Characterizing chaotic melodies in automatic music composition

In this paper, we initially present an algorithm for automatic composition of melodies using chaotic dynamical systems. Afterward, we characterize chaotic music in a comprehensive way as comprising three perspectives: musical discrimination, dynamical influence on musical features, and musical perception. With respect to the first perspective, the coherence between generated chaotic melodies (continuous as well as discrete chaotic melodies) and a set of classical reference melodies is characterized by statistical descriptors and melodic measures. The significant differences among the three types of melodies are determined by discriminant analysis. Regarding the second perspective, the influence of dynamical features of chaotic attractors, e.g., Lyapunov exponent, Hurst coefficient, and correlation dimension, on melodic features is determined by canonical correlation analysis. The last perspective is related to perception of originality, complexity, and degree of melodiousness (Euler's gradus suavitatis) of chaotic and classical melodies by nonparametric statistical tests. (c) 2010 American Institute of Physics. [doi: 10.1063/1.3487516]

Raspberry (Rubus idaeus L.) wine: Yeast selection, sensory evaluation and instrumental analysis of volatile and other compounds

To evaluate the potential for fermentation of raspberry pulp, sixteen yeast strains (S. cerevisiae and S. bayanus) were studied. Volatile compounds were determined by GC-MS, GC-FID, and GC-PFPD. Ethanol. glycerol and organic acids were determined by HPLC. HPLC-DAD was used to analyse phenolic acids. Sensory analysis was performed by trained panellists. After a screening step, CAT-1, UFLA FW 15 and S. bayanus CBS 1505 were previously selected based on their fermentative characteristics and profile of the metabolites identified. The beverage produced with CAT-1 showed the highest volatile fatty acid concentration (1542.6 mu g/L), whereas the beverage produced with UFLA FIN 15 showed the highest concentration of acetates (2211.1 mu g/L) and total volatile compounds (5835 mu g/L). For volatile sulphur compounds. 566.5 mu g/L were found in the beverage produced with S. bayanus CBS 1505. The lowest concentration of volatile sulphur compounds (151.9 mu g/L) was found for the beverage produced with UFLA FW 15. In the sensory analysis, the beverage produced with UFLA FW 15 was characterised by the descriptors raspberry, cherry, sweet, strawberry, floral and violet. In conclusion, strain UFLA FW 15 was the yeast that produced a raspberry wine with a good chemical and sensory quality. (C) 2010 Elsevier Ltd. All rights reserved.

Molecular modeling and QSAR studies of a set of indole and benzimidazole derivatives as H(4) receptor antagonists

Histamine is an important biogenic amine, which acts with a group of four G-protein coupled receptors (GPCRs), namely H(1) to H(4) (H(1)R - H(4)R) receptors. The actions of histamine at H(4)R are related to immunological and inflammatory processes, particularly in pathophysiology of asthma, and H(4)R ligands having antagonistic properties could be helpful as antiinflammatory agents. In this work, molecular modeling and QSAR studies of a set of 30 compounds, indole and benzimidazole derivatives, as H(4)R antagonists were performed. The QSAR models were built and optimized using a genetic algorithm function and partial least squares regression (WOLF 5.5 program). The best QSAR model constructed with training set (N = 25) presented the following statistical measures: r (2) = 0.76, q (2) = 0.62, LOF = 0.15, and LSE = 0.07, and was validated using the LNO and y-randomization techniques. Four of five compounds of test set were well predicted by the selected QSAR model, which presented an external prediction power of 80%. These findings can be quite useful to aid the designing of new anti-H(4) compounds with improved biological response.

Generation and Analysis of Interaction Energy Maps of p-Substituted Benzoic Acid [(5-Nitro-thiophen-2-yl)-methylene]-Hydrazides Active against MRSA

In this preliminary study eighteen p-substituted benzoic acid [(5-nitro-thiophen-2-yl)-methylene]-hydrazides with antimicrobial activity were evaluated against multidrug-resistant Staphylococcus aureus, correlating the three-dimensional characteristics of the ligands with their respective bioactivities. The computer programs Sybyl and CORINA were used, respectively, for the design and three-dimensional conversion of the ligands. Molecular interaction fields were calculated using GRID program. Calculations using Volsurf resulted in a statistically consistent model with 48 structural descriptors showing that hydrophobicity is a fundamental property in the analyzed biological response.

