We propose a new approach for face recognition under arbitrary illumination conditions, which requires only one training image per subject (if there is no pose variation) and no 3D shape information. Our method is based on the recent result [1] which demostrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace. In this paper, we show that we can recover basis images spanning this space from just one image taken under arbitrary illumination conditions. First, using a bootstrap set consisting of 3D face models, we compute a statistical model for each basis image. During training, given a novel face image under arbitrary illumination, we recover a set of images for this face. We prove that these images are the set of basis images with maximum probability. During testing, we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image. We provide a series of experiments that achieve high recognition rates, under a wide range of illumination conditions, including multiple sources of illumination. Our method achieves comparable levels of accuracy with methods that have much more onerous training data requirements.

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Atmospheric benzene, toluene, ethylbenzene, and xylenes (BTEX) can lead to multiple health injuries. However, what remains uncertain is the effect of long-term exposure to low levels of BTEX. Thus, we determined the BTEX levels in the air from the refueling and office areas in gas stations. Then we collected workers’ (200 refueling vs. 52 office workers) peripheral blood samples to analyze the serum total-superoxide dismutase (T-SOD), glutathione (GSH), malondialdehyde (MDA), and 8-hydroxydeoxyguanosine (8-OHdG) levels. DNA damage was analyzed by the comet assay and micronucleus test in buccal epithelial cells. We found that the levels of BTEX in refueling areas were significantly higher than those in office areas (p < 0.001). The serum T-SOD and GSH of refueling workers were significantly lower than those in office workers (p < 0.001). By contrast, the serum MDA and 8-OHdG of refueling workers were significantly higher than those of office workers (p < 0.001, MDA; p = 0.025, 8-OHdG). Furthermore, tail and Olive tail moments in refueling workers were longer (p = 0.004, tail moment; p = 0.001, Olive tail moment), and the micronucleus rate was higher (p < 0.001) than those in office workers. Taken together, long-term exposure to low levels of BTEX may reduce the antioxidant ability and increase the risk of DNA damage in refueling workers of gas stations.

Diesel exhaust particles (DEPs) are common airborne ultrafine particles (UFPs); however, few studies have examined their effects on the gastrointestinal tract. To investigate the interaction of gut microbiota and DEPs‐induced colonic injury, adult C57BL/6 mice are kept in whole‐body inhalation chambers and exposed to filtered room air (FRA) or DEPs (300 µg m−3) 1 h per day for 28 consecutive days. DEPs exposure results in colon epithelial injury with inflammatory cell infiltration and mucus depletion. Abundance of Lactobacillus in murine feces is transiently increased following 7‐day DEPs exposure and then decreased until the end of 28‐day exposure. A reduction of the colonic mucus layer thickness is observed in mice receiving gut microbiota from DEPs‐exposed mice. Mechanistically, RNA‐sequencing suggests disruption of the nitrogen metabolism pathway in DEPs‐exposed NCM460 cells. Upregulation of carbonic anhydrase 9 (CA9) expression levels is observed in epithelia following DEPs exposure both in vivo and in vitro. Oral administration of probiotics protects the mice against DEPS‐induced colon epithelial injury. The results strongly suggest the involvement of gut microbiota in response to DEPs exposure and subsequently epithelial injury in vivo. Supplementation with probiotic may be a potential way to protect against UFPs‐induced colon epithelial injury.

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