Processamento e análise de vídeos utilizando Floresta de Caminhos Ótimos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Processamento e análise de vídeos utilizando Floresta de Caminhos Ótimos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

New species of Moenkhausia Eigenmann (Characiformes: Characidae) from Rio Xingu and Rio Tapajos basins, Brazil, with comments on a putative case of polymorphic Batesian mimicry

A new species of Moenkhausia is described from Rio Xingu and Rio Tapajos basins, Brazil. The new species is distinguished from its congeners, except from Moenkhausia moisae, by having more scales in the lateral series, 43-47 (v. 23-41 in the remaining congeners). The new species is distinguished from M. moisae by its colour pattern, which consists of a dark midlateral stripe, and an asymmetrical caudal blotch (inconspicuous or faded in specimens from the Rio Arinos) continuous with the midlateral stripe (v. narrow dark midlateral line and conspicuous, regularly rounded and symmetrical blotch not continuous with the midlateral line). The new species is putatively assumed to be mimetic to Jupiaba apenima, in the Rio Xingu and Rio Teles Pires drainages, and to Jupiaba yarina in the Rio Arinos. The two species of Jupiaba are sympatric and remarkably similar in size, general external morphology and colouration to the new species. A small difference occurs in the colouration between the two species of Jupiaba and is also observed in the two respectively sympatric morphotypes of the new species of Moenkhausia. The occurrence of polymorphic Batesian mimicry is therefore discussed for neotropical freshwater fishes. (C) 2009 The Authors Journal compilation (C) 2009 The Fisheries Society of the British Isles

Color texture analysis based on fractal descriptors

Color texture classification is an important step in image segmentation and recognition. The color information is especially important in textures of natural scenes, such as leaves surfaces, terrains models, etc. In this paper, we propose a novel approach based on the fractal dimension for color texture analysis. The proposed approach investigates the complexity in R, G and B color channels to characterize a texture sample. We also propose to study all channels in combination, taking into consideration the correlations between them. Both these approaches use the volumetric version of the Bouligand-Minkowski Fractal Dimension method. The results show a advantage of the proposed method over other color texture analysis methods. (C) 2011 Elsevier Ltd. All rights reserved.

Molecular barcode and morphological analyses reveal the taxonomic and biogeographical status of the striped-legged hermit crab species Clibanarius sclopetarius (Herbst, 1796) and Clibanarius vittatus (Bosc, 1802) (Decapoda: Diogenidae)

The taxonomic status of the species Clibanarius sclopetarius (Herbst, 1796) and Clibanarius vittatus (Bosc, 1802), which have sympatric biogeographical distributions restricted to the western Atlantic Ocean, is based only on differences in the colour pattern of the walking legs of adults. Their morphological similarity led to the suggestion that they be synonymised. In order to investigate this hypothesis, we included species of Clibanarius Dana, 1892 in a molecular phylogenetic analysis of partial sequences of the mitochondrial 16S rDNA gene and the COI barcode region. In addition, we combined the molecular results with morphological observations obtained from several samples of these two species. The genetic divergences of the 16S rDNA and COI sequences between C. sclopetarius and C. vittatus ranged from 4.5 to 5.9% and 9.4 to 11.9%, which did not justify their synonymisation. Differences in the telson morphology, chela ornamentation, and coloration of the eyestalks and antennal peduncle provided support for the separation of the two species. Another interesting result was a considerable genetic difference found between populations of C. vittatus from Brazil and the Gulf of Mexico, which may indicate the existence of two homonymous species.

Complex networks analysis of language complexity

Methods from statistical physics, such as those involving complex networks, have been increasingly used in the quantitative analysis of linguistic phenomena. In this paper, we represented pieces of text with different levels of simplification in co-occurrence networks and found that topological regularity correlated negatively with textual complexity. Furthermore, in less complex texts the distance between concepts, represented as nodes, tended to decrease. The complex networks metrics were treated with multivariate pattern recognition techniques, which allowed us to distinguish between original texts and their simplified versions. For each original text, two simplified versions were generated manually with increasing number of simplification operations. As expected, distinction was easier for the strongly simplified versions, where the most relevant metrics were node strength, shortest paths and diversity. Also, the discrimination of complex texts was improved with higher hierarchical network metrics, thus pointing to the usefulness of considering wider contexts around the concepts. Though the accuracy rate in the distinction was not as high as in methods using deep linguistic knowledge, the complex network approach is still useful for a rapid screening of texts whenever assessing complexity is essential to guarantee accessibility to readers with limited reading ability. Copyright (c) EPLA, 2012

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

Automatic aspect discrimination in data clustering

The attributes describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that simultaneously performs fuzzy clustering and aspects weighting was proposed in the literature. However, SCAD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to reduce the number of parameters required to be set by the user. In this paper we prove that each step of the resulting algorithm, named ASCAD, globally minimizes its cost-function with respect to the argument being optimized. The asymptotic analysis of ASCAD leads to a time complexity which is the same as that of fuzzy c-means. A hard version of the algorithm and a novel validity criterion that considers aspect weights in order to estimate the number of clusters are also described. The proposed method is assessed over several artificial and real data sets.

