6 resultados para Tree-like Decomposition

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP)


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Hundreds of tropical plant species house ant colonies in specialized chambers called domatia. When, in 1873, Richard Spruce likened plant-ants to fleas and asserted that domatia are ant-created galls, he incited a debate that lasted almost a century. Although we now know that domatia are not galls and that most ant-plant interactions are mutualisms and not parasitisms, we revisit Spruce`s suggestion that ants can gall in light of our observations of the plant-ant Myrmelachista schumanni, which creates clearings in the Amazonian rain forest called ""supay-chakras,"" or ""devil`s gardens."" We observed swollen scars on the trunks of nonmyrmecophytic canopy trees surrounding supay-chakras, and within these swellings, we found networks of cavities inhabited by M. schumanni. Here, we summarize the evidence supporting the hypothesis that M. schumanni ants make these galls, and we hypothesize that the adaptive benefit of galling is to increase the amount of nesting space available to M. schumanni colonies.

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Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification problems. However, a large number of real problems present more than two classes. A common approach to generalize binary learning techniques to solve problems with more than two classes, also known as multiclass classification problems, consists of hierarchically decomposing the multiclass problem into multiple binary sub-problems, whose outputs are combined to define the predicted class. This strategy results in a tree of binary classifiers, where each internal node corresponds to a binary classifier distinguishing two groups of classes and the leaf nodes correspond to the problem classes. This paper investigates how measures of the separability between classes can be employed in the construction of binary-tree-based multiclass classifiers, adapting the decompositions performed to each particular multiclass problem. (C) 2010 Elsevier B.V. All rights reserved.

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Decomposition was studied in a reciprocal litter transplant experiment to examine the effects of forest type, litter quality and their interaction on leaf decomposition in four tropical forests in south-east Brazil. Litterbags were used to measure decomposition of leaves of one tree species from each forest type: Calophyllum brasiliense from restinga forest; Guapira opposita from Atlantic forest; Esenbeckia leiocarpa from semi-deciduous forest; and Copaifera langsdorffii from cerradao. Decomposition rates in rain forests (Atlantic and restinga) were twice as fast as those in seasonal forests (semi-deciduous and cerradao), suggesting that intensity and distribution of precipitation are important predictors of decomposition rates at regional scales. Decomposition rates varied by species, in the following order: E. leiocarpa > C. langsdorffii > G. opposita > C. brasiliense. However, there was no correlation between decomposition rates and chemical litter quality parameters: C:N, C:P, lignin concentration and lignin:N. The interaction between forest type and litter quality was positive mainly because C. langsdorffii decomposed faster than expected in its native forest. This is a potential indication of a decomposer`s adaptation to specific substrates in a tropical forest. These findings suggest that besides climate, interactions between decomposers and plants might play an essential role in decomposition processes and it must be better understood.

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We have sequenced genes encoding cathepsin L-like (CatL-like) cysteine proteases from isolates of Trypanosoma rangeli from humans, wild mammals and Rhodnius species of Central and South America. Phylogenetic trees of sequences encoding mature CatL-like enzymes of T rangeli and homologous genes from other trypanosomes, Leishmania spp. and bodonids positioned sequences of T rangeli (rangelipain) closest to T cruzi (cruzipain). Phylogenetic tree of kinetoplastids based on sequences of CatL-like was totally congruent with those derived from SSU rRNA and gGAPDH genes. Analysis of sequences from the CatL-like catalytic domains of 17 isolates representative of the overall phylogenetic diversity and geographical range of T rangeli supported all the lineages (A-D) previously defined using ribosomal and spliced leader genes. Comparison of the proteolytic activities of T rangeli isolates revealed heterogeneous banding profiles of cysteine proteases in gelatin gels, with differences even among isolates of the same lineage. CatL-like sequences proved to be excellent targets for diagnosis and genotyping of T rangeli by PCR. Data from CatL-like encoding genes agreed with results from previous studies of kDNA markers, and ribosomal and spliced leader genes, thereby corroborating clonal evolution, independent transmission cycles and the divergence of T rangeli lineages associated with sympatric species of Rhodnius. (c) 2009 Elsevier B.V. All rights reserved.

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Several popular Machine Learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary sub-problems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorithms to determine the tree structure taking into account information collected from the used dataset. This approach allows the tree structure to be determined automatically for any multiclass dataset.

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Increasing efforts exist in integrating different levels of detail in models of the cardiovascular system. For instance, one-dimensional representations are employed to model the systemic circulation. In this context, effective and black-box-type decomposition strategies for one-dimensional networks are needed, so as to: (i) employ domain decomposition strategies for large systemic models (1D-1D coupling) and (ii) provide the conceptual basis for dimensionally-heterogeneous representations (1D-3D coupling, among various possibilities). The strategy proposed in this article works for both of these two scenarios, though the several applications shown to illustrate its performance focus on the 1D-1D coupling case. A one-dimensional network is decomposed in such a way that each coupling point connects two (and not more) of the sub-networks. At each of the M connection points two unknowns are defined: the flow rate and pressure. These 2M unknowns are determined by 2M equations, since each sub-network provides one (non-linear) equation per coupling point. It is shown how to build the 2M x 2M non-linear system with arbitrary and independent choice of boundary conditions for each of the sub-networks. The idea is then to solve this non-linear system until convergence, which guarantees strong coupling of the complete network. In other words, if the non-linear solver converges at each time step, the solution coincides with what would be obtained by monolithically modeling the whole network. The decomposition thus imposes no stability restriction on the choice of the time step size. Effective iterative strategies for the non-linear system that preserve the black-box character of the decomposition are then explored. Several variants of matrix-free Broyden`s and Newton-GMRES algorithms are assessed as numerical solvers by comparing their performance on sub-critical wave propagation problems which range from academic test cases to realistic cardiovascular applications. A specific variant of Broyden`s algorithm is identified and recommended on the basis of its computer cost and reliability. (C) 2010 Elsevier B.V. All rights reserved.