depth image. First, the occlusion detection is accomplished for the depth image of visual object in current view to obtain the occlusion boundary and the nether adjacent boundary. Second, the external surface of occluded region is constructed and modeled according to the occlusion boundary and the nether adjacent boundary. Third, the observation direction, observation center point, and area information of external surface of occluded region are solved. And then, the set of candidate observation directions and the visual space of each candidate direction are determined. Finally, the next best view is achieved by solving the next best observation direction and camera's observation position. The proposed approach does not need the prior knowledge of visual object or limit the camera position on a specially appointed surface. Experimental results demonstrate that the approach is feasible and effective. KeywordsDepth image, next best view, occlusion information, external surface of occluded region, visual space Date

Random noise attenuation of seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) is much lower, which brings great difficulty to seismic data processing and interpretation. To this end, we propose an unconventional and effective seismic random noise attenuation method based on proximal classifier with consistency (PCC) in transform domain. Firstly, we analyze various transforms for seismic data from traditional wavelet transform and curvelet transform to emerging non-subsampled shearlet transform (NSST) and non-subsampled contourlet transform (NSCT). And, we select the excellent NSST to decompose the noisy seismic data into different sub-bands of frequency and orientation responses. Secondly, unlike traditional sparse transform based seismic denoising methods that often directly use a thresholding operator and corresponding inverse transform to denoise seismic data, our proposed method employs a superior performance PCC to classify the NSST coefficients of seismic data before thresholding operator. The added step can effectively divide the NSST coefficients into reflected useful signal coefficients and noise-related coefficients, which can preserve the edge of reflected signals and keep the information of events intact as much as possible. In addition, we also introduce an adaptive threshold computing method and a soft-thresholding method to achieve seismic data denoising better. Finally, the experimental results on the typical synthetic example and real seismic data show the superior performance of the proposed method. INDEX TERMS Seismic data, random noise, attenuation, proximal classifier with consistency (PCC), non-subsampled shearlet transform (NSST).

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