Theoretical models for the antitrypanosomal activity of thiosemicarbazone derivatives

Thiosemicarbazones are cruzain inhibitors which have been identified as potential antitrypanosomal agents. In this work, several molecular properties were calculated at the density functional theory (DFT)/B3LYP/6-311G* level for a set of 44 thiosemicarbazones. Unsupervised and supervised pattern recognition techniques (hierarchical cluster analysis, principal component analysis, kth-nearest neighbors, and soft independent modeling by class analogy) were used to obtain structureactivity relationship models, which are able to classify unknown compounds according to their activities. The chemometric analyses performed here revealed that 12 descriptors can be considered responsible for the discrimination between high and low activity compounds. Classification models were validated with an external test set, showing that predictive classifications were achieved with the selected variable set. The results obtained here are in good agreement with previous findings from the literature, suggesting that our models can be useful on further investigations on the molecular determinants for the antichagasic activity. (C) 2012 Wiley Periodicals, Inc.

Quantitative Chemical Profile and Multivariate Statistical Analysis of Alembic Distilled Sugarcane Spirit Fractions

Concentrations of 39 organic compounds were determined in three fractions (head, heart and tail) obtained from the pot still distillation of fermented sugarcane juice. The results were evaluated using analysis of variance (ANOVA), Tukey's test, principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). According to PCA and HCA, the experimental data lead to the formation of three clusters. The head fractions give rise to a more defined group. The heart and tail fractions showed some overlap consistent with its acid composition. The predictive ability of calibration and validation of the model generated by LDA for the three fractions classification were 90.5 and 100%, respectively. This model recognized as the heart twelve of the thirteen commercial cachacas (92.3%) with good sensory characteristics, thus showing potential for guiding the process of cuts.

Toll-like receptor 9 activation: a novel mechanism linking placenta-derived mitochondrial DNA and vascular dysfunction in pre-eclampsia

Emerging evidence suggests that in addition to being the 'power houses' of our cells, mitochondria facilitate effector responses of the immune system. Cell death and injury result in the release of mtDNA (mitochondrial DNA) that acts via TLR9 (Toll-like receptor 9), a pattern recognition receptor of the immune system which detects bacterial and viral DNA but not vertebrate DNA. The ability of mtDNA to activate TLR9 in a similar fashion to bacterial DNA stems from evolutionarily conserved similarities between bacteria and mitochondria. mtDNA may be the trigger of systemic inflammation in pathologies associated with abnormal cell death. PE (pre-eclampsia) is a hypertensive disorder of pregnancy with devastating maternal and fetal consequences. The aetiology of PE is unknown and removal of the placenta is the only effective cure. Placentas from women with PE show exaggerated necrosis of trophoblast cells, and circulating levels of mtDNA are higher in pregnancies with PE. Accordingly, we propose the hypothesis that exaggerated necrosis of trophoblast cells results in the release of mtDNA, which stimulates TLR9 to mount an immune response and to produce systemic maternal inflammation and vascular dysfunction that lead to hypertension and IUGR (intra-uterine growth restriction). The proposed hypothesis implicates mtDNA in the development of PE via activation of the immune system and may have important preventative and therapeutic implications, because circulating mtDNA may be potential markers of early detection of PE, and anti-TLR9 treatments may be promising in the management of the disease.

Multi-element determination in Brazilian honey samples by inductively coupled plasma mass spectrometry and estimation of geographic origin with data mining techniques

Multi-element analysis of honey samples was carried out with the aim of developing a reliable method of tracing the origin of honey. Forty-two chemical elements were determined (Al, Cu, Pb, Zn, Mn, Cd, Tl, Co, Ni, Rb, Ba, Be, Bi, U, V, Fe, Pt, Pd, Te, Hf, Mo, Sn, Sb, P, La, Mg, I, Sm, Tb, Dy, Sd, Th, Pr, Nd, Tm, Yb, Lu, Gd, Ho, Er, Ce, Cr) by inductively coupled plasma mass spectrometry (ICP-MS). Then, three machine learning tools for classification and two for attribute selection were applied in order to prove that it is possible to use data mining tools to find the region where honey originated. Our results clearly demonstrate the potential of Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Random Forest (RF) chemometric tools for honey origin identification. Moreover, the selection tools allowed a reduction from 42 trace element concentrations to only 5. (C) 2012 Elsevier Ltd. All rights reserved.

Trypanosoma cruzi Adjuvants Potentiate T Cell-Mediated Immunity Induced by a NY-ESO-1 Based Antitumor Vaccine

Immunological adjuvants that induce T cell-mediate immunity (TCMI) with the least side effects are needed for the development of human vaccines. Glycoinositolphospholipids (GIPL) and CpGs oligodeoxynucleotides (CpG ODNs) derived from the protozoa parasite Trypanosoma cruzi induce potent pro-inflammatory reaction through activation of Toll-Like Receptor (TLR) 4 and TLR9, respectively. Here, using mouse models, we tested the T. cruzi derived TLR agonists as immunological adjuvants in an antitumor vaccine. For comparison, we used well-established TLR agonists, such as the bacterial derived monophosphoryl lipid A (MPL), lipopeptide (Pam3Cys), and CpG ODN. All tested TLR agonists were comparable to induce antibody responses, whereas significant differences were noticed in their ability to elicit CD4(+) T and CD8(+) T cell responses. In particular, both GIPLs (GTH, and GY) and CpG ODNs (B344, B297 and B128) derived from T. cruzi elicited interferon-gamma (IFN-gamma) production by CD4(+) T cells. On the other hand, the parasite derived CpG ODNs, but not GIPLs, elicited a potent IFN-gamma response by CD8(+) T lymphocytes. The side effects were also evaluated by local pain (hypernociception). The intensity of hypernociception induced by vaccination was alleviated by administration of an analgesic drug without affecting protective immunity. Finally, the level of protective immunity against the NY-ESO-1 expressing melanoma was associated with the magnitude of both CD4+ T and CD8+ T cell responses elicited by a specific immunological adjuvant.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.