Coherent states of a particle in a magnetic field and the Stieltjes moment problem

A solution to a version of the Stieltjes moment. problem is presented. Using this solution, we construct a family of coherent states of a charged particle in a uniform magnetic field. We prove that these states form an overcomplete set that is normalized and resolves the unity. By the help of these coherent states we construct the Fock-Bergmann representation related to the particle quantization. This quantization procedure takes into account a circle topology of the classical motion. (C) 2009 Elsevier B.V. All rights reserved.

Photo-irradiation effects on the surface morphology of poly(p-phenylene vinylene) films

In this work, we have studied the surface morphology of photo-irradiated poly(p-phenylene vinylene) (PPV) thin films by using atomic force microscopy (AFM). We have analyzed the first-order statistical parameters, the height distribution and the distance between selected peaks. The second-order statistical analysis was introduced calculating the auto-covariance function to determine the correlation length between heights. We have observed that the photo-irradiation process produces a surface topology more homogeneous and isotropic such as a normal surface. In addition, the polymer surface irradiation can be used as a new methodology to obtain materials optically modified. (C) 2009 Elsevier B.V. All rights reserved.

MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modi. cations in their intrinsic organization. For instance, the network of transportation routes between cities and towns ( hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways ( paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation ( in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.

Sensitivity of complex networks measurements

Complex networks obtained from real-world networks are often characterized by incompleteness and noise, consequences of imperfect sampling as well as artifacts in the acquisition process. Because the characterization, analysis and modeling of complex systems underlain by complex networks are critically affected by the quality and completeness of the respective initial structures, it becomes imperative to devise methodologies for identifying and quantifying the effects of the sampling on the network structure. One way to evaluate these effects is through an analysis of the sensitivity of complex network measurements to perturbations in the topology of the network. In this paper, measurement sensibility is quantified in terms of the relative entropy of the respective distributions. Three particularly important kinds of progressive perturbations to the network are considered, namely, edge suppression, addition and rewiring. The measurements allowing the best balance of stability (smaller sensitivity to perturbations) and discriminability (separation between different network topologies) are identified with respect to each type of perturbation. Such an analysis includes eight different measurements applied on six different complex networks models and three real-world networks. This approach allows one to choose the appropriate measurements in order to obtain accurate results for networks where sampling bias cannot be avoided-a very frequent situation in research on complex networks.

Temperature effects on a network of dissipative quantum harmonic oscillators: collective damping and dispersion processes

In this paper we extend the results presented in (de Ponte, Mizrahi and Moussa 2007 Phys. Rev. A 76 032101) to treat quantitatively the effects of reservoirs at finite temperature in a bosonic dissipative network: a chain of coupled harmonic oscillators whatever its topology, i.e., whichever the way the oscillators are coupled together, the strength of their couplings and their natural frequencies. Starting with the case where distinct reservoirs are considered, each one coupled to a corresponding oscillator, we also analyze the case where a common reservoir is assigned to the whole network. Master equations are derived for both situations and both regimes of weak and strong coupling strengths between the network oscillators. Solutions of these master equations are presented through the normal ordered characteristic function. These solutions are shown to be significantly involved when temperature effects are considered, making difficult the analysis of collective decoherence and dispersion in dissipative bosonic networks. To circumvent these difficulties, we turn to the Wigner distribution function which enables us to present a technique to estimate the decoherence time of network states. Our technique proceeds by computing separately the effects of dispersion and the attenuation of the interference terms of the Wigner function. A detailed analysis of the dispersion mechanism is also presented through the evolution of the Wigner function. The interesting collective dispersion effects are discussed and applied to the analysis of decoherence of a class of network states. Finally, the entropy and the entanglement of a pure bipartite system are discussed.

Seeking for simplicity in complex networks

Complex networks can be understood as graphs whose connectivity properties deviate from those of regular or near-regular graphs, which are understood as being ""simple"". While a great deal of the attention so far dedicated to complex networks has been duly driven by the ""complex"" nature of these structures, in this work we address the identification of their simplicity. The basic idea is to seek for subgraphs whose nodes exhibit similar measurements. This approach paves the way for complementing the characterization of networks, including results suggesting that the protein-protein interaction networks, and to a lesser extent also the Internet, may be getting simpler over time. Copyright (C) EPLA, 2009

Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

Differently from theoretical scale-free networks, most real networks present multi-scale behavior, with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by concentric (or hierarchical) measurements. This paper extends previous methodologies based on concentric measurements, by studying the possibility of using agglomerative clustering methods, in order to obtain a set of functional groups of nodes, considering particular institutional collaboration network nodes, including various known communities (departments of the University of Sao Paulo). Among the interesting obtained findings, we emphasize the scale-free nature of the network obtained, as well as identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along concentric levels, contrariwise to the non-uniform pattern found in theoretical scale-free networks such as the BA model. (C) 2008 Elsevier B.V. All rights reserved.

