Cosmic Strings in the Abelian Higgs Model with Conformal Coupling to Gravity

Cosmic string solutions of the abelian Higgs model with conformal coupling to gravity are shown to exist. The main characteristics of the solutions are presented and the differences with respect to the minimally coupled case are studied. An important difference is the absence of Bogomolnyi cosmic string solutions for conformal coupling. Several new features of the abelian Higgs cosmic strings of both types are discussed. The most interesting is perhaps a relation between the angular deficit and the central magnetic field which is bounded by a critical value.Comment: 22 pages, 10 figures; to appear in Phys. Rev.

Cosmological Model-independent Gamma-ray Bursts Calibration and its Cosmological Constraint to Dark Energy

As so far, the redshift of Gamma-ray bursts (GRBs) can extend to $z\sim 8$ which makes it as a complementary probe of dark energy to supernova Ia (SN Ia). However, the calibration of GRBs is still a big challenge when they are used to constrain cosmological models. Though, the absolute magnitude of GRBs is still unknown, the slopes of GRBs correlations can be used as a useful constraint to dark energy in a completely cosmological model independent way. In this paper, we follow Wang's model-independent distance measurement method and calculate their values by using 109 GRBs events via the so-called Amati relation. Then, we use the obtained model-independent distances to constrain $\Lambda$CDM model as an example.Comment: 16 pages, 5 figure

Observational Constraints to Ricci Dark Energy Model by Using: SN, BAO, OHD, fgas Data Sets

In this paper, we perform a global constraint on the Ricci dark energy model with both the flat case and the non-flat case, using the Markov Chain Monte Carlo (MCMC) method and the combined observational data from the cluster X-ray gas mass fraction, Supernovae of type Ia (397), baryon acoustic oscillations, current Cosmic Microwave Background, and the observational Hubble function. In the flat model, we obtain the best fit values of the parameters in $1\sigma, 2\sigma$ regions: $\Omega_{m0}=0.2927^{+0.0420 +0.0542}_{-0.0323 -0.0388}$, $\alpha=0.3823^{+0.0331 +0.0415}_{-0.0418 -0.0541}$, $Age/Gyr=13.48^{+0.13 +0.17}_{-0.16 -0.21}$, $H_0=69.09^{+2.56 +3.09}_{-2.37 -3.39}$. In the non-flat model, the best fit parameters are found in $1\sigma, 2\sigma$ regions:$\Omega_{m0}=0.3003^{+0.0367 +0.0429}_{-0.0371 -0.0423}$, $\alpha=0.3845^{+0.0386 +0.0521}_{-0.0474 -0.0523}$, $\Omega_k=0.0240^{+0.0109 +0.0133}_{-0.0130 -0.0153}$, $Age/Gyr=12.54^{+0.51 +0.65}_{-0.37 -0.49}$, $H_0=72.89^{+3.31 +3.88}_{-3.05 -3.72}$. Compared to the constraint results in the $\Lambda \textmd{CDM}$ model by using the same datasets, it is shown that the current combined datasets prefer the $\Lambda \textmd{CDM}$ model to the Ricci dark energy model.Comment: 12 pages, 3 figure

Dilatonic global strings

We examine the field equations of a self-gravitating global string in low energy superstring gravity, allowing for an arbitrary coupling of the global string to the dilaton. Massive and massless dilatons are considered. For the massive dilaton the spacetime is similar to the recently discovered non-singular time-dependent Einstein self-gravitating global string, but the massless dilaton generically gives a singular spacetime, even allowing for time-dependence. We also demonstrate a time-dependent non-singular string/anti-string configuration, in which the string pair causes a compactification of two of the spatial dimensions, albeit on a very large scale.Comment: 18 pages RevTeX, 3 figures, references amende

Observational constraints on holographic dark energy with varying gravitational constant

We use observational data from Type Ia Supernovae (SN), Baryon Acoustic Oscillations (BAO), Cosmic Microwave Background (CMB) and observational Hubble data (OHD), and the Markov Chain Monte Carlo (MCMC) method, to constrain the cosmological scenario of holographic dark energy with varying gravitational constant. We consider both flat and non-flat background geometry, and we present the corresponding constraints and contour-plots of the model parameters. We conclude that the scenario is compatible with observations. In 1$\sigma$ we find $\Omega_{\Lambda0}=0.72^{+0.03}_{-0.03}$, $\Omega_{k0}=-0.0013^{+0.0130}_{-0.0040}$, $c=0.80^{+0.19}_{-0.14}$ and $\Delta_G\equiv G'/G=-0.0025^{+0.0080}_{-0.0050}$, while for the present value of the dark energy equation-of-state parameter we obtain $w_0=-1.04^{+0.15}_{-0.20}$.Comment: 12 pages, 2 figures, version published in JCA

