Magnetic and structural-properties of electrodeposited Co1-xPx amorphous ribbons

The specific magnetic moment, coercive force, anisotropy field, and saturation magnetostriction constant have been measured in Co(1-x)P(x) amorphous ribbons with 0.04 less-than-or-equal-to x less-than-or-equal-to 0.27. Differential scanning calorimetry, x-ray diffraction, and transmission electron microscopy analysis have been made in order to study the transition from the amorphous state to the crystalline one. Results suggest that transition takes place when x decreases from 0.19

Graph Metrics for Temporal Networks

Temporal networks, i.e., networks in which the interactions among a set of elementary units change over time, can be modelled in terms of time-varying graphs, which are time-ordered sequences of graphs over a set of nodes. In such graphs, the concepts of node adjacency and reachability crucially depend on the exact temporal ordering of the links. Consequently, all the concepts and metrics proposed and used for the characterisation of static complex networks have to be redefined or appropriately extended to time-varying graphs, in order to take into account the effects of time ordering on causality. In this chapter we discuss how to represent temporal networks and we review the definitions of walks, paths, connectedness and connected components valid for graphs in which the links fluctuate over time. We then focus on temporal node-node distance, and we discuss how to characterise link persistence and the temporal small-world behaviour in this class of networks. Finally, we discuss the extension of classic centrality measures, including closeness, betweenness and spectral centrality, to the case of time-varying graphs, and we review the work on temporal motifs analysis and the definition of modularity for temporal graphs.Comment: 26 pages, 5 figures, Chapter in Temporal Networks (Petter Holme and Jari Saram\"aki editors). Springer. Berlin, Heidelberg 201

Reconstruction of the Dark Energy equation of state

One of the main challenges of modern cosmology is to investigate the nature of dark energy in our Universe. The properties of such a component are normally summarised as a perfect fluid with a (potentially) time-dependent equation-of-state parameter $w(z)$. We investigate the evolution of this parameter with redshift by performing a Bayesian analysis of current cosmological observations. We model the temporal evolution as piecewise linear in redshift between `nodes', whose $w$-values and redshifts are allowed to vary. The optimal number of nodes is chosen by the Bayesian evidence. In this way, we can both determine the complexity supported by current data and locate any features present in $w(z)$. We compare this node-based reconstruction with some previously well-studied parameterisations: the Chevallier-Polarski-Linder (CPL), the Jassal-Bagla-Padmanabhan (JBP) and the Felice-Nesseris-Tsujikawa (FNT). By comparing the Bayesian evidence for all of these models we find an indication towards possible time-dependence in the dark energy equation-of-state. It is also worth noting that the CPL and JBP models are strongly disfavoured, whilst the FNT is just significantly disfavoured, when compared to a simple cosmological constant $w=-1$. We find that our node-based reconstruction model is slightly disfavoured with respect to the $\Lambda$CDM model.Comment: 17 pages, 5 figures, minor correction

Mesoscopic organization reveals the constraints governing C. elegans nervous system

One of the biggest challenges in biology is to understand how activity at the cellular level of neurons, as a result of their mutual interactions, leads to the observed behavior of an organism responding to a variety of environmental stimuli. Investigating the intermediate or mesoscopic level of organization in the nervous system is a vital step towards understanding how the integration of micro-level dynamics results in macro-level functioning. In this paper, we have considered the somatic nervous system of the nematode Caenorhabditis elegans, for which the entire neuronal connectivity diagram is known. We focus on the organization of the system into modules, i.e., neuronal groups having relatively higher connection density compared to that of the overall network. We show that this mesoscopic feature cannot be explained exclusively in terms of considerations, such as optimizing for resource constraints (viz., total wiring cost) and communication efficiency (i.e., network path length). Comparison with other complex networks designed for efficient transport (of signals or resources) implies that neuronal networks form a distinct class. This suggests that the principal function of the network, viz., processing of sensory information resulting in appropriate motor response, may be playing a vital role in determining the connection topology. Using modular spectral analysis, we make explicit the intimate relation between function and structure in the nervous system. This is further brought out by identifying functionally critical neurons purely on the basis of patterns of intra- and inter-modular connections. Our study reveals how the design of the nervous system reflects several constraints, including its key functional role as a processor of information.Comment: Published version, Minor modifications, 16 pages, 9 figure

Random Walks on Stochastic Temporal Networks

In the study of dynamical processes on networks, there has been intense focus on network structure -- i.e., the arrangement of edges and their associated weights -- but the effects of the temporal patterns of edges remains poorly understood. In this chapter, we develop a mathematical framework for random walks on temporal networks using an approach that provides a compromise between abstract but unrealistic models and data-driven but non-mathematical approaches. To do this, we introduce a stochastic model for temporal networks in which we summarize the temporal and structural organization of a system using a matrix of waiting-time distributions. We show that random walks on stochastic temporal networks can be described exactly by an integro-differential master equation and derive an analytical expression for its asymptotic steady state. We also discuss how our work might be useful to help build centrality measures for temporal networks.Comment: Chapter in Temporal Networks (Petter Holme and Jari Saramaki editors). Springer. Berlin, Heidelberg 2013. The book chapter contains minor corrections and modifications. This chapter is based on arXiv:1112.3324, which contains additional calculations and numerical simulation

