Abscisic acid (ABA) mediates resistance to abiotic stress and controls developmental processes in plants. The group-A PP2Cs, of which ABI1 is the prototypical member, are protein phosphatases that play critical roles as negative regulators very early in ABA signal transduction. Because redundancy is thought to limit the genetic dissection of early ABA signalling, to identify redundant and early ABA signalling proteins, we pursued a proteomics approach. We generated YFP-tagged ABI1 Arabidopsis expression lines and identified in vivo ABI1-interacting proteins by mass-spectrometric analyses of ABI1 complexes. Known ABA signalling components were isolated including SnRK2 protein kinases. We confirm previous studies in yeast and now show that ABI1 interacts with the ABA-signalling kinases OST1, SnRK2.2 and SnRK2.3 in plants. Interestingly, the most robust in planta ABI1-interacting proteins in all LC-MS/MS experiments were nine of the 14 PYR/PYL/RCAR proteins, which were recently reported as ABA-binding signal transduction proteins, providing evidence for in vivo PYR/PYL/RCAR interactions with ABI1 in Arabidopsis. ABI1–PYR1 interaction was stimulated within 5 min of ABA treatment in Arabidopsis. Interestingly, in contrast, PYR1 and SnRK2.3 co-immunoprecipitated equally well in the presence and absence of ABA. To investigate the biological relevance of the PYR/PYLs, we analysed pyr1/pyl1/pyl2/pyl4 quadruple mutant plants and found strong insensitivities in ABA-induced stomatal closure and ABA-inhibition of stomatal opening. These findings demonstrate that ABI1 can interact with several PYR/PYL/RCAR family members in Arabidopsis, that PYR1–ABI1 interaction is rapidly stimulated by ABA in Arabidopsis and indicate new SnRK2 kinase-PYR/PYL/RCAR interactions in an emerging model for PYR/PYL/RCAR-mediated ABA signalling.

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a b s t r a c tIn this paper the application of the Particle Swarm Optimization (PSO) method to estimate the Weibull parameters for wind resources in the Brazilian Northeast Region (BRNER) is reported. For the present research, wind speed data from three 80 m towers installed at different sites in the region were collected. The measuring periods for each tower site are: Petrolina. Aiming to compare with the PSO performance, five numerical methods are applied to calculate the Weibull distribution parameters. Best performance for all analyzed sites is achieved by the PSO method, with a correlation higher than 99% and an error close to zero. PSO proves to be a valuable technique for characterization of the particular wind conditions found in the BRNER.

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