Equivalence between supersymmetric self-dual and Maxwell-Chern-Simons models coupled to a matter spinor superfield

We study the duality of the supersymmetric self-dual and Maxwell-Chern-Simons theories coupled to a fermionic matter superfield, using a master action. This approach evades the difficulties inherent to the quartic couplings that appear when matter is represented by a scalar superfield. The price is that the spinorial matter superfield represents a unusual supersymmetric multiplet, whose main physical properties we also discuss.Comment: v2, 16 pages, elsarticle.cls, accepted for publication in PL

Combined constraints on modified Chaplygin gas model from cosmological observed data: Markov Chain Monte Carlo approach

We use the Markov Chain Monte Carlo method to investigate a global constraints on the modified Chaplygin gas (MCG) model as the unification of dark matter and dark energy from the latest observational data: the Union2 dataset of type supernovae Ia (SNIa), the observational Hubble data (OHD), the cluster X-ray gas mass fraction, the baryon acoustic oscillation (BAO), and the cosmic microwave background (CMB) data. In a flat universe, the constraint results for MCG model are, $\Omega_{b}h^{2}=0.02263^{+0.00184}_{-0.00162}$ ($1\sigma$) $^{+0.00213}_{-0.00195}$ $(2\sigma)$, $B_{s}=0.7788^{+0.0736}_{-0.0723}$ ($1\sigma$) $^{+0.0918}_{-0.0904}$ $(2\sigma)$, $\alpha=0.1079^{+0.3397}_{-0.2539}$ ($1\sigma$) $^{+0.4678}_{-0.2911}$ $(2\sigma)$, $B=0.00189^{+0.00583}_{-0.00756}$ ($1\sigma$) $^{+0.00660}_{-0.00915}$ $(2\sigma)$, and $H_{0}=70.711^{+4.188}_{-3.142}$ ($1\sigma$) $^{+5.281}_{-4.149}$ $(2\sigma)$.Comment: 12 pages, 1figur

Thimet oligopeptidase (EC 3.4.24.15) key functions suggested by knockout mice phenotype characterization

Thimet oligopeptidase (THOP1) is thought to be involved in neuropeptide metabolism, antigen presentation, neurodegeneration, and cancer. Herein, the generation of THOP1 C57BL/6 knockout mice (THOP1(-/-)) is described showing that they are viable, have estrus cycle, fertility, and a number of puppies per litter similar to C57BL/6 wild type mice (WT). In specific brain regions, THOP1(-/-) exhibit altered mRNA expression of proteasome beta5, serotonin 5HT2a receptor and dopamine D2 receptor, but not of neurolysin (NLN). Peptidomic analysis identifies differences in intracellular peptide ratios between THOP1(-/-) and WT mice, which may affect normal cellular functioning. In an experimental model of multiple sclerosis THOP1(-/-) mice present worse clinical behavior scores compared to WT mice, corroborating its possible involvement in neurodegenerative diseases. THOP1(-/-) mice also exhibit better survival and improved behavior in a sepsis model, but also a greater peripheral pain sensitivity measured in the hot plate test after bradykinin administration in the paw. THOP1(-/-) mice show depressive-like behavior, as well as attention and memory retention deficits. Altogether, these results reveal a role of THOP1 on specific behaviors, immune-stimulated neurodegeneration, and infection-induced inflammation

Does accelerating universe indicates Brans-Dicke theory

The evolution of universe in Brans-Dicke (BD) theory is discussed in this paper. Considering a parameterized scenario for BD scalar field $\phi=\phi_{0}a^{\alpha}$ which plays the role of gravitational "constant" $G$, we apply the Markov Chain Monte Carlo method to investigate a global constraints on BD theory with a self-interacting potential according to the current observational data: Union2 dataset of type supernovae Ia (SNIa), high-redshift Gamma-Ray Bursts (GRBs) data, observational Hubble data (OHD), the cluster X-ray gas mass fraction, the baryon acoustic oscillation (BAO), and the cosmic microwave background (CMB) data. It is shown that an expanded universe from deceleration to acceleration is given in this theory, and the constraint results of dimensionless matter density $\Omega_{0m}$ and parameter $\alpha$ are, $\Omega_{0m}=0.286^{+0.037+0.050}_{-0.039-0.047}$ and $\alpha=0.0046^{+0.0149+0.0171}_{-0.0171-0.0206}$ which is consistent with the result of current experiment exploration, $\mid\alpha\mid \leq 0.132124$. In addition, we use the geometrical diagnostic method, jerk parameter $j$, to distinguish the BD theory and cosmological constant model in Einstein's theory of general relativity.Comment: 16 pages, 3 figure

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization