First-order turbulence closure for modelling complex canopy flows

Simple first-order closure remains an attractive way of formulating equations for complex canopy flows when the aim is to find analytic or simple numerical solutions to illustrate fundamental physical processes. Nevertheless, the limitations of such closures must be understood if the resulting models are to illuminate rather than mislead. We propose five conditions that first-order closures must satisfy then test two widely used closures against them. The first is the eddy diffusivity based on a mixing length. We discuss the origins of this approach, its use in simple canopy flows and extensions to more complex flows. We find that it satisfies most of the conditions and, because the reasons for its failures are well understood, it is a reliable methodology. The second is the velocity-squared closure that relates shear stress to the square of mean velocity. Again we discuss the origins of this closure and show that it is based on incorrect physical principles and fails to satisfy any of the five conditions in complex canopy flows; consequently its use can lead to actively misleading conclusions.

Influence of a co-substituted A-site on structural characteristics and ferroelectricity of (Pb, Ba, Ca)TiO3 complex perovskites: analysis of local-, medium- and long-range order

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Complex sochastic boolean systems : new properties of the intrinsic order graph

[EN]A complex stochastic Boolean system (CSBS) is a system depending on an arbitrary number n of stochastic Boolean variables. The analysis of CSBSs is mainly based on the intrinsic order: a partial order relation defined on the set f0; 1gn of binary n-tuples. The usual graphical representation for a CSBS is the intrinsic order graph: the Hasse diagram of the intrinsic order. In this paper, some new properties of the intrinsic order graph are studied. Particularly, the set and the number of its edges, the degree and neighbors of each vertex, as well as typical properties, such as the symmetry and fractal structure of this graph, are analyzed…

Stability switches in a first-order complex neutral delay equation

Stability of the first-order neutral delay equation x’ (t) + ax’ (t – τ) = bx(t) + cx(t – τ) with complex coefficients is studied, by analyzing the existence of stability switches.

Novel complex gratings with third-order group-delay variations for tunable pure dispersion slope compensation

We present a novel tunable dispersion compensator that can provide pure slope compensation. The approach uses two specially designed complex fiber Bragg gratings (FBGs) with reversely varied third-order group delay curves to generate the dispersion slope. The slope can be changed by adjusting the relative wavelength positions of the two FBGs. Several design examples of such complex gratings are presented and discussed. Experimentally, we achieve a dispersion slope tuning range of +/-650ps/nm2 with >0.9nm usable bandwidth.

Quantum transition state theory and solving a first order master equation for complex reactions numerically

The field of chemical kinetics is an exciting and active field. The prevailing theories make a number of simplifying assumptions that do not always hold in actual cases. Another current problem concerns a development of efficient numerical algorithms for solving the master equations that arise in the description of complex reactions. The objective of the present work is to furnish a completely general and exact theory of reaction rates, in a form reminiscent of transition state theory, valid for all fluid phases and also to develop a computer program that can solve complex reactions by finding the concentrations of all participating substances as a function of time. To do so, the full quantum scattering theory is used for deriving the exact rate law, and then the resulting cumulative reaction probability is put into several equivalent forms that take into account all relativistic effects if applicable, including one that is strongly reminiscent of transition state theory, but includes corrections from scattering theory. Then two programs, one for solving complex reactions, the other for solving first order linear kinetic master equations to solve them, have been developed and tested for simple applications.

Characterization of subgraph relationships and distribution in complex networks

A network can be analyzed at different topological scales, ranging from single nodes to motifs, communities, up to the complete structure. We propose a novel approach which extends from single nodes to the whole network level by considering non-overlapping subgraphs (i.e. connected components) and their interrelationships and distribution through the network. Though such subgraphs can be completely general, our methodology focuses on the cases in which the nodes of these subgraphs share some special feature, such as being critical for the proper operation of the network. The methodology of subgraph characterization involves two main aspects: (i) the generation of histograms of subgraph sizes and distances between subgraphs and (ii) a merging algorithm, developed to assess the relevance of nodes outside subgraphs by progressively merging subgraphs until the whole network is covered. The latter procedure complements the histograms by taking into account the nodes lying between subgraphs, as well as the relevance of these nodes to the overall subgraph interconnectivity. Experiments were carried out using four types of network models and five instances of real-world networks, in order to illustrate how subgraph characterization can help complementing complex network-based studies.

