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.

Direct assembly of a metallodendrimer encompassing seven triruthenium clusters units

A general strategy for the assembly of dendrimeric metallo-cluster species based on tritopic trinuclear ruthenium acetate complexes is demonstrated. First, a central core consisting of a [Ru3O(CH3COO)(6)(TPEB)(3)]PF6 complex (G0), where TPEB is the tripodal 1,3,5-tri-4-pyridyl-1,2-ethenylbenzene ligand, was synthesized and then reacted with the end-capping complex [Ru3O(CH3COO)(6)(py)(2)(MeOH)]PF6, thus composing the first generation shell of a dendrimer encompassing twenty-one ruthenium ions (G1). The core and dendrimeric complexes were characterized by elemental analysis, UV-Vis, H-1 NMR, ESI-MS spectrometry and Differential pulse voltammetry. All results were consistent with the structure of that multinuclear cationic dendrimeric species. The isotopologic profile of daughter fragments and the strength of the metal-ligand bonds were carefully investigated providing the fragmentation pathway for the metallo-dendrimer upon ESI-MS dissociation conditions. (C) 2012 Elsevier B.V. All rights reserved.

Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

The classification of texts has become a major endeavor with so much electronic material available, for it is an essential task in several applications, including search engines and information retrieval. There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies. (c) 2012 Elsevier B.V. All rights reserved.

Identification of literary movements using complex networks to represent texts

The use of statistical methods to analyze large databases of text has been useful in unveiling patterns of human behavior and establishing historical links between cultures and languages. In this study, we identified literary movements by treating books published from 1590 to 1922 as complex networks, whose metrics were analyzed with multivariate techniques to generate six clusters of books. The latter correspond to time periods coinciding with relevant literary movements over the last five centuries. The most important factor contributing to the distinctions between different literary styles was the average shortest path length, in particular the asymmetry of its distribution. Furthermore, over time there has emerged a trend toward larger average shortest path lengths, which is correlated with increased syntactic complexity, and a more uniform use of the words reflected in a smaller power-law coefficient for the distribution of word frequency. Changes in literary style were also found to be driven by opposition to earlier writing styles, as revealed by the analysis performed with geometrical concepts. The approaches adopted here are generic and may be extended to analyze a number of features of languages and cultures.

First-principles studies of complex magnetism in Mn nanostructures on the Fe(001) surface

The magnetic properties of Mn nanostructures on the Fe(001) surface have been studied using the noncollinear first-principles real space-linear muffin-tin orbital-atomic sphere approximation method within density-functional theory. We have considered a variety of nanostructures such as adsorbed wires, pyramids, and flat and intermixed clusters of sizes varying from two to nine atoms. Our calculations of interatomic exchange interactions reveal the long-range nature of exchange interactions between Mn-Mn and Mn-Fe atoms. We have found that the strong dependence of these interactions on the local environment, the magnetic frustration, and the effect of spin-orbit coupling lead to the possibility of realizing complex noncollinear magnetic structures such as helical spin spiral and half-skyrmion.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Adsorption of NO on the Rh-13, Pd-13, Ir-13, and Pt-13 Clusters: A Density Functional Theory Investigation

The adsorption of NO on transition-metal (TM) surfaces has been widely studied by experimental and theoretical techniques; however, our atomistic understanding of the interaction of nitrogen monoxide (NO) with small TM clusters is far from satisfactory, which compromises a deep understanding of real catalyst devices. In this study, we report a density functional theory study of the adsorption properties of NO on the TM13 (TM = Rh, Pd, Ir, Pt) clusters employing the projected augmented wave method. We found that the interaction of NO with TM13 is much more complex than that for NO/TM(111). In particular, for low symmetry TM13 clusters, there is a strong rearrangement of the electronic charge density upon NO adsorption and, as a consequence, the adsorption energy shows a very complex dependence even for adsorption sites with the same local effective coordination. We found a strong enhancement of the binding energy of NO to the TM13 clusters compared with the TM(111) surfaces, as the antibonding NO states are not occupied for NO/TM13, and the general relationship based on the d-band model between adsorption energy and the center of gravity of the occupied d-states does not hold for the studied TM13 clusters, in particular, for clusters with low symmetry. In contrast with the adsorption energy trends, the geometric NO/TM13 parameters and the vibrational N-O frequencies for different coordination sites follow the same trend as for the respective TM(111) surfaces, while the changes in the frequencies between different surfaces and TM13 clusters reflect the strong NO-TM13 interaction.

