Universidade Federal de Viçosa

About: Universidade Federal de Viçosa is a education organization based out in Viçosa, Brazil. It is known for research contribution in the topics: Population & Biology. The organization has 16012 authors who have published 26711 publications receiving 353416 citations.

Differential metabolic networks unravel the effects of silent plant phenotypes

Abstract: Current efforts aim to functionally characterize each gene in model plants. Frequently, however, no morphological or biochemical phenotype can be ascribed for antisense or knock-out plant genotypes. This is especially the case when gene suppression or knockout is targeted to isoenzymes or gene families. Consequently, pleiotropic effects and gene redundancy are responsible for phenotype resistance. Here, techniques are presented to detect unexpected pleiotropic changes in such instances despite very subtle changes in overall metabolism. The method consists of the relative quantitation of >1,000 compounds by GC/time-of-flight MS, followed by classical statistics and multivariate clustering. Complementary to these tools, metabolic networks are constructed from pair-wise analysis of linear metabolic correlations. The topology of such networks reflects the underlying regulatory pathway structure. A differential analysis of network connectivity was applied for a silent potato plant line suppressed in expression of sucrose synthase isoform II. Metabolic alterations could be assigned to carbohydrate and amino acid metabolism even if no difference in average metabolite levels was found.

Genes Software - extended and integrated with the R, Matlab and Selegen

Abstract: The Genes Software is useful for analyzing and processing phenotypic and molecular data using different biometric models. In the current version we dispose routines to integrate it with three other softwares: the R, Matlab and Selegen. This version allows in plant and animal breeding complementary analyzes in several breeding research fields as genomic selection, prediction of genetic values, use of neural networks and Fuzzy logic. The Genes is important to estimate parameters for understanding biological phenomena necessary to make decisions and predict the success and viability of strategic selection. The original programme can be downloaded in Portuguese, English or Spanish with the specific literature from (http://www.livraria.ufv.br/) and the user guide from (http://www.ufv.br/dbg/genes/genes.htm and http://www.ufv.br/dbg/biodata.htm). The user has also support in the address www.facebook.com/ GenesNews. The Genes is also integrated in the application softwares MS Word, MS Excel and Paint to efficiently import data and export results as numbers and figures.

Selectivity Sequence and Competitive Adsorption of Heavy Metals by Brazilian Soils

Abstract: Heavy-metal cations can be introduced into agricultural soils by application of fertilizers, liming materials, sewage sludge, composts, and other industrial and urban waste materials. Therefore, heavy-metal adsorption reactions, in a competitive system, are important to determine heavy-metal availability to plants and their mobility throughout the soil. This study was conducted to evaluate the selectivity sequence and estimate the competitive adsorption of several heavy metals in seven soils with different chemical and mineralogical characteristics. Distribution coefficients (K d ), which represent the sorption affinity of metals for the solid phase, were obtained for each soil and heavy-metal cation. On the basis of these K d , the selectivity sequence was evaluated. The most common sequences were Cr > Pb > Cu > > Cd > Zn > Ni and Pb > Cr > Cu > Cd > Ni > Zn. Chromium, Pb, and Cu were the heavy-metal cations most strongly adsorbed by all soils, whereas Cd, Ni, and Zn were the least adsorbed, in the competitive situation. Selectivity sequences related to valence for the trivalent Cr. For metals of the same valence, sequences did not exactly follow the order of electronegativity. For individual elements, the Misono softness parameter and hydrolysis properties of the heavy-metal cations may have influenced the sequences. Correlation analysis showed that soil characteristics that may have affected the heavy-metals adsorption, represented by the distribution coefficients, were pH and cation-exchange capacity (CEC) for Cd and Cr; organic carbon, clay, and gibbsite contents for Cu; pH and CEC for Ni and Pb.