Topliss method in the optimization of salicylic acid derivatives as potential antimycobacterial agents

The Topliss method was used to guide a synthetic path in support of drug discovery efforts toward the identification of potent antimycobacterial agents. Salicylic acid and its derivatives, p-chloro, p-methoxy, and m-chlorosalicylic acid, exemplify a series of synthetic compounds whose minimum inhibitory concentrations for a strain of Mycobacterium were determined and compared to those of the reference drug, p-aminosalicylic acid. Several physicochemical descriptors (including Hammett`s sigma constant, ionization constant, dipole moment, Hansch constant, calculated partition coefficient, Sterimol-L and -B-4 and molecular volume) were considered to elucidate structure-activity relationships. Molecular electrostatic potential and molecular dipole moment maps were also calculated using the AM1 semi-empirical method. Among the new derivatives, m-chlorosalicylic acid showed the lowest minimum inhibitory concentration. The overall results suggest that both physicochemical properties and electronic features may influence the biological activity of this series of antimycobacterial agents and thus should be considered in designing new p-aminosalicylic acid analogs.

Inhibition of immune complex-mediated neutrophil oxidative metabolism: A pharmacophore model for 3-phenylcoumarin derivatives using GRIND-based 3D-QSAR and 2D-QSAR procedures

In this study, twenty hydroxylated and acetoxylated 3-phenylcoumarin derivatives were evaluated as inhibitors of immune complex-stimulated neutrophil oxidative metabolism and possible modulators of the inflammatory tissue damage found in type III hypersensitivity reactions. By using lucigenin- and luminol-enhanced chemiluminescence assays (CL-luc and CL-lum, respectively), we found that the 6,7-dihydroxylated and 6,7-diacetoxylated 3-phenylcoumarin derivatives were the most effective inhibitors. Different structural features of the other compounds determined CL-luc and/or CL-lum inhibition. The 2D-QSAR analysis suggested the importance of hydrophobic contributions to explain these effects. In addition, a statistically significant 3D-QSAR model built applying GRIND descriptors allowed us to propose a virtual receptor site considering pharmacophoric regions and mutual distances. Furthermore, the 3-phenylcoumarins studied were not toxic to neutrophils under the assessed conditions. (C) 2007 Elsevier Masson SAS. All rights reserved.

A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.

Forest structure in a mosaic of rainforest sites: The effect of fragmentation and recovery after clear cut

A variety of human-induced disturbances such as forest fragmentation and recovery after deforestation for pasture or agricultural activities have resulted in a complex landscape mosaic in the Una region of northeastern Brazil. Using a set of vegetation descriptors, we investigated the main structural changes observed in forest categories that comprise the major components of the regional landscape and searched for potential key descriptors that could be used to discriminate among different forest categories. We assessed the forest structure of five habitat categories defined as (I) interiors and (2) edges of large fragments of old-growth forest (>1000 ha), (3) interiors and (4) edges of small forest fragments (<100 ha), and (5) early secondary forests. Forest descriptors used here were: frequency of herbaceous lianas and woody climbers, number of standing dead trees, number of fallen trunks, litter depth, number of pioneer plants (early secondary and shade-intolerant species), vertical foliage stratification profile and distribution Of trees in different diameter classes. Edges and interiors of forest fragments were significantly different only in the number of standing dead trees. Secondary forests and edges of fragments showed differences in litter depth, fallen trunks and number of pioneer trees, and secondary forests were significantly different from fragment interiors in the number of standing dead trees and the number of pioneer trees. Horizontal and vertical structure evaluated via ordination analysis showed that fragment interiors, compared to secondary forests, were characterized by a greater number of medium (25-35 cm) and large (35-50 cm) trees and smaller numbers of thin trees (5-10 cm). There was great heterogeneity at the edges of small and large fragments, as these sites were distributed along almost the entire gradient. Most interiors of large and small fragments presented higher values of foliage densities at higher strata ( 15-20 m and at 20-25 m height), and lower densities at 1-5 m. All secondary forests and some fragment edge sites showed an opposite tendency. A discriminant function highlighted differences among forest categories, with transects of large fragment interiors and secondary forests representing two extremes along a disturbance gradient determined by foliage structure (densities at 15-20 m and 20-25 m), with the edges of both large and small fragments and the interiors of small fragments scattered across the gradient. The major underlying processes determining patterns of forest disturbance in the study region are discussed, highlighting the importance of forest fragments, independently of its size, as forests recovery after clear cut show a greatly distinct structure, with profound implications on fauna movements. (C) 2009 Elsevier BY. All rights reserved.

