Linear-wavelet networks

This paper proposes a nonlinear regression structure comprising a wavelet network and a linear term. The introduction of the linear term is aimed at providing a more parsimonious interpolation in high-dimensional spaces when the modelling samples are sparse. A constructive procedure for building such structures, termed linear-wavelet networks, is described. For illustration, the proposed procedure is employed in the framework of dynamic system identification. In an example involving a simulated fermentation process, it is shown that a linear-wavelet network yields a smaller approximation error when compared with a wavelet network with the same number of regressors. The proposed technique is also applied to the identification of a pressure plant from experimental data. In this case, the results show that the introduction of wavelets considerably improves the prediction ability of a linear model. Standard errors on the estimated model coefficients are also calculated to assess the numerical conditioning of the identification process.

Metabifurcation analysis of a mean field model of the cortex

Mean field models (MFMs) of cortical tissue incorporate salient, average features of neural masses in order to model activity at the population level, thereby linking microscopic physiology to macroscopic observations, e.g., with the electroencephalogram (EEG). One of the common aspects of MFM descriptions is the presence of a high-dimensional parameter space capturing neurobiological attributes deemed relevant to the brain dynamics of interest. We study the physiological parameter space of a MFM of electrocortical activity and discover robust correlations between physiological attributes of the model cortex and its dynamical features. These correlations are revealed by the study of bifurcation plots, which show that the model responses to changes in inhibition belong to two archetypal categories or “families”. After investigating and characterizing them in depth, we discuss their essential differences in terms of four important aspects: power responses with respect to the modeled action of anesthetics, reaction to exogenous stimuli such as thalamic input, and distributions of model parameters and oscillatory repertoires when inhibition is enhanced. Furthermore, while the complexity of sustained periodic orbits differs significantly between families, we are able to show how metamorphoses between the families can be brought about by exogenous stimuli. We here unveil links between measurable physiological attributes of the brain and dynamical patterns that are not accessible by linear methods. They instead emerge when the nonlinear structure of parameter space is partitioned according to bifurcation responses. We call this general method “metabifurcation analysis”. The partitioning cannot be achieved by the investigation of only a small number of parameter sets and is instead the result of an automated bifurcation analysis of a representative sample of 73,454 physiologically admissible parameter sets. Our approach generalizes straightforwardly and is well suited to probing the dynamics of other models with large and complex parameter spaces.

Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

Na5(Ga4S)(GaS4)3·6H2O: a three-dimensional thiogallate containing a novel octahedral building block

The new thiogallate Na5(Ga4S)(GaS4)3·6H2O has been prepared solvothermally, using 3,5-dimethyl pyridine as a solvent, and characterised by powder and single crystal X-ray diffraction. This material, which exhibits a three-dimensional crystal structure, crystallises in the cubic space group View the MathML sourceF4¯3c (a = 17.557(4) Å). The crystal structure contains octahedral building blocks [Ga4S (GaS4)6]20−, linked into a three-dimensional network with a perovskite-type topology, and sodium hydrate clusters, [Na5(H2O)6]5+, filling the cavities in the [Ga4S(GaS4)6/2]5− framework. UV–Vis diffuse reflectance measurements indicate that this material is a wide band gap semiconductor, with a band gap of ca. 4.4 eV.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.

HiPP: A Novel Hierarchical Point Placement Strategy and its Application to the Exploration of Document Collections

Point placement strategies aim at mapping data points represented in higher dimensions to bi-dimensional spaces and are frequently used to visualize relationships amongst data instances. They have been valuable tools for analysis and exploration of data sets of various kinds. Many conventional techniques, however, do not behave well when the number of dimensions is high, such as in the case of documents collections. Later approaches handle that shortcoming, but may cause too much clutter to allow flexible exploration to take place. In this work we present a novel hierarchical point placement technique that is capable of dealing with these problems. While good grouping and separation of data with high similarity is maintained without increasing computation cost, its hierarchical structure lends itself both to exploration in various levels of detail and to handling data in subsets, improving analysis capability and also allowing manipulation of larger data sets.

Quantitative structure-activity relationships for a series of inhibitors of cruzain from Trypanosoma cruzi: Molecular modeling, CoMFA and CoMSIA studies

Human parasitic diseases are the foremost threat to human health and welfare around the world. Trypanosomiasis is a very serious infectious disease against which the currently available drugs are limited and not effective. Therefore, there is an urgent need for new chemotherapeutic agents. One attractive drug target is the major cysteine protease from Trypanosoma cruzi, cruzain. In the present work, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted on a series of thiosemicarbazone and semicarbazone derivatives as inhibitors of cruzain. Molecular modeling studies were performed in order to identify the preferred binding mode of the inhibitors into the enzyme active site, and to generate structural alignments for the three-dimensional quantitative structure-activity relationship (3D QSAR) investigations. Statistically significant models were obtained (CoMFA. r(2) = 0.96 and q(2) = 0.78; CoMSIA, r(2) = 0.91 and q(2) = 0.73), indicating their predictive ability for untested compounds. The models were externally validated employing a test set, and the predicted values were in good agreement with the experimental results. The final QSAR models and the information gathered from the 3D CoMFA and CoMSIA contour maps provided important insights into the chemical and structural basis involved in the molecular recognition process of this family of cruzain inhibitors, and should be useful for the design of new structurally related analogs with improved potency. (C) 2009 Elsevier Inc. All rights reserved.

