The requirements for Ca2+, protein phosphorylation and concurrent protein synthesis for zeatin signaling of acidic chitinase transcript accumulation in Cucumis sativus L

We have previously reported that both Ca2+ and staurosporine-sensitive protein kinase(s) are involved in the cytokinin zeatin induction of cucumber chitinase activity and its protein content (Barwe et al. 2001). To further characterize signal transduction events involved in this cytokinin induction of chitinase gene expression, Northern hybridizations of total RNAs prepared from excised, dark-grown cucumber cotyledons treated with cytokinins and/or various agonists and antagonists of signal transduction components, were carried out using a cucumber acidic chitinase (CACHT) cDNA probe (Metraux et al. 1989). CACHT mRNA increased by approximately 5- to 6-fold in response to exogenous zeatin (Z), zeatin riboside (ZR), and benzyladenine (BA) treatment, but failed to accumulate in response to kinetin (K). Among the cytokinins tested, Z was most effective. The Z-induced accumulation of CACHT mRNA was inhibited by a plasma membrane Ca2+ channel blocker verapamil. Treatment of cotyledons with exogenous CaCl2 and calcium ionophore A23187 in the presence and absence of cytokinin enhanced CACHT mRNA accumulation. These two observations suggest the participation of extracellular calcium in signaling Z-induction. Furthermore, the presence of staurosporine (an inhibitor of protein kinase) in Z treatment reduced CACHT mRNA, suggesting the involvement of phosphorylation of one or more cellular proteins. In addition, we provide evidence that the Z-induction of CACHT mRNA is blocked by protein synthesis inhibitor cycloheximide treatment. Taken together, these results suggest that Ca2+ influx from extracellular space, protein phosphorylation, and concurrent protein synthesis events participate in cytokinin signaling during Z-induced CACHT transcript accumulation.

Trans -> Cis isomerization of trans-[(dPPM)(2)Ru(H)(L)][BF4] (L = P(OR)(3)) Complexes: Preparation of cis-[(dppm)(2)Ru(eta(2)-H-2)(L)][BF4](2)

A series of new dicationic dihydrogen complexes of ruthenium of the type cis-[(dppm)(2)Ru(eta(2)-H-2)(L)][BF4](2) (dppm = Ph2PCH2PPh2; L = P(OMe)(3), P(OEt)(3), PF((OPr)-Pr-i)(2)) have been prepared by protonating the precursor hydride complexes cis-[(dppm)(2)Ru(H)(L)][BF4] (L = P(OMe)(3), P(OEt)(3), P((OPr)-Pr-i)(3)) using HBF4.Et2O. The cis-[(dppm)(2)Ru(H)(L)][BF4] complexes were obtained from the trans hydrides via an isomerization reaction that is acid-accelerated. This isomerization reaction gives mixtures of cis and trans hydride complexes, the ratios of which depend on the cone angles of the phosphite ligands: the greater the cone angle, the greater is the amount of the cis isomer. The eta(2)-H-2 ligand in the dihydrogen complexes is labile, and the loss of H-2 was found to be reversible. The protonation reactions of the starting hydrides with trans PMe3 or PMe2Ph yield mixtures of the cis and the trans hydride complexes; further addition of the acid, however, give trans-[(dPPM)(2)Ru(BF4)Cl]. The roles of the bite angles of the dppm ligand as well as the steric and the electronic properties of the monodentate phosphorus ligands in this series of complexes are discussed. X-ray crystal structures of trans-[(dppm)(2)Ru(H)(P(OMe)(3))][BF4], cis-[(dppm)(2)Ru-(H)(P(OMe)(3))][BF4], and cis-[(dppm)(2)Ru(H)(P((OPr)-Pr-i)(3))][BF4] complexes have been determined.

Regionalization of precipitation in data sparse areas using large scale atmospheric variables - A fuzzy clustering approach

Delineation of homogeneous precipitation regions (regionalization) is necessary for investigating frequency and spatial distribution of meteorological droughts. The conventional methods of regionalization use statistics of precipitation as attributes to establish homogeneous regions. Therefore they cannot be used to form regions in ungauged areas, and they may not be useful to form meaningful regions in areas having sparse rain gauge density. Further, validation of the regions for homogeneity in precipitation is not possible, since the use of the precipitation statistics to form regions and subsequently to test the regional homogeneity is not appropriate. To alleviate this problem, an approach based on fuzzy cluster analysis is presented. It allows delineation of homogeneous precipitation regions in data sparse areas using large scale atmospheric variables (LSAV), which influence precipitation in the study area, as attributes. The LSAV, location parameters (latitude, longitude and altitude) and seasonality of precipitation are suggested as features for regionalization. The approach allows independent validation of the identified regions for homogeneity using statistics computed from the observed precipitation. Further it has the ability to form regions even in ungauged areas, owing to the use of attributes that can be reliably estimated even when no at-site precipitation data are available. The approach was applied to delineate homogeneous annual rainfall regions in India, and its effectiveness is illustrated by comparing the results with those obtained using rainfall statistics, regionalization based on hard cluster analysis, and meteorological sub-divisions in India. (C) 2011 Elsevier B.V. All rights reserved.