Chain motifs: The tails and handles of complex networks

A great part of the interest in complex networks has been motivated by the presence of structured, frequently nonuniform, connectivity. Because diverse connectivity patterns tend to result in distinct network dynamics, and also because they provide the means to identify and classify several types of complex network, it becomes important to obtain meaningful measurements of the local network topology. In addition to traditional features such as the node degree, clustering coefficient, and shortest path, motifs have been introduced in the literature in order to provide complementary descriptions of the network connectivity. The current work proposes a different type of motif, namely, chains of nodes, that is, sequences of connected nodes with degree 2. These chains have been subdivided into cords, tails, rings, and handles, depending on the type of their extremities (e.g., open or connected). A theoretical analysis of the density of such motifs in random and scale-free networks is described, and an algorithm for identifying these motifs in general networks is presented. The potential of considering chains for network characterization has been illustrated with respect to five categories of real-world networks including 16 cases. Several interesting findings were obtained, including the fact that several chains were observed in real-world networks, especially the world wide web, books, and the power grid. The possibility of chains resulting from incompletely sampled networks is also investigated.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Coarse-grid higher order finite-difference time-domain algorithm with low dispersion errors

Higher order (2,4) FDTD schemes used for numerical solutions of Maxwell`s equations are focused on diminishing the truncation errors caused by the Taylor series expansion of the spatial derivatives. These schemes use a larger computational stencil, which generally makes use of the two constant coefficients, C-1 and C-2, for the four-point central-difference operators. In this paper we propose a novel way to diminish these truncation errors, in order to obtain more accurate numerical solutions of Maxwell`s equations. For such purpose, we present a method to individually optimize the pair of coefficients, C-1 and C-2, based on any desired grid size resolution and size of time step. Particularly, we are interested in using coarser grid discretizations to be able to simulate electrically large domains. The results of our optimization algorithm show a significant reduction in dispersion error and numerical anisotropy for all modeled grid size resolutions. Numerical simulations of free-space propagation verifies the very promising theoretical results. The model is also shown to perform well in more complex, realistic scenarios.

FtsH2 and FtsH5: two homologous subunits use different integration mechanisms leading to the same thylakoid multimeric complex

P>The Arabidopsis thylakoid FtsH protease complex is composed of FtsH1/FtsH5 (type A) and FtsH2/FtsH8 (type B) subunits. Type A and type B subunits display a high degree of sequence identity throughout their mature domains, but no similarity in their amino-terminal targeting peptide regions. In chloroplast import assays, FtsH2 and FtsH5 were imported and subsequently integrated into thylakoids by a two-step processing mechanism that resulted in an amino-proximal lumenal domain, a single transmembrane anchor, and a carboxyl proximal stromal domain. FtsH2 integration into washed thylakoids was entirely dependent on the proton gradient, whereas FtsH5 integration was dependent on NTPs, suggesting their integration by Tat and Sec pathways, respectively. This finding was corroborated by in organello competition and by antibody inhibition experiments. A series of constructs were made in order to understand the molecular basis for different integration pathways. The amino proximal domains through the transmembrane anchors were sufficient for proper integration as demonstrated with carboxyl-truncated versions of FtsH2 and FtsH5. The mature FtsH2 protein was found to be incompatible with the Sec machinery as determined with targeting peptide-swapping experiments. Incompatibility does not appear to be determined by any specific element in the FtsH2 domain as no single domain was incompatible with Sec transport. This suggests an incompatible structure that requires the intact FtsH2. That the highly homologous type A and type B subunits of the same multimeric complex use different integration pathways is a striking example of the notion that membrane insertion pathways have evolved to accommodate structural features of their respective substrates.

Ruthenium-nitrite complex as pro-drug releases NO in a tissue and enzyme-dependent way