Classical and hologram QSAR studies on a series of inhibitors of trypanosomatid Glyceraldehyde-3-Phosphate Dehydrogenase

Leishmaniasis and trypanosomiasis are major causes of morbidity and mortality in both tropical and subtropical regions of the world. The current available drugs are limited, ineffective, and require long treatment regimens. Due to the high dependence of trypanosomatids on glycolysis as a source of energy, some glycolytic enzymes have been identified as attractive targets for drug design. In the present work, classical Two-Dimensional Quantitative Structure -Activity Relationships (2D QSAR) and Hologram QSAR (HQSAR) studies were performed on a series of adenosine derivatives as inhibitors of Leishmania mexicana Glyceraldehyde-3-Phosphate Dehydrogenase (LmGAPDH). Significant correlation coefficients (classical QSAR, r(2)=0.83 and q(2) =0.81; HQSAR, r(2)=0.91 and q(2) =0.86) were obtained for the 56 training set compounds, indicating the potential of the models for untested compounds. The models were then externally validated using a test set of 14 structurally related compounds and the predicted values were in good agreement with the experimental results (classical QSAR, r(pred)(2) = 0.94; HQSAR, r(pred)(2) = 0.92).

Classical and fragment-based hologram structure-activity relationships for a series of analgesic cyclic imides

Cyclic imides have been widely employed in drug design research due to their multiple pharmacological and biological properties. In the present study, two-dimensional quantitative structure-activity relationship (2D QSAR) studies were conducted on a series of potent analgesic cyclic imides using both classical and hologram QSAR (HQSAR) methods, yielding significant statistical models (classical QSAR, q(2) = 0.80; HQSAR, q(2) = 0.84). The models were then used to evaluate an external data test, and the predicted values were in good agreement with the experimental results, indicating their consistency for untested compounds.

Structure-activity relationships for a class of selective inhibitors of the major cysteine protease from Trypanosoma cruzi

Chagas` disease is a parasitic infection widely distributed throughout Latin America, with devastating consequences in terms of human morbidity and mortality. Cruzain, the major cysteine protease from Trypanosoma cruzi, is an attractive target for antitrypanosomal chemotherapy. In the present work, classical two-dimensional quantitative structure-activity relationships (2D QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 45 thiosemicarbazone and semicarbazone derivatives as inhibitors of T. cruzi cruzain. Significant statistical models (HQSAR, q2=0.75 and r2=0.96; classical QSAR, q2=0.72 and r2=0.83) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 10 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, [image omitted]=0.95; classical QSAR, [image omitted]=0.91), indicating the existence of complementary between the two ligand-based drug design techniques.

Novas fases amorfas de carbono produzidas por irradiação iônica de filmes de C/sub 60/, [alfa]-C e [alfa]-C:H

Neste trabalho estuda-se a formação de novas fases de carbono amorfo através da irradiação iônica de filmes de fulereno, a-C e a-C:H polimérico. Os efeitos da irradiação iônica na modificação das propriedades ópticas e mecânicas dos filmes de carbono irradiados são analisados de forma correlacionada com as alterações estruturais a nivel atômico. O estudo envolve tanto a análise dos danos induzidos no fulereno pela irradiação iônica a baixas fluências, correspondendo a baixas densidades de energia depositada, quanto a investigação das propriedades físico-químicas das fases amorfas obtidas após irradiações dos filmes de fulereno, a-C e a-C:H com altas densidades de energia depositada. As propriedades ópticas, mecânicas e estruturais das amostras são analisadas através de técnicas de espectroscopia Raman e infravermelho, espectrofotometria UV-VIS-NIR, microscopias ópticas e de força atômica, nanoindentação e técnicas de análise por feixe de íons, tais como retroespalhamento Rutherford e análises por reação nuclear. As irradiações produzem profundas modificações nas amostras de fulereno, a-C e a-C:H, e por conseqüência significativas alterações em suas propriedades ópticas e mecânicas. Após máximas fluências de irradiação fases amorfas rígidas (com dureza de 14 e 17 GPa) e com baixos gaps ópticos (0,2 e 0,5 eV) são formadas. Estas estruturas não usuais correspondem a arranjos atômicos com 90 a 100% de estados sp2. Em geral fases sp2 são planares e apresentam baixa dureza, como predito pelo modelo de “cluster”. Entretanto, os resultados experimentais mostram que as propriedades elásticas das novas fases formadas são alcançadas através da criação de uma estrutura sp2 tridimensional. A indução de altas distorções angulares, através da irradiação iônica, possibilita a formação de anéis de carbono não hexagonais, tais como pentágonos e heptágonos, permitindo assim a curvatura da estrutura. Utilizando um modelo de contagem de vínculos é feita uma análise comparativa entre a topologia (estrutura geométrica) de ligações C-sp2 e as propriedades nanomecânicas. São comparados os efeitos de estruturas sp2 planares e tridimensionais (aleatórias) no processo de contagem de vínculos e, conseqüentemente, nas propriedades elásticas de cada sistema. Os resultados mostram que as boas propriedades mecânicas das novas fases de carbono formadas seguem as predições do modelo de vínculos para uma rede atômica sp2 tridimensional. A formação de uma fase amorfa dura e 100% sp2 representa uma importante conquista na procura de novas estruturas rígidas de carbono. A síntese da estrutura desordenada sp2 tridimensional e vinculada aqui apresentada é bastante incomum na literatura. O presente trabalho mostra que o processo de não-equilíbrio de deposição de energia durante a irradiação iônica permite a formação de distorções angulares nas ligações sp2-C, possibilitando a criação de estruturas grafíticas tridimensionais.

Protein Hydrolysis by Immobilized and Stabilized Trypsin

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic variability of porcine parvovirus isolates revealed by analysis of partial sequences of the structural coding gene VP2

The 3'-terminal 853 nt (and the putative 283 aa) sequence of the VP2-encoding gene from 29 field strains of porcine parvovirus (PPV) were determined and compared both to each other and with other published sequences. Sequences were examined using maximum-parsimony and statistical analyses for nucleotide diversity and sequence variability. Among the nucleotide sequences of the PPV field strains, 26 polymorphic sites were encountered; 22 polymorphic sites were detected in the putative amino acid sequence. Mapping polymorphic sites of protein data onto the three-dimensional (3D) structure of PPV VP2 revealed that almost all substitutions were located on the external surface of the viral capsid. Mapping amino acid substitutions to the alignment between PPV VP2 sequences and the 3D structure of canine parvovirus (CPV) capsid, many PPV substitutions were observed to map to regions of recognized antigenicity and/or to contain phenotypically important residues for CPV and other parvoviruses. In spite of the high sequence similarity, genetic analysis has shown the existence of at least two virus lineages among the samples. In conclusion, these results highlight the need for close surveillance on PPV genetic drift, with an assessment of its potential ability to modify the antigenic make-up of the virus.

Structure of human uropepsin at 2.45 angstrom resolution

The molecular structure of human uropepsin, an aspartic proteinase from the urine produced in the form of pepsinogen A in the gastric mucosa, has been determined by molecular replacement using human pepsin as the search model. Crystals belong to space group P2(1)2(1)2(1), with unit-cell parameters a = 50.99, b = 75.56, c = 89.90 Angstrom. Crystallographic refinement led to an R factor of 0.161 at 2.45 Angstrom resolution. The positions of 2437 non-H protein atoms in 326 residues have been determined and the model contains 143 water molecules. The structure is bilobal, consisting of two predominantly beta -sheet lobes which, as observed in other aspartic proteinases, are related by a pseudo-twofold axis. A model of the uropepsin-pepstatin complex has been constructed based on the high-resolution crystal structure of pepsin complexed with pepstatin.

Blue cooperative luminescence properties in Yb3+ doped GeO2-PbO-Bi2O3 vitreous system for the production of thin films

Germanate glasses are of interest for optoelectronic applications because they combine high mechanical strength, high chemical durability and temperature stability with a large transmission window (400 to 4500 nm) and high refractive index (2.0). GeO2-PbO-Bi2O3 glasses doped with Y-b(3+) were fabricated by melting powders in a crucible and then pouring them in a brass mold. Energy Dispersive Spectroscopy showed that the glass composition has a high spatial uniformity and that the Yb concentration in the solid sample is proportional to the Yb concentration in the melt, what was confirmed by absorption measurements. Intense blue emission at 507 nm was observed, corresponding to half of the wavelength of the near infrared region (NIR) emission; besides, a decay lifetime of 0.25 ms was measured and this corresponds to half of the decay lifetime in the infrared region; these are very strong indications of the presence of blue cooperative luminescence. Larger targets have been produced to be sputtered, resulting in thin films for three dimensional (3D) display and waveguide applications. (c) 2006 Elsevier B.V. All rights reserved.