Effect of weak dipolar interaction on the magnetic properties of Ni nanoparticles assembly analyzed with different protocols

The effect of weak dipolar interactions (DIs) between Ni nanoparticles (NPs) in samples with different Ni concentrations was investigated by performing a detailed characterization of their structural and magnetic properties. From the determination of several physical parameters of Ni NP assemblies, it was found that the ac and dc magnetic susceptibility measurements are valuable for identifying the DIs between NPs while hysteresis loops measurements showed to be very insensitive, provided that the strength of the DI field is much smaller than the maximum coercive field. Therefore, the sensitivity of the observed static and dynamical magnetic properties to the effect of weak DI depends on the measurement protocols used. (C) 2011 American Institute of Physics. [doi:10.1063/1.3556767]

Casimir interaction between a perfect conductor and graphene described by the Dirac model

We adopt the Dirac model for graphene and calculate the Casimir interaction energy between a plane suspended graphene sample and a parallel plane perfect conductor. This is done in two ways. First, we use the quantum-field-theory approach and evaluate the leading-order diagram in a theory with 2+1-dimensional fermions interacting with 3+1-dimensional photons. Next, we consider an effective theory for the electromagnetic field with matching conditions induced by quantum quasiparticles in graphene. The first approach turns out to be the leading order in the coupling constant of the second one. The Casimir interaction for this system appears to be rather weak. It exhibits a strong dependence on the mass of the quasiparticles in graphene.

Network of recurrent events for the Olami-Feder-Christensen model

We numerically study the dynamics of a discrete spring-block model introduced by Olami, Feder, and Christensen (OFC) to mimic earthquakes and investigate to what extent this simple model is able to reproduce the observed spatiotemporal clustering of seismicity. Following a recently proposed method to characterize such clustering by networks of recurrent events [J. Davidsen, P. Grassberger, and M. Paczuski, Geophys. Res. Lett. 33, L11304 (2006)], we find that for synthetic catalogs generated by the OFC model these networks have many nontrivial statistical properties. This includes characteristic degree distributions, very similar to what has been observed for real seismicity. There are, however, also significant differences between the OFC model and earthquake catalogs, indicating that this simple model is insufficient to account for certain aspects of the spatiotemporal clustering of seismicity.

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs-a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.

Knowledge acquisition by networks of interacting agents in the presence of observation errors

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Renyi or Barabasi-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.

Spin Hall effect due to intersubband-induced spin-orbit interaction in symmetric quantum wells

We investigate the intrinsic spin Hall effect in two-dimensional electron gases in quantum wells with two subbands, where a new intersubband-induced spin-orbit coupling is operative. The bulk spin Hall conductivity sigma(z)(xy) is calculated in the ballistic limit within the standard Kubo formalism in the presence of a magnetic field B and is found to remain finite in the B=0 limit, as long as only the lowest subband is occupied. Our calculated sigma(z)(xy) exhibits a nonmonotonic behavior and can change its sign as the Fermi energy (the carrier areal density n(2D)) is varied between the subband edges. We determine the magnitude of sigma(z)(xy) for realistic InSb quantum wells by performing a self-consistent calculation of the intersubband-induced spin-orbit coupling.

Control of the persistent currents in two interacting quantum rings through the Coulomb interaction and interring tunneling

The persistent current in two vertically coupled quantum rings containing few electrons is studied. We find that the Coulomb interaction between the rings in the absence of tunneling affects the persistent current in each ring and the ground-state configurations. Quantum tunneling between the rings alters significantly the ground state and the persistent current in the system.

Intersubband-induced spin-orbit interaction in quantum wells

Recently, we have found an additional spin-orbit (SO) interaction in quantum wells with two subbands [Bernardes , Phys. Rev. Lett. 99, 076603 (2007)]. This new SO term is nonzero even in symmetric geometries, as it arises from the intersubband coupling between confined states of distinct parities, and its strength is comparable to that of the ordinary Rashba. Starting from the 8x8 Kane model, here we present a detailed derivation of this new SO Hamiltonian and the corresponding SO coupling. In addition, within the self-consistent Hartree approximation, we calculate the strength of this new SO coupling for realistic symmetric modulation-doped wells with two subbands. We consider gated structures with either a constant areal electron density or a constant chemical potential. In the parameter range studied, both models give similar results. By considering the effects of an external applied bias, which breaks the structural inversion symmetry of the wells, we also calculate the strength of the resulting induced Rashba couplings within each subband. Interestingly, we find that for double wells the Rashba couplings for the first and second subbands interchange signs abruptly across the zero bias, while the intersubband SO coupling exhibits a resonant behavior near this symmetric configuration. For completeness we also determine the strength of the Dresselhaus couplings and find them essentially constant as function of the applied bias.