Collybistin and gephyrin are novel components of the eukaryotic translation initiation factor 3 complex

Abstract Background Collybistin (CB), a neuron-specific guanine nucleotide exchange factor, has been implicated in targeting gephyrin-GABAA receptors clusters to inhibitory postsynaptic sites. However, little is known about additional CB partners and functions. Findings Here, we identified the p40 subunit of the eukaryotic translation initiation factor 3 (eIF3H) as a novel binding partner of CB, documenting the interaction in yeast, non-neuronal cell lines, and the brain. In addition, we demonstrated that gephyrin also interacts with eIF3H in non-neuronal cells and forms a complex with eIF3 in the brain. Conclusions Together, our results suggest, for the first time, that CB and gephyrin associate with the translation initiation machinery, and lend further support to the previous evidence that gephyrin may act as a regulator of synaptic protein synthesis.

Systematics of the Oswaldoi Complex (Anopheles, Nyssorhynchus) in South America

Abstract Background Effective malaria control relies on accurate identification of those Anopheles mosquitoes responsible for the transmission of Plasmodium parasites. Anopheles oswaldoi s.l. has been incriminated as a malaria vector in Colombia and some localities in Brazil, but not ubiquitously throughout its Neotropical range. This evidence together with variable morphological characters and genetic differences supports that An. oswaldoi s.l. compromises a species complex. The recent fully integrated redescription of An. oswaldoi s.s. provides a solid taxonomic foundation from which to molecularly determine other members of the complex. Methods DNA sequences of the Second Internal Transcribed Spacer (ITS2 - rDNA) (n = 192) and the barcoding region of the Cytochrome Oxidase I gene (COI - mtDNA) (n = 110) were generated from 255 specimens of An. oswaldoi s.l. from 33 localities: Brazil (8 localities, including the lectotype series of An. oswaldoi), Ecuador (4), Colombia (17), Trinidad and Tobago (1), and Peru (3). COI sequences were analyzed employing the Kimura-two-parameter model (K2P), Bayesian analysis (MrBayes), Mixed Yule-Coalescent model (MYC, for delimitation of clusters) and TCS genealogies. Results Separate and combined analysis of the COI and ITS2 data sets unequivocally supported four separate species: two previously determined (An. oswaldoi s.s. and An. oswaldoi B) and two newly designated species in the Oswaldoi Complex (An. oswaldoi A and An. sp. nr. konderi). The COI intra- and inter-specific genetic distances for the four taxa were non-overlapping, averaging 0.012 (0.007 to 0.020) and 0.052 (0.038 to 0.064), respectively. The concurring four clusters delineated by MrBayes and MYC, and four independent TCS networks, strongly confirmed their separate species status. In addition, An. konderi of Sallum should be regarded as unique with respect to the above. Despite initially being included as an outgroup taxon, this species falls well within the examined taxa, suggesting a combined analysis of these taxa would be most appropriate. Conclusions: Through novel data and retrospective comparison of available COI and ITS2 DNA sequences, evidence is shown to support the separate species status of An. oswaldoi s.s., An. oswaldoi A and An. oswaldoi B, and at least two species in the closely related An. konderi complex (An. sp. nr. konderi, An. konderi of Sallum). Although An. oswaldoi s.s. has never been implicated in malaria transmission, An. oswaldoi B is a confirmed vector and the new species An. oswaldoi A and An. sp. nr. konderi are circumstantially implicated, most likely acting as secondary vectors.