High-precision measurements of the co-polar correlation coefficient: non-Gaussian errors and retrieval of the dispersion parameter µ in rainfall

The co-polar correlation coefficient (ρhv) has many applications, including hydrometeor classification, ground clutter and melting layer identification, interpretation of ice microphysics and the retrieval of rain drop size distributions (DSDs). However, we currently lack the quantitative error estimates that are necessary if these applications are to be fully exploited. Previous error estimates of ρhv rely on knowledge of the unknown "true" ρhv and implicitly assume a Gaussian probability distribution function of ρhv samples. We show that frequency distributions of ρhv estimates are in fact highly negatively skewed. A new variable: L = -log10(1 - ρhv) is defined, which does have Gaussian error statistics, and a standard deviation depending only on the number of independent radar pulses. This is verified using observations of spherical drizzle drops, allowing, for the first time, the construction of rigorous confidence intervals in estimates of ρhv. In addition, we demonstrate how the imperfect co-location of the horizontal and vertical polarisation sample volumes may be accounted for. The possibility of using L to estimate the dispersion parameter (µ) in the gamma drop size distribution is investigated. We find that including drop oscillations is essential for this application, otherwise there could be biases in retrieved µ of up to ~8. Preliminary results in rainfall are presented. In a convective rain case study, our estimates show µ to be substantially larger than 0 (an exponential DSD). In this particular rain event, rain rate would be overestimated by up to 50% if a simple exponential DSD is assumed.

Successional stage and geographic features determine floristic similarity among Atlantic Forest remnants, Sao Paulo State, Brazil

Analysis of floristic similarity relationships between plant communities can detect patterns of species occurrence and also explain conditioning factors. Searching for such patterns, floristic similarity relationships among Atlantic Forest sites situated at Ibiuna Plateau, Sao Paulo state, Brazil, were analyzed by multivariate techniques. Twenty one forest fragments and six sites within a continuous Forest Reserve were included in the analyses. Floristic composition and structure of the tree community (minimum dbh 5 cm) were assessed using the point centered quarter method. Two methods were used for multivariate analysis: Detrended Correspondence Analysis (DCA) and Two-Way Indicator Species Analysis (TWINSPAN). Similarity relationships among the study areas were based on the successional stage of the community and also on spatial proximity. The more similar the successional stage of the communities, the higher the floristic similarity between them, especially if the communities are geographically close. A floristic gradient from north to south was observed, suggesting a transition between biomes, since northern indicator species are mostly heliophytes, occurring also in cerrado vegetation and seasonal semideciduous forest, while southern indicator species are mostly typical ombrophilous and climax species from typical dense evergreen Atlantic Forest.

Paged similarity queries

An important feature of a database management systems (DBMS) is its client/server architecture, where managing shared memory among the clients and the server is always an tough issue. However, similarity queries are specially sensitive to this kind of architecture, since the answer sizes vary widely. Usually, the answers of similarity query are fully processed to be sent in full to the user, who often is interested in just parts of the answer, e.g. just few elements closer or farther to the query reference. Compelling the DBMS to retrieve the full answer, further ignoring its majority is at least a waste of server processing power. Paging the answer is a technique that splits the answer onto several pages, following client requests. Despite the success of paging on traditional queries, little work has been done to support it in similarity queries. In this work, we present a technique that not only provides paging in similarity range or k-nearest neighbor queries, but also supports them in two variations: the forward similarity query and the backward similarity query. They return elements either increasingly farther of increasingly closer to the query reference. The reported experiments show that, depending on the proportion of the interesting part over the full answer, both techniques allow answering queries much faster than it is obtained in the non-paged way. (C) 2010 Elsevier Inc. All rights reserved.

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,

Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.

A criterion for unitary similarity of upper triangular matrices in general position

Each square complex matrix is unitarily similar to an upper triangular matrix with diagonal entries in any prescribed order. Let A = [a(ij)] and B = [b(ij)] be upper triangular n x n matrices that are not similar to direct sums of square matrices of smaller sizes, or are in general position and have the same main diagonal. We prove that A and B are unitarily similar if and only if parallel to h(A(k))parallel to = parallel to h(B(k))parallel to for all h is an element of C vertical bar x vertical bar and k = 1, ..., n, where A(k) := [a(ij)](i.j=1)(k) and B(k) := [b(ij)](i.j=1)(k) are the leading principal k x k submatrices of A and B, and parallel to . parallel to is the Frobenius norm. (C) 2011 Elsevier Inc. All rights reserved.