Passive Restoration in Biodiversity Hotspots: Consequences for an Atlantic Rainforest Lizard Taxocene

Long-term conservation in biodiversity hotspots depends on the recovery of communities in secondary forest fragments. In most cases, however, recovery strategies for these areas are based only on passive restoration. It is therefore necessary to determine the efficiency of such strategies. In this study, we assess the efficiency of passive restoration on a 567-ha 28-yr-old fragment of Atlantic Rainforest in Northeastern Brazil. We measured richness, composition, abundance and biomass of a lizard taxocene and also vegetation structure and availability of several microhabitat descriptors in 18 plots of this secondary forest. We then compared them with measures in 29 plots from two neighboring reference sites. Species richness, abundance, biomass and microhabitat descriptors availability inside the secondary fragment did not differ from reference sites. However, composition and vegetation structure showed small differences. Some forest specialist lizards, which should be a focus of conservation efforts in fragmented landscapes of the Atlantic Rainforest, were not found in the fragment and data indicate that this was not due to sampling or a lack of suitable habitat or microhabitat. In the presence of preserved source sites, passive restoration may be a cheap and effective way to recover lizard taxocenes of the Atlantic Rainforest. Some of the species may need to be re-introduced to accelerate the full recovery of original composition of lizard taxocenes in secondary Atlantic Rainforests.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

Texture analysis using graphs generated by deterministic partially self-avoiding walks

Texture is one of the most important visual attributes for image analysis. It has been widely used in image analysis and pattern recognition. A partially self-avoiding deterministic walk has recently been proposed as an approach for texture analysis with promising results. This approach uses walkers (called tourists) to exploit the gray scale image contexts in several levels. Here, we present an approach to generate graphs out of the trajectories produced by the tourist walks. The generated graphs embody important characteristics related to tourist transitivity in the image. Computed from these graphs, the statistical position (degree mean) and dispersion (entropy of two vertices with the same degree) measures are used as texture descriptors. A comparison with traditional texture analysis methods is performed to illustrate the high performance of this novel approach. (C) 2011 Elsevier Ltd. All rights reserved.

Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.

A complex network-based approach for boundary shape analysis

This paper introduces a novel methodology to shape boundary characterization, where a shape is modeled into a small-world complex network. It uses degree and joint degree measurements in a dynamic evolution network to compose a set of shape descriptors. The proposed shape characterization method has all efficient power of shape characterization, it is robust, noise tolerant, scale invariant and rotation invariant. A leaf plant classification experiment is presented on three image databases in order to evaluate the method and compare it with other descriptors in the literature (Fourier descriptors, Curvature, Zernike moments and multiscale fractal dimension). (C) 2008 Elsevier Ltd. All rights reserved.

Plant Leaf Identification Using Gabor Wavelets

This article presents a novel method of plant classification using Gabor wavelet filters to extract texture filters in a foliar surface. The aim of this promising method is to add to the results obtained by other leaf attributes (such as shape, contour, color, among others), increasing, therefore, the percentage of classification of plant species. To corroborate the efficiency of the technique, an experiment using 20 species from Brazilian flora was done and discussed. The results are also compared with texture Fourier descriptors and cooccurrence matrices. (C) 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 236-243, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20201

Fragment-based and classical quantitative structure-activity relationships for a series of hydrazides as antituberculosis agents

Worldwide, tuberculosis (TB) is the leading cause of death among curable infectious diseases. Multidrug-resistant Mycobacterium tuberculosis is an emerging problem of great importance to public health, and there is an urgent need for new anti-TB drugs. In the present work, classical 2D quantitative structure-activity relationships (QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 91 isoniazid derivatives. Significant statistical models (classical QSAR, q(2) = 0.68 and r(2) = 0.72; HQSAR, q(2) = 0.63 and r(2) = 0.86) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 24 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, r(pred)(2) = 0.87; classical QSAR, r(pred)(2) = 0.75).