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International audience; Sulfur dioxide (SO 2) can enhance the formation of secondary aerosols from biogenic volatile organic compounds (VOCs), but its influence on secondary aerosol formation from anthropogenic VOCs, particularly complex mixtures like vehicle exhaust, remains uncertain. Gasoline vehicle exhaust (GVE) and SO 2 , a typical pollutant from coal burning, are directly co-introduced into a smog chamber, in this study, to investigate the formation of secondary organic aerosols (SOA) and sulfate aerosols through photooxidation. New particle formation was enhanced, while substantial sul-fate was formed through the oxidation of SO 2 in the presence of high concentration of SO 2. Homogenous oxidation by OH radicals contributed a negligible fraction to the conversion of SO 2 to sulfate, and instead the oxidation by stabilized Criegee intermediates (sCIs), formed from alkenes in the exhaust reacting with ozone, dominated the conversion of SO 2. After 5 h of photochemical aging, GVE's SOA production factor revealed an increase by 60-200 % in the presence of high concentration of SO 2. The increase could principally be attributed to acid-catalyzed SOA formation as evidenced by the strong positive linear correlation (R 2 = 0.97) between the SOA production factor and in situ particle acidity calculated by the AIM-II model. A high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS) resolved OA's relatively lower oxygen-to-carbon (O : C) (0.44 ± 0.02) and higher hydrogen-to-carbon (H : C) (1.40 ± 0.03) molar ratios for the GVE / SO 2 mixture, with a significantly lower estimated average carbon oxidation state (OS c) of −0.51 ± 0.06 than −0.19 ± 0.08 for GVE alone. The relative higher mass loading of OA in the experiments with SO 2 might be a significant explanation for the lower SOA oxidation degree.

Sub1A, an ethylene-response-factor-like (ERE-like) gene, mediates the extinguished submergence tolerance of rice. To gain further insight into the function of Sub1A in other species, we transformed tobacco plants with the gene under the control of the ubiquitin promoter. Compared to the wild-type plants, transgenic plants over-expressing Sub1A exhibited a greater ability to adapt to hypoxia, as evidenced by the highly induced activities of enzymes (pyruvate decarboxylase and alcohol dehydrogenase) regulating ethanolic fermentation. Furthermore, Sub1A upregulated activities of the main antioxidant enzymes, such as superoxide dismutase, ascorbate peroxidase and catalase, making the transgenic plants scavenge reactive oxygen species (ROS) more effectively. This was further confirmed by the less accumulation of malondialdehyde, an end product of lipid peroxidation. Taken together, our results suggest that Sub1A promotes plants hypoxic stress tolerance by regulating genes involved in anaerobic metabolism as well as ROS amelioration. In addition, it also suggests that Sub1A can be used potentially to improve hypoxic stress tolerance in plant breeding.Key words: Hypoxic stress, Sub1A, Tobacco, transgenic plants.

In this paper, the robust exponential stability problem of uncertain discrete-time recurrent neural networks with timevarying delay is investigated. By constructing a new augmented Lyapunov-Krasovskii function, some new improved stability criteria are obtained in forms of linear matrix inequality (LMI). Compared with some recent results in literature, the conservatism of the new criteria is reduced notably. Two numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results., {"references":["Y. He, et al., Stability analysis for neural networks with time-varying\ninterval delay, IEEE Trans. Neural Netw., 18 (2007) 1850-1854.","J. Qiu, J. Cao, Delay-dependent exponential stability for a class of neural\nnetworks with time delays and reactionCdiffusion terms, J. Franklin Inst.,\n4 (2009) 301-314.","J. Wang, L. Huang and Z. Guo, Dynamical behavior of delayed Hopfield\nneural networks with discontinuous activations, Appl. Math. 