Ferrocene-Conjugated L-Tryptophan Copper(II) Complexes of Phenanthroline Bases Showing DNA Photocleavage Activity and Cytotoxicity

Ferrocene-conjugated L-tryptophan (L-Trp) reduced Schiff base (Fc-TrpH) copper(II) complexes [Cu(Fc-Trp)(L)](ClO(4)) of phenanthroline bases (L), viz. 2,2'-bipyridine (bpy in 1), 1,10-phenanthroline (phen in 2), dipyrido[3,2-d:2',3'-f]quinoxaline (dpq in 3), and dipyrido[3,2-a:2',3'-c]phenazine (dppz in 4), were prepared and characterized and their photocytotoxicity studied. Cationic reduced Schiff base (Ph-TrpH) complexes [Cu(Ph-Trp)(L)(H(2)O)] (ClO(4)) (L = phen in 5; dppz in 6) having the ferrocenyl moiety replaced by a phenyl group and the Zn(II) analogue (7) of complex 4 were prepared and used as control species. The crystal structures of 1 and 5 with respective square-planar CuN(3)O and square-pyramidal CuN(3)O(2) coordination geometry show significantly different core structures. Complexes 1-4 exhibit a Cu(II)-Cu(I) redox couple near -0.1 V and the Fc(+)-Fc couple at similar to 0.5 V vs SCE in DMF-0.1 M [Bu(4)(n)N] (ClO(4)) (Fc = ferrocenyl moiety). The complexes display a copper(II)-based d-d band near 600 nm and a Fc-centered band at similar to 450 nm in DMF-Tris-HCl buffer. The complexes are efficient binders to calf thymus DNA. They are synthetic chemical nucleases in the presence of thiol or H(2)O(2), forming hydroxyl radicals. The photoactive complexes are cleavers of pUC19 DNA in visible light, forming hydroxyl radicals. Complexes 2-6 show photocytotoxicity in HeLa cancer cells, giving IC(50) values of 4.7, 10.2, 1.3, 4.8, and 4.3 mu M, respectively, in visible light with the appearance of apoptotic bodies. The complexes also show photocytotoxicity in MCF-7 cancer cells. Nuclear chromatin cleavage has been observed with acridine orange/ethidium bromide (AO/EB) dual staining with complex 4 in visible light. The complexes induce caspase-independent apoptosis in the HeLa cells.

Fuzzy Clustering Based Ad Recommendation for TV Programs

Advertisements(Ads) are the main revenue earner for Television (TV) broadcasters. As TV reaches a large audience, it acts as the best media for advertisements of products and services. With the emergence of digital TV, it is important for the broadcasters to provide an intelligent service according to the various dimensions like program features, ad features, viewers’ interest and sponsors’ preference. We present an automatic ad recommendation algorithm that selects a set of ads by considering these dimensions and semantically match them with programs. Features of the ad video are captured interms of annotations and they are grouped into number of predefined semantic categories by using a categorization technique. Fuzzy categorical data clustering technique is applied on categorized data for selecting better suited ads for a particular program. Since the same ad can be recommended for more than one program depending upon multiple parameters, fuzzy clustering acts as the best suited method for ad recommendation. The relative fuzzy score called “degree of membership” calculated for each ad indicates the membership of a particular ad to different program clusters. Subjective evaluation of the algorithm is done by 10 different people and rated with a high success score.

Minimization of Constraint Violation in Fuzzy Multiobjective Programming

Fuzzy multiobjective programming for a deterministic case involves maximizing the minimum goal satisfaction level among conflicting goals of different stakeholders using Max-min approach. Uncertainty due to randomness in a fuzzy multiobjective programming may be addressed by modifying the constraints using probabilistic inequality (e.g., Chebyshev’s inequality) or by addition of new constraints using statistical moments (e.g., skewness). Such modifications may result in the reduction of the optimal value of the system performance. In the present study, a methodology is developed to allow some violation in the newly added and modified constraints, and then minimizing the violation of those constraints with the objective of maximizing the minimum goal satisfaction level. Fuzzy goal programming is used to solve the multiobjective model. The proposed methodology is demonstrated with an application in the field of Waste Load Allocation (WLA) in a river system.

Fuzzy inference based data preprocessing for VBR video traffic prediction

Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural network and support vector regression and analyze their performance on H.264 video traces. Our experimental results reveal that data preprocessing greatly improves the performance of linear regression and neural network, but is not effective on support vector regression.