On the effects of geographical constraints on task execution in complex

In the present work, the effects of spatial constraints on the efficiency of task execution in systems underlain by geographical complex networks are investigated, where the probability of connection decreases with the distance between the nodes. The investigation considers several configurations of the parameters defining the network connectivity, and the Barabasi-Albert network model is also considered for comparisons. The results show that the effect of connectivity is significant only for shorter tasks, the locality of connection simplied by the spatial constraints reduces efficiency, and the addition of edges can improve the efficiency of the execution, although with increasing locality of the connections the improvement is small.

Complex networks analysis of manual and machine translations

Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by NIT tools can be distinguished from their manual counterparts by means of metrics such as in-(ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better NIT tools and automatic evaluation metrics.

A pattern recognition approach to complex networks

Complex networks exist in many areas of science such as biology, neuroscience, engineering, and sociology. The growing development of this area has led to the introduction of several topological and dynamical measurements, which describe and quantify the structure of networks. Such characterization is essential not only for the modeling of real systems but also for the study of dynamic processes that may take place in them. However, it is not easy to use several measurements for the analysis of complex networks, due to the correlation between them and the difficulty of their visualization. To overcome these limitations, we propose an effective and comprehensive approach for the analysis of complex networks, which allows the visualization of several measurements in a few projections that contain the largest data variance and the classification of networks into three levels of detail, vertices, communities, and the global topology. We also demonstrate the efficiency and the universality of the proposed methods in a series of real-world networks in the three levels.

Estimating complex cortical networks via surface recordings-A critical note

We discuss potential caveats when estimating topologies of 3D brain networks from surface recordings. It is virtually impossible to record activity from all single neurons in the brain and one has to rely on techniques that measure average activity at sparsely located (non-invasive) recording sites Effects of this spatial sampling in relation to structural network measures like centrality and assortativity were analyzed using multivariate classifiers A simplified model of 3D brain connectivity incorporating both short- and long-range connections served for testing. To mimic M/EEG recordings we sampled this model via non-overlapping regions and weighted nodes and connections according to their proximity to the recording sites We used various complex network models for reference and tried to classify sampled versions of the ""brain-like"" network as one of these archetypes It was found that sampled networks may substantially deviate in topology from the respective original networks for small sample sizes For experimental studies this may imply that surface recordings can yield network structures that might not agree with its generating 3D network. (C) 2010 Elsevier Inc All rights reserved

Collapse of rho (xx) Ringlike Structures in 2DEGs Under Tilted Magnetic Fields

In the quantum Hall regime, the longitudinal resistivity rho (xx) plotted as a density-magnetic-field (n (2D) -B) diagram displays ringlike structures due to the crossings of two sets of spin split Landau levels from different subbands [see, e.g., Zhang et al., in Phys. Rev. Lett. 95:216801, 2005. For tilted magnetic fields, some of these ringlike structures ""shrink"" as the tilt angle is increased and fully collapse at theta (c) a parts per thousand 6A degrees. Here we theoretically investigate the topology of these structures via a non-interacting model for the 2DEG. We account for the inter Landau-level coupling induced by the tilted magnetic field via perturbation theory. This coupling results in anticrossings of Landau levels with parallel spins. With the new energy spectrum, we calculate the corresponding n (2D) -B diagram of the density of states (DOS) near the Fermi level. We argue that the DOS displays the same topology as rho (xx) in the n (2D) -B diagram. For the ring with filling factor nu=4, we find that the anticrossings make it shrink for increasing tilt angles and collapse at a large enough angle. Using effective parameters to fit the theta=0A degrees data, we find a collapsing angle theta (c) a parts per thousand 3.6A degrees. Despite this factor-of-two discrepancy with the experimental data, our model captures the essential mechanism underlying the ring collapse.

