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Enhanced phytoextraction proposes the use of soil amendments to increase the heavy-metal content of above-ground harvestable plant tissues. This study compares the effect of synthetic aminopolycarboxylic acids [ethylenediamine tetraacetatic acid (EDTA), nitriloacetic acid (NTA), and diethylenetriamine pentaacetic acid (DTPA)] with a number of biodegradable, low-molecular weight, organic acids (citric acid, ascorbic acid, oxalic acid, salicylic acid, and NH4 acetate) as potential soil amendments for enhancing phytoextraction of heavy metals (Cu, Zn, Cd, Pb, and Ni) by Zea mays. The treatments in this study were applied at a dose of 2 mmol/kg(-1) 1 d before sowing. To compare possible effects between presow and postgermination treatments, a second smaller experiment was conducted in which EDTA, citric acid, and NH4 acetate were added 10 d after germination as opposed to 1 d before sowing. The soil used in this screening was a moderately contaminated topsoil derived from a dredged sediment disposal site. This site has been in an oxidized state for more than 8 years before being used in this research. The high carbonate, high organic matter, and high clay content characteristic to this type of sediment are thought to suppress heavy-metal phytoavailability. Both EDTA and DTPA resulted in increased levels of heavy metals in the above-ground biomass. However, the observed increases in uptake were not as large as reported in the literature. Neither the NTA nor organic acid treatments had any significant effect on uptake when applied prior to sowing. This was attributed to the rapid mineralization of these substances and the relatively low doses applied. The generally low extraction observed in this experiment restricts the use of phytoextraction as an effective remediation alternative under the current conditions, with regard to amendments used, applied dose (2 mmol/kg(-1) soil), application time (presow), plant species (Zea mays), and sediment (calcareous clayey soil) under study.

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In this paper, we present a distributed computing framework for problems characterized by a highly irregular search tree, whereby no reliable workload prediction is available. The framework is based on a peer-to-peer computing environment and dynamic load balancing. The system allows for dynamic resource aggregation, does not depend on any specific meta-computing middleware and is suitable for large-scale, multi-domain, heterogeneous environments, such as computational Grids. Dynamic load balancing policies based on global statistics are known to provide optimal load balancing performance, while randomized techniques provide high scalability. The proposed method combines both advantages and adopts distributed job-pools and a randomized polling technique. The framework has been successfully adopted in a parallel search algorithm for subgraph mining and evaluated on a molecular compounds dataset. The parallel application has shown good calability and close-to linear speedup in a distributed network of workstations.

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Empirical orthogonal functions (EOFs) are widely used in climate research to identify dominant patterns of variability and to reduce the dimensionality of climate data. EOFs, however, can be difficult to interpret. Rotated empirical orthogonal functions (REOFs) have been proposed as more physical entities with simpler patterns than EOFs. This study presents a new approach for finding climate patterns with simple structures that overcomes the problems encountered with rotation. The method achieves simplicity of the patterns by using the main properties of EOFs and REOFs simultaneously. Orthogonal patterns that maximise variance subject to a constraint that induces a form of simplicity are found. The simplified empirical orthogonal function (SEOF) patterns, being more 'local'. are constrained to have zero loadings outside the main centre of action. The method is applied to winter Northern Hemisphere (NH) monthly mean sea level pressure (SLP) reanalyses over the period 1948-2000. The 'simplified' leading patterns of variability are identified and compared to the leading patterns obtained from EOFs and REOFs. Copyright (C) 2005 Royal Meteorological Society.

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Summary: The program LVB seeks parsimonious phylogenies from nucleotide alignments, using the simulated annealing heuristic. LVB runs fast and gives high quality results.

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We present argon predissociation vibrational spectra of the OH-.H2O and Cl-.H2O complexes in the 1000-1900 cm(-1) energy range, far below the OH stretching region reported in previous studies. This extension allows us to explore the fundamental transitions of the intramolecular bending vibrations associated with the water molecule, as well as that of the shared proton inferred from previous assignments of overtones in the higher energy region. Although the water bending fundamental in the Cl-.H2O spectrum is in very good agreement with expectations, the OH-.H2O spectrum is quite different than anticipated, being dominated by a strong feature at 1090 cm(-1). New full-diniensionality calculations of the OH-.H2O vibrational level structure using diffusion Monte Carlo and the VSCF/CI methods indicate this band arises from excitation of the shared proton.

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Thirty one new sodium heterosulfamates, RNHSO3Na, where the R portion contains mainly thiazole, benzothiazole, thiadiazole and pyridine ring structures, have been synthesized and their taste portfolios have been assessed. A database of 132 heterosulfamates ( both open-chain and cyclic) has been formed by combining these new compounds with an existing set of 101 heterosulfamates which were previously synthesized and for which taste data are available. Simple descriptors have been obtained using (i) measurements with Corey-Pauling-Koltun (CPK) space- filling models giving x, y and z dimensions and a volume VCPK, (ii) calculated first order molecular connectivities ((1)chi(v)) and (iii) the calculated Spartan program parameters to obtain HOMO, LUMO energies, the solvation energy E-solv and V-SPART AN. The techniques of linear (LDA) and quadratic (QDA) discriminant analysis and Tree analysis have then been employed to develop structure-taste relationships (SARs) that classify the sweet (S) and non-sweet (N) compounds into separate categories. In the LDA analysis 70% of the compounds were correctly classified ( this compares with 65% when the smaller data set of 101 compounds was used) and in the QDA analysis 68% were correctly classified ( compared to 80% previously). TheTree analysis correctly classified 81% ( compared to 86% previously). An alternative Tree analysis derived using the Cerius2 program and a set of physicochemical descriptors correctly classified only 54% of the compounds.