1,269 research outputs found

    Gravitational Mesoscopic Constraints in Cosmological Dark Matter Halos

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    We present an analysis of the behaviour of the `coarse-grained' (`mesoscopic') rank partitioning of the mean energy of collections of particles composing virialized dark matter halos in a Lambda-CDM cosmological simulation. We find evidence that rank preservation depends on halo mass, in the sense that more massive halos show more rank preservation than less massive ones. We find that the most massive halos obey Arnold's theorem (on the ordering of the characteristic frequencies of the system) more frequently than less massive halos. This method may be useful to evaluate the coarse-graining level (minimum number of particles per energy cell) necessary to reasonably measure signatures of `mesoscopic' rank orderings in a gravitational system.Comment: LaTeX, 15 pages, 3 figures. Accepted for publication in Celestial Mechanics and Dynamical Astronomy Journa

    Ácidos fenólicos, flavonoides e atividade antioxidante em méis de Melipona fasciculata, M. flavolineata (Apidae, Meliponini) e Apis mellifera (Apidae, Apini) da Amazônia.

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    PHENOLIC ACIDS, FLAVONOIDS AND ANTIOXIDANT ACTIVITY IN HONEY OF Melipona fasciculata, M. flavolineata (Apidae, Meliponini) AND Apis mellifera (Apidae, Apini) FROM THE AMAZON. Honey produced by three stingless bee species (Melipona flavolineata, M. fasciculata and Apis mellifera) from different regions of the Amazon was analyzed by separating phenolic acids and flavonoids using the HPLC technique. Data were subjected to multivariate statistical analysis (PCA, HCA and DA). Results showed the three species of honey samples could be distinguished by phenolic composition. Antioxidant activity of the honeys was determined by studying the capacity of inhibiting radicals using DPPH assay. Honeys with higher phenolic compound contents had greater antioxidant capacity and darker color

    An Exact Algorithm for Side-Chain Placement in Protein Design

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    Computational protein design aims at constructing novel or improved functions on the structure of a given protein backbone and has important applications in the pharmaceutical and biotechnical industry. The underlying combinatorial side-chain placement problem consists of choosing a side-chain placement for each residue position such that the resulting overall energy is minimum. The choice of the side-chain then also determines the amino acid for this position. Many algorithms for this NP-hard problem have been proposed in the context of homology modeling, which, however, reach their limits when faced with large protein design instances. In this paper, we propose a new exact method for the side-chain placement problem that works well even for large instance sizes as they appear in protein design. Our main contribution is a dedicated branch-and-bound algorithm that combines tight upper and lower bounds resulting from a novel Lagrangian relaxation approach for side-chain placement. Our experimental results show that our method outperforms alternative state-of-the art exact approaches and makes it possible to optimally solve large protein design instances routinely
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