Anomalous Rashba spin-orbit interaction in InAs/GaSb quantum wells

We theoretically investigate the Rashba spin-orbit interaction in InAs/GaSb quantum wells (QWs). We find that the Rashba spin-splitting (RSS) sensitively depends on the thickness of the InAs layer. The RSS exhibits nonlinear behavior for narrow InAs/GaSb QWs and the oscillating feature for wide InAs/GaSb QWs. The nonlinear and oscillating behaviors arise from the weakened and enhanced interband coupling. The RSS also show asymmetric features respect to the direction of the external electric field. (C) 2008 American Institute of Physics.

Scanning tunneling microscope operating as a spin diode

We theoretically investigate spin-polarized transport in a system composed of a ferromagnetic scanning-tunneling-microscope (STM) tip coupled to an adsorbed atom (adatom) on a host surface. Electrons can tunnel directly from the tip to the surface or via the adatom. Since the tip is ferromagnetic and the host surface (metal or semiconductor) is nonmagnetic we obtain a spin-diode effect when the adatom is in the regime of single occupancy. This effect leads to an unpolarized current for direct bias (V > 0) and polarized current for reverse (V < 0) bias voltages, if the tip is nearby the adatom. Within the nonequilibrium Keldysh technique we analyze the interplay between the lateral displacement of the tip and the intra adatom Coulomb interaction on the spin-diode effect. As the tip moves away from the adatom the spin-diode effect vanishes and the currents become polarized for both V > 0 and V < 0. We also find an imbalance between the up and down spin populations in the adatom, which can be tuned by the tip position and the bias. Finally, due to the presence of the adsorbate on the surface, we observe spin-resolved Friedel oscillations in the current, which reflects the oscillations in the calculated local density of states (LDOS) of the subsystem surface + adatom.

Ultra-high-energy cosmic rays from centaurus A: jet interaction with gaseous shells

Ultra-high-energy cosmic rays (UHECRs), with energies above similar to 6 x 10(19) eV, seem to show a weak correlation with the distribution of matter relatively near to us in the universe. It has earlier been proposed that UHECRs could be accelerated in either the nucleus or the outer lobes of the nearby radio galaxy Cen A. We show that UHECR production at a spatially intermediate location about 15 kpc northeast from the nucleus, where the jet emerging from the nucleus is observed to strike a large star-forming shell of gas, is a plausible alternative. A relativistic jet is capable of accelerating lower energy heavy seed cosmic rays (CRs) to UHECRs on timescales comparable to the time it takes the jet to pierce the large gaseous cloud. In this model, many CRs arising from a starburst, with a composition enhanced in heavy elements near the knee region around PeV, are boosted to ultra-high energies by the relativistic shock of a newly oriented jet. This model matches the overall spectrum shown by the Auger data and also makes a prediction for the chemical composition as a function of particle energy. We thus predict an observable anisotropy in the composition at high energy in the sense that lighter nuclei should preferentially be seen toward the general direction of Cen A. Taking into consideration the magnetic field models for the Galactic disk and a Galactic magnetic wind, this scenario may resolve the discrepancy between HiRes and Auger results concerning the chemical composition of UHECRs.