The SERRATE protein is involved in alternative splicing in <em>Arabidopsis thaliana</em>

Howalternative splicing (AS) is regulated in plants has not yet been elucidated. Previously, we have shown that the nuclear cap-binding protein complex (AtCBC) is involved in AS in Arabidopsis thaliana. Here we show that both subunits of AtCBC (AtCBP20 and AtCBP80) interact with SERRATE (AtSE), a protein involved in the microRNA biogenesis pathway. Moreover, using a high-resolution reverse transcript-ase-polymerase chain reaction AS system we have found that AtSE influences AS in a similar way to the cap-binding complex (CBC), preferentially affecting selection of 50 splice site of first introns. The AtSE protein acts in cooperation with AtCBC: many changes observed in the mutant lacking the correct SERRATE activity were common to those observed in the cbp mutants. Interestingly, significant changes in AS of some genes were also observed in other mutants of plant microRNA biogenesis pathway, hyl1-2 and dcl1-7, but a majority of them did not cor-respond to the changes observed in the se-1mutant. Thus, the role of SERRATE in AS regulation is distinct from that of HYL1andDCL1, and is similar to the regu-lation of AS in which CBC is involved

Measurement of ISR-FSR interference in the processes e+ e- --> mu+ mu- gamma and e+ e- --> pi+ pi- gamma

Charge asymmetry in processes e+ e- --> mu+ mu- gamma and e+ e- --> pi+ pi- gamma is measured using 232 fb-1 of data collected with the BABAR detector at center-of-mass energies near 10.58 GeV. An observable is introduced and shown to be very robust against detector asymmetries while keeping a large sensitivity to the physical charge asymmetry that results from the interference between initial and final state radiation. The asymmetry is determined as afunction of the invariant mass of the final-state tracks from production threshold to a few GeV/c2. It is compared to the expectation from QED for e+ e- --> mu+ mu- gamma and from theoretical models for e+ e- --> pi+ pi- gamma. A clear interference pattern is observed in e+ e- --> pi+ pi- gamma, particularly in the vicinity of the f_2(1270) resonance. The inferred rate of lowest order FSR production is consistent with the QED expectation for e+ e- --> mu+ mu- gamma, and is negligibly small for e+ e- --> pi+ pi- gamma.Comment: 32 pages,29 figures, to be submitted to Phys. Rev.

Measurement of the quasi-elastic axial vector mass in neutrino-oxygen interactions

The weak nucleon axial-vector form factor for quasi-elastic interactions is determined using neutrino interaction data from the K2K Scintillating Fiber detector in the neutrino beam at KEK. More than 12,000 events are analyzed, of which half are charged-current quasi-elastic interactions nu-mu n to mu- p occurring primarily in oxygen nuclei. We use a relativistic Fermi gas model for oxygen and assume the form factor is approximately a dipole with one parameter, the axial vector mass M_A, and fit to the shape of the distribution of the square of the momentum transfer from the nucleon to the nucleus. Our best fit result for M_A = 1.20 \pm 0.12 GeV. Furthermore, this analysis includes updated vector form factors from recent electron scattering experiments and a discussion of the effects of the nucleon momentum on the shape of the fitted distributions.Comment: 14 pages, 10 figures, 6 table

Single-molecule experiments in biological physics: methods and applications

I review single-molecule experiments (SME) in biological physics. Recent technological developments have provided the tools to design and build scientific instruments of high enough sensitivity and precision to manipulate and visualize individual molecules and measure microscopic forces. Using SME it is possible to: manipulate molecules one at a time and measure distributions describing molecular properties; characterize the kinetics of biomolecular reactions and; detect molecular intermediates. SME provide the additional information about thermodynamics and kinetics of biomolecular processes. This complements information obtained in traditional bulk assays. In SME it is also possible to measure small energies and detect large Brownian deviations in biomolecular reactions, thereby offering new methods and systems to scrutinize the basic foundations of statistical mechanics. This review is written at a very introductory level emphasizing the importance of SME to scientists interested in knowing the common playground of ideas and the interdisciplinary topics accessible by these techniques. The review discusses SME from an experimental perspective, first exposing the most common experimental methodologies and later presenting various molecular systems where such techniques have been applied. I briefly discuss experimental techniques such as atomic-force microscopy (AFM), laser optical tweezers (LOT), magnetic tweezers (MT), biomembrane force probe (BFP) and single-molecule fluorescence (SMF). I then present several applications of SME to the study of nucleic acids (DNA, RNA and DNA condensation), proteins (protein-protein interactions, protein folding and molecular motors). Finally, I discuss applications of SME to the study of the nonequilibrium thermodynamics of small systems and the experimental verification of fluctuation theorems. I conclude with a discussion of open questions and future perspectives.Comment: Latex, 60 pages, 12 figures, Topical Review for J. Phys. C (Cond. Matt