A canonical form for nonderogatory matrices under unitary similarity

A square matrix is nonderogatory if its Jordan blocks have distinct eigenvalues. We give canonical forms for (1) nonderogatory complex matrices up to unitary similarity, and (2) pairs of complex matrices up to similarity, in which one matrix has distinct eigenvalues. The types of these canonical forms are given by undirected and, respectively, directed graphs with no undirected cycles. (C) 2011 Elsevier Inc. All rights reserved.

Integration of Ligand- and Target-Based Virtual Screening for the Discovery of Cruzain Inhibitors

A myriad of methods are available for virtual screening of small organic compound databases. In this study we have successfully applied a quantitative model of consensus measurements, using a combination of 3D similarity searches (ROCS and EON), Hologram Quantitative Structure Activity Relationships (HQSAR) and docking (FRED, FlexX, Glide and AutoDock Vina), to retrieve cruzain inhibitors from collected databases. All methods were assessed individually and then combined in a Ligand-Based Virtual Screening (LBVS) and Target-Based Virtual Screening (TBVS) consensus scoring, using Receiving Operating Characteristic (ROC) curves to evaluate their performance. Three consensus strategies were used: scaled-rank-by-number, rank-by-rank and rank-by-vote, with the most thriving the scaled-rank-by-number strategy, considering that the stiff ROC curve appeared to be satisfactory in every way to indicate a higher enrichment power at early retrieval of active compounds from the database. The ligand-based method provided access to a robust and predictive HQSAR model that was developed to show superior discrimination between active and inactive compounds, which was also better than ROCS and EON procedures. Overall, the integration of fast computational techniques based on ligand and target structures resulted in a more efficient retrieval of cruzain inhibitors with desired pharmacological profiles that may be useful to advance the discovery of new trypanocidal agents.

2D QSAR and similarity studies on cruzain inhibitors aimed at improving selectivity over cathepsin L

Hologram quantitative structure-activity relationships (HQSAR) were applied to a data set of 41 cruzain inhibitors. The best HQSAR model (Q(2) = 0.77; R-2 = 0.90) employing Surflex-Sim, as training and test sets generator, was obtained using atoms, bonds, and connections as fragment distinctions and 4-7 as fragment size. This model was then used to predict the potencies of 12 test set compounds, giving satisfactory predictive R-2 value of 0,88. The contribution maps obtained from the best HQSAR model are in agreement with the biological activities of the study compounds. The Trypanosoma cruzi cruzain shares high similarity with the mammalian homolog cathepsin L. The selectivity toward cruzam was checked by a database of 123 compounds, which corresponds to the 41 cruzain inhibitors used in the HQSAR model development plus 82 cathepsin L inhibitors. We screened these compounds by ROCS (Rapid Overlay of Chemical Structures), a Gaussian-shape volume overlap filter that can rapidly identify shapes that match the query molecule. Remarkably, ROCS was able to rank the first 37 hits as being only cruzain inhibitors. In addition, the area under the curve (AUC) obtained with ROCS was 0.96, indicating that the method was very efficient to distinguishing between cruzain and cathepsin L inhibitors. (c) 2007 Elsevier Ltd. All rights reserved.

Novel Application of 2D and 3D-Similarity Searches To Identify Substrates among Cytochrome P450 2C9, 2D6, and 3A4

Cytochrome P450 (CYP450) is a class of enzymes where the substrate identification is particularly important to know. It would help medicinal chemists to design drugs with lower side effects due to drug-drug interactions and to extensive genetic polymorphism. Herein, we discuss the application of the 2D and 3D-similarity searches in identifying reference Structures with higher capacity to retrieve Substrates of three important CYP enzymes (CYP2C9, CYP2D6, and CYP3A4). On the basis of the complementarities of multiple reference structures selected by different similarity search methods, we proposed the fusion of their individual Tanimoto scores into a consensus Tanimoto score (T(consensus)). Using this new score, true positive rates of 63% (CYP2C9) and 81% (CYP2D6) were achieved with false positive rates of 4% for the CYP2C9-CYP2D6 data Set. Extended similarity searches were carried out oil a validation data set, and the results showed that by using the T(consensus) score, not only the area of a ROC graph increased, but also more substrates were recovered at the beginning of a ranked list.