Monodispersed silver selenide (Ag 2 Se) nanoparticles have been prepared successfully by a hydrothermal reaction of AgNO 3 with Na 2 SeSO 3 in the presence of poly(vinyl pyrrolidone) (PVP) and KI at 180 C for 20 h. TEM revealed that the nanoparticles are much like husked rice with lengths of about 60-80 nm and widths of about 30-40 nm. KI was dispersed in a PVP solution first, then an appropriate amount of AgNO 3 solution was slowly added until the molar ratio of Ag + to I À reached 1 : 1. The formed complex ions of [Ag m I n ] (nÀm)À and AgI in the procedure are more stable owing to the protection effect of PVP, or these could be considered as complexes of PVP-[Ag m I n ] (nÀm)À and PVP-AgI. These complexes functioned as the precursors; the formation rate of Ag 2 Se crystal cores in the hydrothermal reaction could be well controlled. The final shape and size of the product were affected by the amount of PVP and the molar ratio of I À to Ag + , and higher PVP content was beneficial to the formation of the husked rice-like shape. The Ag 2 Se nanoparticles can be used for the detection of DNA hybridization. A specific DNA sequence related to the PEP promoter gene in transgenic plants was determined with a detection range from 1.0 Â 10 À12 to 1.0 Â 10 À8 mol L À1 and a detection limit of 2.3 Â 10 À13 mol L À1 (3s). The method had good selectivity and was successfully used to distinguish between a three-base mismatched ssDNA sequence, a non-complementary sequence and a complementary sequence. Experimental ReagentsSelenium powder, Na 2 SO 3 , AgNO 3 , KI, cetyltrimethyl ammonium bromide (CTAB), poly(vinyl pyrrolidone) (PVP, molecular

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We present a novel model, named Category Constraint-Latent Dirichlet Allocation (CC-LDA), to learn and recognize natural scene category. Previous work had to resort to additional classifier after obtaining image topic representation. Our model puts the category information in topic inference, so every category is represented in a different topics simplex and topic size, which is consistent with human cognitive habit. The significant feature in our model is that it can do discrimination without combined additional classifier, during the same time of getting topic representation. We investigate the classification performance with variable scene category tasks. The experiments have demonstrated that our learning model can get better performance with less training data.