Oleoresin glands in copaiba (Copaifera trapezifolia Hayne: Leguminosae), a Brazilian rainforest tree

Although studies have addressed the chemical analysis and the biological activity of oleoresin in species of Copaifera, the cellular mechanisms of oleoresin production, storage, and release have rarely been investigated. This study detailed the distribution, ontogeny, and ultrastructure of secretory cavities and canals distributed in leaf and stem, respectively, of Copaifera trapezifolia, a Brazilian species included in a plant group of great economic interest. Axillary vegetative buds, leaflets, and portions of stem in primary and secondary growth were collected and processed in order to study the anatomy, histolocalization of substances, and ultrastructure. Secretory cavities are observed in the foliar blade and secretory canals in the petiolule and stem. They are made up of a uniseriate epithelium delimiting an isodiametric or elongated lumen. Biseriate epithelium is rarely observed and is a novelty for Leguminosae. Cavities and canals originate from ground meristem cells and the lumen is formed by schizogenesis. The content of the cavities and canals of both stem and leaf is oily and resinous, which suggests that the oleoresin could be extracted from the leaf instead of the stem. Phenolic compounds are also detected in the epithelial cell cytoplasm. Cavities and canals in the beginning of developmental stages have polarized epithelial cells. The cytoplasm is rich in smooth and rough endoplasmic reticula connected to vesicles or plastids. Smooth and rough endoplasmic reticulum and plastids were found to be predominant in the epithelial cells of the secretory cavities and canals of C. trapezifolia. Such features testify the quantities of oleoresin found in the lumen and phenolic compounds in the epithelial cell cytoplasm of these glands. Other studies employing techniques such as correlative light electron microscopy could show the vesicle traffic and the compartmentalization of the produced substances in such glands.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.

Solar-Terrestrial Signal Record in Tree Ring Width Time Series from Brazil

This work investigates the behavior of the sunspot number and Southern Oscillation Index (SOI) signal recorded in the tree ring time series for three different locations in Brazil: Humaita in Amaznia State, Porto Ferreira in So Paulo State, and Passo Fundo in Rio Grande do Sul State, using wavelet and cross-wavelet analysis techniques. The wavelet spectra of tree ring time series showed signs of 11 and 22 years, possibly related to the solar activity, and periods of 2-8 years, possibly related to El Nio events. The cross-wavelet spectra for all tree ring time series from Brazil present a significant response to the 11-year solar cycle in the time interval between 1921 to after 1981. These tree ring time series still have a response to the second harmonic of the solar cycle (5.5 years), but in different time intervals. The cross-wavelet maps also showed that the relationship between the SOI x tree ring time series is more intense, for oscillation in the range of 4-8 years.

Substrate specificity of the Chamaerops excelsa palm tree peroxidase. A steady-state kinetic study

The steady state kinetic mechanism of the H(2)O(2)-supported oxidation of different organic substrates by peroxidase from leaves of Chamaerops excelsa palm trees (CEP) has been investigated. An analysis of the initial rates vs. H(2)O(2) and reducing substrate concentrations is consistent with a substrate-inhibited Ping-Pong Bi Bi reaction mechanism. The phenomenological approach expresses the peroxidase Ping-Pong mechanism in the form of the Michaelis-Menten equation and leads to an interpretation of the effects in terms of the kinetic parameters K(m)(H2O2)center dot K(m)(AH2)center dot k(cat)center dot K(SI)(AH2) and of the microscopic rate constants k(1) and k(3) of the shared three-step catalytic cycle of peroxidases. (C) 2011 Elsevier B.V. All rights reserved.

A new eriophyoid mite genus and species, Gymnaceria cupuassu (Acari: Eriophyidae), described from the cupuacu tree in Brazil

A new eriophyoid mite genus and species, Gymnaceria cupuassu n. sp. et n. gen. (Acari: Eriophyidae: Eriophyinae: Aceriini), is described from young fruits and other plant parts of the cupuacu tree, Theobroma grandiflorum (Willd. Ex Spreng.) K. Schum. (Sterculiaceae), from the State of Bahia, northeastern Brazil. No visible damage symptoms were observed.