Nitric oxide (NO) plays an important role in the control of the vascular tone and the most often employed NO donors have limitations due to their harmful side-effects. In this context, new NO donors have been prepared, in order to minimize such undesirable effects. cis-[Ru(bpy)(2)(py)NO(2)](PF(6)) (RuBPY) is a new nitrite complex synthesized in our laboratory that releases NO in the presence of the vascular tissue only. In this work the vasorelaxation induced by this NO donor has been studied and compared to that obtained with the well known NO donor SNP. The relaxation induced by RuBPY is concentration-dependent in denuded rat aortas pre-contracted with phenylephrine (EC(50)). This new compound induced relaxation with efficacy similar to that of SNP, although its potency is lower. The time elapsed until maximum relaxation is achieved (E(max) = 240 s) is similar to measured for SNP (210 s). Vascular reactivity experiments demonstrated that aortic relaxation by RuBPY is inhibited by the soluble guanylyl-cyclase inhibitor 1H-[1,2,4] oxadiozolo[4,3-a]quinoxaline-1-one (ODQ 1 mu M). In a similar way, 1 mu M ODQ also reduces NO release from the complex as measured with DAF-2 DA by confocal microscopy. These findings suggest that this new complex RuBPY that has nitrite in its structure releases NO inside the vascular smooth muscle cell. This ruthenium complex releases significant amounts of NO only in the presence of the aortic tissue. Reduction of nitrite to NO is most probably dependent on the soluble guanylyl-cyclase enzyme, since NO release is inhibited by ODQ. (C) 2011 Elsevier Inc. All rights reserved.

A new nitrosyl ruthenium complex nitric oxide donor presents higher efficacy than sodium nitroprusside on relaxation of airway smooth muscle

Nitric oxide (NO) has been demonstrated to be the primary agent in relaxing airways in humans and animals. We investigated the mechanisms involved in the relaxation induced by NO-donors, ruthenium complex [Ru(terpy)(bdq)NO(+)](3+) (TERPY) and sodium nitroprusside (SNP) in isolated trachea of rats contracted with carbachol in an isolated organs chamber. For instance, we verified the contribution of K(+) channels, the importance of sGC/cGMP pathway, the influence of the extra and intracellular Ca(2+) sources and the contribution of the epithelium on the relaxing response. Additionally, we have used confocal microscopy in order to analyze the action of the NO-donors on cytosolic Ca(2+) concentration. The results demonstrated that both compounds led to the relaxation of trachea in a dependent-concentration way. However, the maximum effect (E(max)) of TERPY is higher than the SNP. The relaxation induced by SNP (but not TERPY) was significantly reduced by pretreatment with ODQ (sGC inhibitor). Only TERPY-induced relaxation was reduced by tetraethylammonium (K(+) channels blocker) and by pre-contraction with 75 mM KCl (membrane depolarization). The response to both NO-donors was not altered by the presence of thapsigargin (sarcoplasmic reticulum Ca(2+)-ATPase inhibitor). The epithelium removal has reduced the relaxation only to SNP, and it has no effect on TERPY. The both NO-donors reduced the contraction evoked by Ca(2+) influx, while TERPY have shown a higher inhibitory effect on contraction. Moreover, the TERPY was more effective than SNP in reducing the cytosolic Ca(2+) concentration measured by confocal microscopy. In conclusion, these results show that TERPY induces airway smooth muscle relaxation by cGMP-independent mechanisms, it involves the fluxes of Ca(2+) and K(+) across the membrane, it is more effective in reducing cytosolic Ca(2+) concentration and inducing relaxation in the rat trachea than the standard drug, SNP. (C) 2011 Elsevier B.V. All rights reserved.

In vitro metabolism study of a new nitrosyl ruthenium complex [Ru(NH center dot NHq)(terpy)NO](3+) nitric oxide donor using rat microsomes

A new nitrosyl ruthenium complex [Ru(NH center dot NHq)(terpy)NO](3+) nitric oxide donor was recently developed and due to its excellent vasodilator activity, it has been considered as a potential drug candidate. Drug metabolism is one of the main parameters that should be evaluated in the early drug development, so the biotransformation of this complex by rat hepatic microsomes was investigated. In order to perform the biotransformation study, a simple, sensitive and selective HPLC method was developed and carefully validated. The parameters evaluated in the validation procedure were: linearity, recovery, precision, accuracy, selectivity and stability. Except for the stability study, all the parameters evaluated presented values below the recommended by FDA guidelines. The stability study showed a time-dependent degradation profile. After method validation, the biotransformation study was accomplished and the kinetic parameters were determined. The biotransformation study obeyed the Michaelis-Menten kinetics. The V(max) and K(m) were, respectively, 0.1625 +/- 0.010 mu mol/mg protein/min and 79.97 +/- 11.52 mu M. These results indicate that the nitrosyl complex is metabolized by CYP450. (C) 2009 Elsevier Inc. All rights reserved.