Synergistic interaction between gold nanoparticles and nickel phthalocyanine in layer-by-layer (LbL) films: evidence of constitutional dynamic chemistry (CDC)

The concept of constitutional dynamic chemistry (CDC) based on the control of non-covalent interactions in supramolecular structures is promising for having a large impact on nanoscience and nanotechnology if adequate nanoscale manipulation methods are used. In this study, we demonstrate that the layer-by-layer (LbL) technique may be used to produce electroactive electrodes with ITO coated by tetrasulfonated nickel phthalocyanine (NiTsPc) alternated with poly(allylamine hydrochloride) (PAH) incorporating gold nanoparticles (AuNP), in which synergy has been achieved in the interaction between the nanoparticles and NiTsPc. The catalytic activity toward hydrogen peroxide (H(2)O(2)) in multilayer films was investigated using cyclic voltammetry, where oxidation of H(2)O(2) led to increased currents in the PAH-AuNP/NiTsPc films for the electrochemical processes associated with the phthalocyanine ring and nickel at 0.52 and 0.81 V vs. SCE, respectively, while for PAH/NiTsPc films (without AuNP) only the first redox process was affected. In control experiments we found out that the catalytic activity was not solely due to the presence of AuNP, but rather to the nanoparticles inducing NiTsPc supramolecular structures that favored access to their redox sites, thus yielding strong charge transfer. The combined effects of NiTsPc and AuNP, which could only be observed in nanostructured LbL films, point to another avenue to pursue within the CDC paradigm.

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.

Role of the gp85/Trans-Sialidases in Trypanosoma cruzi Tissue Tropism: Preferential Binding of a Conserved Peptide Motif to the Vasculature In Vivo

Background: Transmitted by blood-sucking insects, the unicellular parasite Trypanosoma cruzi is the causative agent of Chagas' disease, a malady manifested in a variety of symptoms from heart disease to digestive and urinary tract dysfunctions. The reasons for such organ preference have been a matter of great interest in the field, particularly because the parasite can invade nearly every cell line and it can be found in most tissues following an infection. Among the molecular factors that contribute to virulence is a large multigene family of proteins known as gp85/trans-sialidase, which participates in cell attachment and invasion. But whether these proteins also contribute to tissue homing had not yet been investigated. Here, a combination of endothelial cell immortalization and phage display techniques has been used to investigate the role of gp85/trans-sialidase in binding to the vasculature. Methods: Bacteriophage expressing an important peptide motif (denominated FLY) common to all gp85/trans-sialidase proteins was used as a surrogate to investigate the interaction of this motif with the endothelium compartment. For that purpose phage particles were incubated with endothelial cells obtained from different organs or injected into mice intravenously and the number of phage particles bound to cells or tissues was determined. Binding of phages to intermediate filament proteins has also been studied. Findings and Conclusions: Our data indicate that FLY interacts with the endothelium in an organ-dependent manner with significantly higher avidity for the heart vasculature. Phage display results also show that FLY interaction with intermediate filament proteins is not limited to cytokeratin 18 (CK18), which may explain the wide variety of cells infected by the parasite. This is the first time that members of the intermediate filaments in general, constituted by a large group of ubiquitously expressed proteins, have been implicated in T. cruzi cell invasion and tissue homing.

Haplotype distribution of five nuclear genes based on network genealogies and Bayesian inference indicates that Trypanosoma cruzi hybrid strains are polyphyletic

Chagas disease is still a major public health problem in Latin America. Its causative agent, Trypanosoma cruzi, can be typed into three major groups, T. cruzi I, T. cruzi II and hybrids. These groups each have specific genetic characteristics and epidemiological distributions. Several highly virulent strains are found in the hybrid group; their origin is still a matter of debate. The null hypothesis is that the hybrids are of polyphyletic origin, evolving independently from various hybridization events. The alternative hypothesis is that all extant hybrid strains originated from a single hybridization event. We sequenced both alleles of genes encoding EF-1 alpha, actin and SSU rDNA of 26 T. cruzi strains and DHFR-TS and TR of 12 strains. This information was used for network genealogy analysis and Bayesian phylogenies. We found T. cruzi I and T. cruzi II to be monophyletic and that all hybrids had different combinations of T. cruzi I and T. cruzi II haplotypes plus hybrid-specific haplotypes. Bootstrap values (networks) and posterior probabilities (Bayesian phylogenies) of clades supporting the monophyly of hybrids were far below the 95% confidence interval, indicating that the hybrid group is polyphyletic. We hypothesize that T. cruzi I and T. cruzi II are two different species and that the hybrids are extant representatives of independent events of genome hybridization, which sporadically have sufficient fitness to impact on the epidemiology of Chagas disease.