Tree species richness, does it play a key role on a forest restoration plantation?

The Brazilian Atlantic forest is considered one of the world's biodiversity conservation hotspot. Today there is less than ten percent remaining. Therefore it is necessary to restore these ecosystems. There are many ways of achieving restoration's main goals, but there is a lack of ecological studies that analyzes tree species richness as a variable. Thus, this study's goal is to investigate if there is a difference between a forest restoration in a gradient of tree species richness that varies from 20, 60 to 120 species, by using the litterfall as an indicator. Every month, for one year the forest litter was collected from litter traps that were previously installed. Results revealed that stands produced litterfall by the increasing gradient of species was of 5,370, 5,909 and 6,432 kg ha(-1) yr(-1). The statistical analyses revealed no significant difference among them. Therefore this six-year-old forest restoration plantation shows no difference on the litter production by the tree species richness.

Yield mapping, soil fertility and tree gaps in an orange orchard

The current high competition on Citrus industry demands from growers new management technologies for superior efficiency and sustainability. In this context, precision agriculture (PA) has developed techniques based on yield mapping and management systems that recognize field spatial variability, which contribute to increase profitability of commercial crops. Because spatial variability is often not perceived the orange orchards are still managed as uniform and adoption of PA technology on citrus farms is low. Thus, the objective of the present study was to characterize the spatial variability of three factors: fruit yield, soil fertility and occurrence of plant gaps caused by either citrus blight or huanglongbing (HLB) in a commercial Valencia orchard in Brotas, São Paulo State, Brazil. Data from volume, geographic coordinates and representative area of the bags used on harvest were recorded to generate yield points that were then interpolated to produce the yield map. Soil chemical characteristics were studied by analyzing samples collected along planting rows and inter-rows in 24 points distributed in the field. A map of density of tree gaps was produced by georeferencing individual gaps and later by counting the number of gaps within 500 m² cells. Data were submitted to statistical and geostatistical analyses. A t test was used to compare means of soil chemical characteristics between sampling regions. High variation on yield and density of tree gaps was observed from the maps. It was also demonstrated overlapping regions of high density of plant absence and low fruit yield. Soil fertility varied depending on the sampling region in the orchard. The spatial variability found on yield, soil fertility and on disease occurrence demonstrated the importance to adopt site specific nutrient management and disease control as tools to guarantee efficiency of fruit production.

Vegetation mapping in an area of Ombrophilous Dense Forest at Parque Estadual da Serra do Mar, São Paulo State, Brazil, and floristic composition of the tree component of some physiognomies*

This study aimed to map phytophysiognomies of an area of Ombrophilous Dense Forest at Parque Estadual da Serra do Mar and characterize their floristic composition. Photointerpretation of aerial photographs in scale of 1:35,000 was realized in association with field work. Thirteen physiognomies were mapped and they were classified as Montane Ombrophilous Dense Forest, Alluvial Ombrophilous Dense Forest or Secondary System. Three physiognomies identified at Casa de Pedra streamlet's basin were studied with more details. Riparian forest (RF), valley forest (VF), and hill forest (HF) presented some floristic distinction, as confirmed by Detrended Correspondence Analysis (DCA) and Indicator Species Analysis (ISA) conducted here. Anthropic or natural disturbances and heterogeneity of environmental conditions may be the causes of physiognomic variation in the vegetation of the region. The results presented here may be useful to decisions related to management and conservation of Núcleo Santa Virgínia forests, in general.

Multi-objective evolutionary algorithm for single and multiple fault service restoration in large-scale distribution systems