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In this paper, some new nonlinear generalized Gronwall-Bellman-Type integral inequalities with mixed time delays are established. These inequalities can be used as handy tools to research stability problems of delayed differential and integral dynamic systems. As applications, based on these new established inequalities, some p-stable results of a integro-differential equation are also given. Two numerical examples are presented to illustrate the validity of the main results., {"references":["A. Halanay, Differential Equations: Stability, Oscillations, Time Lags,\nAcademic Press, New York, NY, USA, 1966.","R. Agarwal, Y. Kim, and S. Sen, Advanced Discrete Halanay-Type\nInequalities: Stability of Difference Equations, Volume 2009, Article ID\n535849, 11 pages doi:10.1155/2009/535849.","J. L, On Some New Impulsive Integral Inequalities, Journal of Inequalities\nand Applications Volume 2008, Article ID 312395, 8 pages\ndoi:10.1155/2008/312395.","D. Xu, Z. Yang, Impulsive delay diffrential inequality and stability of\nneural networks, Journal of Mathematical Analysis and Applications, 305\n(2005) 107-120.","Z. Ma, X. Wang, A new singular impulsive delay dierential inequality\nand its application, Journal of Inequalities and Applications, Accepted\nArticle.","W. Cheung, D. Zhao, Gronwall-Bellman-Type Integral Inequalities and\napplications to BVPs, Journal of Inequalities and Applications, Accepted\nArticle.","Y. Yang, J. Cao, Solving Quadratic Programming Problems by Delayed\nProjection Neural Network, IEEE Transactions on neural metworks. 17\n(2006) 1630-1634.","X. Hu, Applications of the general projection neural network in solving\nextended linear-quadratic programming problems with linear constraints,\nNeurocomputing (2008), doi:10.1016/j.neucom.2008.02.016","Y. Yang, J. Cao, A feedback neural network for solving convex ..., Appl.\nMath. Comput. (2008), doi:10.1016/j.amc.2007.12.029\n[10] Li P et al., Delay-dependent robust BIBO stabilization ..., Chaos,\nSolitons and Fractals (2007), doi:10.1016/j.chaos.2007.08.059\n[11] O. Lipovan, A Retarded Gronwall-Like Inequality and Its Applications,\nJournal of Mathematical Analysis and Applications 252 (2000) 389-401\n.\n[12] R. Agarwal, S. Deng and W. Zhang, Generalization of a retarded\nGronwall-like inequality and its applications, Applied Mathematics and\nComputation 165 (2005) 599-612.\n[13] A. Gallo, A. Piccirillo, About new analogies of GronwallCBellmanCBihari\ntype inequalities for discontinuous functions and estimated solutions\nfor impulsive differential systems, Nonlinear Analysis 67 (2007) 1550-\n1559.\n[14] W. Wang, A generalized retarded Gronwall-like inequality in two variables\nand applications to BVP, Applied Mathematics and Computation\n191 (2007) 144-154.\n[15] W. Zhang, S. Deng, Projected GronwallCBellmans inequality for integral\nfunctions, Mathematical and Computer Modelling. 34 (2001) 393-\n402.\n[16] X. Mao, Stochastic Differential Equations and Applications, Horwood\nPublication, Chichester, 1997.\n[17] Yonghui Sun,Jinde Cao, pth moment exponential stability of stochastic\nrecurrent neural networks with time-varying delays, Nonlinear Analysis:\nRealWorld Applications, 8 (2007) 1171-1185.\n[18] Y. Xia, Z. Huang, M. Han, Exponential p-stability of delayed Cohen-\nGrossberg-type BAM neural networks with impulses, Chaos, Solitons and\nFractals, 38 (2008) 806-818.\n[19] L. Sheng, H. Yang, Exponential synchronization of a class of neural\nnetworks with mixed time-varying delays and impulsive effects, Neurocomputing,\n71 (2008) 3666-3674.\n[20] H. Kopka and P. W. Daly, A Guide to LATEX, 3rd ed. Harlow, England:\nAddison-Wesley, 1999."]}

