This study identified 104 poplar WRKY genes and demonstrated WRKY gene hot spots on chromosome 14. Furthermore, semi-quantitative RT-PCR showed variable stress responses in subgroup III.

The production of cyanobacterial toxins microcystins (MCs) by cyanobacterial bloom which may promote the growth of tumor in human liver is a growing environmental problem worldwide. In this paper, the adsorption of MC-RR and LR, which were extracted from cyanobacterial cells in Dianchi Lake in China, by carbon nanotubes (CNTs), wood-based activated carbon (ACs) and clays were investigated. Compared with ACs and clay materials of sepiolite, kaolinite and talc tested, CNTs were found to have a strong ability in the adsorption of MCs. At the concentrations of 21.5 mg l À1 MC-RR and 9.6 mg l À1 MC-LR in 50 mmol phosphate buffer solution (pH 7.0), the adsorption amounts of MCs by CNTs with the range of outside diameter from 2 to 10 nm were 14.8 and 5.9 mg g À1 , which were about four times higher than those by other adsorbents tested. It was shown that with the decrease of CNTs outside diameters from 60 to 2 nm, the adsorption amount of MCs was apparently increased, however the size of CNTs particles formed in solution declined. This result implies that the size of CNTs tube pore that is fit for the molecular dimension of MCs plays a dominant role. Furthermore the specific surface area of CNTs was also found to be a factor in the adsorption of MCs. The results suggested that the selection of suitable size of CNTs as a kind of adsorbent is very important in the efficient eliminating MCs from drinking water in future.

No abstract

Food recognition is a key component in evaluation of everyday food intakes, and its challenge is due to intraclass variation. In this paper, we present an automatic food classification method, DietCam, which specifically addresses the variation of food appearances. DietCam consists of two major components, ingredient detection and food classification. Food ingredients are detected through a combination of a deformable part-based model and a texture verification model. From the detected ingredients, food categories are classified using a multiview multikernel SVM. In the experiment, DietCam presents reliability and outperformance in recognition of food with complex ingredients on a database including 15,262 food images of 55 food types.

This paper proposes the recognition framework of car makes and models from a single image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be captured in different viewpoints and lighting conditions, and the image quality varies in resolution and color depth. In the framework, cars are first detected using a part-based detector, and license plates and headlamps are detected as cardinal anchor points to rectify projective distortion. Car features are extracted, normalized, and classified using an ensemble of neural-network classifiers. In the experiment, the performance of the proposed method is evaluated on a data set of practical traffic images. The results prove the effectiveness of the proposed method in vehicle detection and model recognition. Index Terms-Vehicle identification, traffic surveillance, vehicle tracking, intelligent transportation.1524-9050