Network reconfiguration for service restoration (SR) in distribution systems is a complex optimization problem. For large-scale distribution systems, it is computationally hard to find adequate SR plans in real time since the problem is combinatorial and non-linear, involving several constraints and objectives. Two Multi-Objective Evolutionary Algorithms that use Node-Depth Encoding (NDE) have proved able to efficiently generate adequate SR plans for large distribution systems: (i) one of them is the hybridization of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) with NDE, named NSGA-N; (ii) the other is a Multi-Objective Evolutionary Algorithm based on subpopulation tables that uses NDE, named MEAN. Further challenges are faced now, i.e. the design of SR plans for larger systems as good as those for relatively smaller ones and for multiple faults as good as those for one fault (single fault). In order to tackle both challenges, this paper proposes a method that results from the combination of NSGA-N, MEAN and a new heuristic. Such a heuristic focuses on the application of NDE operators to alarming network zones according to technical constraints. The method generates similar quality SR plans in distribution systems of significantly different sizes (from 3860 to 30,880 buses). Moreover, the number of switching operations required to implement the SR plans generated by the proposed method increases in a moderate way with the number of faults.

3D reconstruction from a vascular tree model

[EN] In this paper, we present a vascular tree model made with synthetic materials and which allows us to obtain images to make a 3D reconstruction.We have used PVC tubes of several diameters and lengths that will let us evaluate the accuracy of our 3D reconstruction. In order to calibrate the camera we have used a corner detector. Also we have used Optical Flow techniques to follow the points through the images going and going back. We describe two general techniques to extract a sequence of corresponding points from multiple views of an object. The resulting sequence of points will be used later to reconstruct a set of 3D points representing the object surfaces on the scene. We have made the 3D reconstruction choosing by chance a couple of images and we have calculated the projection error. After several repetitions, we have found the best 3D location for the point.

Fault detection in rotating machines by vibration signal processing techniques

Machines with moving parts give rise to vibrations and consequently noise. The setting up and the status of each machine yield to a peculiar vibration signature. Therefore, a change in the vibration signature, due to a change in the machine state, can be used to detect incipient defects before they become critical. This is the goal of condition monitoring, in which the informations obtained from a machine signature are used in order to detect faults at an early stage. There are a large number of signal processing techniques that can be used in order to extract interesting information from a measured vibration signal. This study seeks to detect rotating machine defects using a range of techniques including synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet transform, Wigner-Ville distribution and spectral correlation density function. The detection and the diagnostic capability of these techniques are discussed and compared on the basis of experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity to fault severity is assessed by the application of these signal processing techniques to gear tooth faults of different sizes.

Kernel Methods for Tree Structured Data

Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of objects of a domain, the kernel function. Designing fast and good kernel functions is a challenging problem. In the case of tree structured data two issues become relevant: kernel for trees should not be sparse and should be fast to compute. The sparsity problem arises when, given a dataset and a kernel function, most structures of the dataset are completely dissimilar to one another. In those cases the classifier has too few information for making correct predictions on unseen data. In fact, it tends to produce a discriminating function behaving as the nearest neighbour rule. Sparsity is likely to arise for some standard tree kernel functions, such as the subtree and subset tree kernel, when they are applied to datasets with node labels belonging to a large domain. A second drawback of using tree kernels is the time complexity required both in learning and classification phases. Such a complexity can sometimes prevents the kernel application in scenarios involving large amount of data. This thesis proposes three contributions for resolving the above issues of kernel for trees. A first contribution aims at creating kernel functions which adapt to the statistical properties of the dataset, thus reducing its sparsity with respect to traditional tree kernel functions. Specifically, we propose to encode the input trees by an algorithm able to project the data onto a lower dimensional space with the property that similar structures are mapped similarly. By building kernel functions on the lower dimensional representation, we are able to perform inexact matchings between different inputs in the original space. A second contribution is the proposal of a novel kernel function based on the convolution kernel framework. Convolution kernel measures the similarity of two objects in terms of the similarities of their subparts. Most convolution kernels are based on counting the number of shared substructures, partially discarding information about their position in the original structure. The kernel function we propose is, instead, especially focused on this aspect. A third contribution is devoted at reducing the computational burden related to the calculation of a kernel function between a tree and a forest of trees, which is a typical operation in the classification phase and, for some algorithms, also in the learning phase. We propose a general methodology applicable to convolution kernels. Moreover, we show an instantiation of our technique when kernels such as the subtree and subset tree kernels are employed. In those cases, Direct Acyclic Graphs can be used to compactly represent shared substructures in different trees, thus reducing the computational burden and storage requirements.