This paper studies the pth moment exponential synchronization of a class of stochastic neural networks with mixed delays. Based on Lyapunov stability theory, by establishing a new integrodifferential inequality with mixed delays, several sufficient conditions have been derived to ensure the pth moment exponential stability for the error system. The criteria extend and improve some earlier results. One numerical example is presented to illustrate the validity of the main results., {"references":["T .L. Carroll, L .M. Pecora. Synchronization in chaotic systems. Phys.\nRev. Lett, 1990, 64: 821-824.","T .L. Carroll, L .M. Pecora. Synchronizing chaotic circuits. IEEE Trans.\nCirc. Syst, 1991, 38: 453-456.","T .Li, S .M. Fei, K .J. Zhang. Synchronization control of recurrent neural\nnetworks with distributed delays. Physica A, 2008, 387: 982-996.","T .Liu, G .M. Dimirovskib, J .Zhao. Exponential synchronization of\ncomplex delayed dynamical networks with general topology. Phys. Lett.\nA, 2008, 387: 643-652.","X .Lou, B .Cui. Synchronization of neural networks based on parameter\nidentification and via output or state coupling, Journal of Computational\nand Applied Mathematics (2007), doi:10.1016/j.cam.2007.11.015.","S .Li. et al., Adaptive exponential synchronization of delayed ..., Chaos,\nSolitons, Fractals (2007), doi:10.1016/j.chaos.2007.08.047.","T . Li, S .M. Fei, Q .Zhu, S .Song. Exponential synchronization of\nchaotic neural networks with mixed delays, Neurocomputing (2008),\ndoi:10.1016/j.neucom.2007.12.029.","J .Yan, J .Lin, M .Hung, T .Liao. On the synchronization of neural\nnetworks containing time-varying delays and sector nonlinearity, Phys.\nLett. A, 2007, 361: 70-77.","Y .Dai, Y .Z. Cai, X .M. Xu. Synchronization and Exponential Estimates\nof Complex Networks with Mixed Time-varying Coupling Delays. Int.\nJour. Auto. Comput.,, 2007, 4(1): 100-106.\n[10] Y .Q. Yang, J .D. Cao. Exponential lag synchronization of a class of\nchaotic delayed neural networks with impulsive effects. Physica A, 2007,\n386: 492-502.\n[11] W .Yu, J .D. Cao. Adaptive synchronization and lag synchronization\nof uncertain dynamical system with time delay based on parameter\nidentification. Phys. Lett. A,, 2007, 375: 467-482\n[12] Q .J. Zhang, J .A. Lu. Chaos synchronization of a new chaotic system\nvia nonlinear control. Chaos, Solitons and Fractals, 2008, 37: 175-179\n[13] H .G. Zhang, Y .H. Xie, Z .L. Wang. Adaptive synchronization between\ntwo different chaotic neural networks with time delay. IEEE Transaction\non Neural Networks, 2007, 18: 1841-1845.\n[14] C.T.H. Baker, E. Buckwar. Exponential stability in pth mean of solutions,\nand of convergent Euler-type solutions, of stochastic delay differential\nequations. J. Comput. Appl. Math, 2005, 184: 404-427.\n[15] J .W. Luo. A note on exponential stability in pth mean of solutions of\nstochastic delay differential equations. J. Comput. Appl. Math, 2007, 198:\n143-148.\n[16] Z .G. Yang, D .Y. Xu, L .Xiang. Exponential p-stability of impulsive\nstochastic differential equations with delays. Phys. Lett. A, 2006, 359:\n129-137.\n[17] X .R. Mao. Exponential stability in mean square of neutral stochastic\ndifferential functional equations. Sys. Contr.Lett, 1995, 26: 245-251.\n[18] X .R. Mao. Razumikhin type theorems on exponential stability of neutral\nstochastic functional differential equations. SIAM J. Math. Anal, 1997,\n28(2): 389-401.\n[19] J. Randjelovi'c, S. Jankovi'c. On the pth moment exponential stability\ncriteria of neutral stochastic functional differential equations. J. Math.\nAnal. Appl, 2007, 326: 266-280.\n[20] Y .H. Sun, J .D. Cao. pth moment exponential stability of stochastic\nrecurrent neural networks with time-varying delays. Nonlinear Anal: Real.\nworld. Appl., 2007, 8: 1171-1185.\n[21] L .Wan, J .Sun. Mean square exponential stability of stochastic delayed\nHopfield neural networks. Phys. Lett. A, 2005, 343:306-318.\n[22] C .X. Huang, et al. pth moment stability analysis of stochastic recurrent\nneural networks with time-varying delays. Inf.Sci., 2008, 178: 2194-2203.\n[23] S .J. Wu, D .Han, X .Z. Meng. p-Moment stability of stochastic\ndifferential equations with jumps. J. Comput. Appl. Math, 2004, 152:\n505-519.\n[24] S .J. Wu, X .L. Guo, Y .Zhou. p-moment stability of functional\ndifferential equations with random impulsive. Comput. Math. Appl., 2006,\n52: 1683-1694.\n[25] H .J. Wu, J .T. Sun. p-Moment stability of stochastic differential\nequations with impulsive jump and Markovian switching. Automatica,\n2006, 42: 1753-1759.\n[26] X .R. Mao. Stochastic Differential Equation and Application, Horwood\nPublishing, Chichester, 1997.\n[27] D .Y. Xu,Z .Wei, S. J. Long. Global exponential stability of impulsive\nintegro-differential equation. Nonlinear Analysis, 2006, 64: 2805-2816."]}