Habitat simplification affects nuclear-follower foraging association among stream fishes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Statistical analysis of the error associated with the simplification of the stratigraphy in geotechnical models

The uncertainties in the determination of the stratigraphic profile of natural soils is one of the main problems in geotechnics, in particular for landslide characterization and modeling. The study deals with a new approach in geotechnical modeling which relays on a stochastic generation of different soil layers distributions, following a boolean logic – the method has been thus called BoSG (Boolean Stochastic Generation). In this way, it is possible to randomize the presence of a specific material interdigitated in a uniform matrix. In the building of a geotechnical model it is generally common to discard some stratigraphic data in order to simplify the model itself, assuming that the significance of the results of the modeling procedure would not be affected. With the proposed technique it is possible to quantify the error associated with this simplification. Moreover, it could be used to determine the most significant zones where eventual further investigations and surveys would be more effective to build the geotechnical model of the slope. The commercial software FLAC was used for the 2D and 3D geotechnical model. The distribution of the materials was randomized through a specifically coded MatLab program that automatically generates text files, each of them representing a specific soil configuration. Besides, a routine was designed to automate the computation of FLAC with the different data files in order to maximize the sample number. The methodology is applied with reference to a simplified slope in 2D, a simplified slope in 3D and an actual landslide, namely the Mortisa mudslide (Cortina d’Ampezzo, BL, Italy). However, it could be extended to numerous different cases, especially for hydrogeological analysis and landslide stability assessment, in different geological and geomorphological contexts.

View-Dependent Extraction of Contours with Distance Transforms for Adaptive Polygonal for adaptive polygonal Mesh-Simplification

During decades Distance Transforms have proven to be useful for many image processing applications, and more recently, they have started to be used in computer graphics environments. The goal of this paper is to propose a new technique based on Distance Transforms for detecting mesh elements which are close to the objects' external contour (from a given point of view), and using this information for weighting the approximation error which will be tolerated during the mesh simplification process. The obtained results are evaluated in two ways: visually and using an objective metric that measures the geometrical difference between two polygonal meshes.

Mechanism of topology simplification by type II DNA topoisomerases

Type II DNA topoisomerases actively reduce the fractions of knotted and catenated circular DNA below thermodynamic equilibrium values. To explain this surprising finding, we designed a model in which topoisomerases introduce a sharp bend in DNA. Because the enzymes have a specific orientation relative to the bend, they act like Maxwell's demon, providing unidirectional strand passage. Quantitative analysis of the model by computer simulations proved that it can explain much of the experimental data. The required sharp DNA bend was demonstrated by a greatly increased cyclization of short DNA fragments from topoisomerase binding and by direct visualization with electron microscopy.

Duplication and functional divergence in the chalcone synthase gene family of Asteraceae: evolution with substrate change and catalytic simplification.

Plant-specific polyketide synthase genes constitute a gene superfamily, including universal chalcone synthase [CHS; malonyl-CoA:4-coumaroyl-CoA malonyltransferase (cyclizing) (EC 2.3.1.74)] genes, sporadically distributed stilbene synthase (SS) genes, and atypical, as-yet-uncharacterized CHS-like genes. We have recently isolated from Gerbera hybrida (Asteraceae) an unusual CHS-like gene, GCHS2, which codes for an enzyme with structural and enzymatic properties as well as ontogenetic distribution distinct from both CHS and SS. Here, we show that the GCHS2-like function is encoded in the Gerbera genome by a family of at least three transcriptionally active genes. Conservation within the GCHS2 family was exploited with selective PCR to study the occurrence of GCHS2-like genes in other Asteraceae. Parsimony analysis of the amplified sequences together with CHS-like genes isolated from other taxa of angiosperm subclass Asteridae suggests that GCHS2 has evolved from CHS via a gene duplication event that occurred before the diversification of the Asteraceae. Enzyme activity analysis of proteins produced in vitro indicates that the GCHS2 reaction is a non-SS variant of the CHS reaction, with both different substrate specificity (to benzoyl-CoA) and a truncated catalytic profile. Together with the recent results of Durbin et al. [Durbin, M. L., Learn, G. H., Jr., Huttley, G. A. & Clegg, M. T. (1995) Proc. Natl. Acad. Sci. USA 92, 3338-3342], our study confirms a gene duplication-based model that explains how various related functions have arisen from CHS during plant evolution.

Evaluating approximations generated by the GNG3D method for mesh simplification

In this paper we present different error measurements with the aim to evaluate the quality of the approximations generated by the GNG3D method for mesh simplification. The first phase of this method consists on the execution of the GNG3D algorithm, described in the paper. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The implementation of three error functions, named Eavg, Emax, Esur, permitts us to control the error of the simplified model, as it is shown in the examples studied.

Computational cost of GNG3D algorithm for mesh simplification

In this paper we present a study of the computational cost of the GNG3D algorithm for mesh optimization. This algorithm has been implemented taking as a basis a new method which is based on neural networks and consists on two differentiated phases: an optimization phase and a reconstruction phase. The optimization phase is developed applying an optimization algorithm based on the Growing Neural Gas model, which constitutes an unsupervised incremental clustering algorithm. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The computational cost of both phases is calculated, showing some examples.