The CATH classification revisited-architectures reviewed and new ways to characterize structural divergence in superfamilies

The latest version of CATH (class, architecture, topology, homology) (version 3.2), released in July 2008 (http://www.cathdb.info), contains 1 14215 domains, 2178 Homologous superfamilies and 1110 fold groups. We have assigned 20 330 new domains, 87 new homologous superfamilies and 26 new folds since CATH release version 3.1. A total of 28 064 new domains have been assigned since our NAR 2007 database publication (CATH version 3.0). The CATH website has been completely redesigned and includes more comprehensive documentation. We have revisited the CATH architecture level as part of the development of a `Protein Chart` and present information on the population of each architecture. The CATHEDRAL structure comparison algorithm has been improved and used to characterize structural diversity in CATH superfamilies and structural overlaps between superfamilies. Although the majority of superfamilies in CATH are not structurally diverse and do not overlap significantly with other superfamilies, similar to 4% of superfamilies are very diverse and these are the superfamilies that are most highly populated in both the PDB and in the genomes. Information on the degree of structural diversity in each superfamily and structural overlaps between superfamilies can now be downloaded from the CATH website.

Thermodynamic characterization of the palm tree Roystonea regia peroxidase stability

The structural stability of a peroxidase, a dimeric protein from royal palm tree (Roystonea regia) leaves, has been characterized by high-sensitivity differential scanning calorimetry, circular dichroism, steady-state tryptophan fluorescence and analytical ultracentifugation under different solvent conditions. It is shown that the thermal and chemical (using guanidine hydrochloride (Gdn-HCl)) folding/unfolding of royal palm tree peroxidase (RPTP) at pH 7 is a reversible process involving a highly cooperative transition between the folded dimer and unfolded monomers, with a free stabilization energy of about 23 kcal per mol of monomer at 25 degrees C. The structural stability of RPTP is pH-dependent. At pH 3, where ion pairs have disappeared due to protonation, the thermally induced denaturation of RPTP is irreversible and strongly dependent upon the scan rate, suggesting that this process is under kinetic control. Moreover, thermally induced transitions at this pH value are dependent on the protein concentration, allowing it to be concluded that in solution RPTP behaves as dimer, which undergoes thermal denaturation coupled with dissociation. Analysis of the kinetic parameters of RPTP denaturation at pH 3 was accomplished on the basis of the simple kinetic scheme N ->(k) D, where k is a first-order kinetic constant that changes with temperature, as given by the Arrhenius equation; N is the native state, and D is the denatured state, and thermodynamic information was obtained by extrapolation of the kinetic transition parameters to an infinite heating rate. Obtained in this way, the value of RPTP stability at 25 degrees C is ca. 8 kcal per mole of monomer lower than at pH 7. In all probability, this quantity reflects the contribution of ion pair interactions to the structural stability of RPTP. From a comparison of the stability of RPTP with other plant peroxidases it is proposed that one of the main factors responsible for the unusually high stability of RPTP which enhances its potential use for biotechnological purposes, is its dimerization. (c) 2008 Elsevier Masson SAS. All rights reserved.

Cloning, expression, purification and characterization of recombinant glutathione-S-transferase from Xylella fastidiosa

Xylella fastidiosa is an important pathogen bacterium transmitted by xylem-feedings leafhoppers that colonizes the xylem of plants and causes diseases on several important crops including citrus variegated chlorosis (CVC) in orange and lime trees. Glutathione-S-transferases (GST) form a group of multifunctional isoenzymes that catalyzes both glutathione (GSH)-dependent conjugation and reduction reactions involved in the cellular detoxification of xenobiotic and endobiotic compounds. GSTs are the major detoxification enzymes found in the intracellular space and mainly in the cytosol from prokaryotes to mammals, and may be involved in the regulation of stress-activated signals by suppressing apoptosis signal-regulating kinase 1. In this study, we describe the cloning of the glutathione-S-transferase from X. fastidiosa into pET-28a(+) vector, its expression in Escherichia coli, purification and initial structural characterization. The purification of recombinant xfGST (rxfGST) to near homogeneity was achieved using affinity chromatography and size-exclusion chromatography (SEC). SEC demonstrated that rxfGST is a homodimer in solution. The secondary and tertiary structures of recombinant protein were analyzed by circular dichroism and fluorescence spectroscopy, respectively. The enzyme was assayed for activity and the results taken together indicated that rxfGST is a stable molecule, correctly folded, and highly active. Several members of the GST family have been extensively studied. However, xfGST is part of a less-studied subfamily which yet has not been structurally and biochemically characterized. In addition, these studies should provide a useful basis for future studies and biotechnological approaches of rxfGST. (C) 2008 Elsevier Inc. All rights reserved.