Fast Hamiltonian sampling for large scale structure inference

In this work we present a new and efficient Bayesian method for nonlinear three dimensional large scale structure inference. We employ a Hamiltonian Monte Carlo (HMC) sampler to obtain samples from a multivariate highly non-Gaussian lognormal Poissonian density posterior given a set of observations. The HMC allows us to take into account the nonlinear relations between the observations and the underlying density field which we seek to recover. As the HMC provides a sampled representation of the density posterior any desired statistical summary, such as the mean, mode or variance, can be calculated from the set of samples. Further, it permits us to seamlessly propagate non-Gaussian uncertainty information to any final quantity inferred from the set of samples. The developed method is extensively tested in a variety of test scenarios, taking into account a highly structured survey geometry and selection effects. Tests with a mock galaxy catalog based on the millennium run show that the method is able to recover the filamentary structure of the nonlinear density field. The results further demonstrate the feasibility of non-Gaussian sampling in high dimensional spaces, as required for precision nonlinear large scale structure inference. The HMC is a flexible and efficient method, which permits for simple extension and incorporation of additional observational constraints. Thus, the method presented here provides an efficient and flexible basis for future high precision large scale structure inference.Comment: 14 pages, 7 figure

Cosmic Structure and Dynamics of the Local Universe

We present a cosmography analysis of the Local Universe based on the recently released Two-Micron All-Sky Redshift Survey (2MRS). Our method is based on a Bayesian Networks Machine Learning algorithm (the Kigen-code) which self-consistently samples the initial density fluctuations compatible with the observed galaxy distribution and a structure formation model given by second order Lagrangian perturbation theory (2LPT). From the initial conditions we obtain an ensemble of reconstructed density and peculiar velocity fields which characterize the local cosmic structure with high accuracy unveiling nonlinear structures like filaments and voids in detail. Coherent redshift space distortions are consistently corrected within 2LPT. From the ensemble of cross-correlations between the reconstructions and the galaxy field and the variance of the recovered density fields we find that our method is extremely accurate up to k ~ 1 h Mpc^-1 and still yields reliable results down to scales of about 3-4 h^-1 Mpc. The motion of the local group we obtain within ~ 80 h^-1 Mpc (v_LG=522+-86 km s^-1, l_LG=291^o +- 16^o, b_LG=34^o+-8^o) is in good agreement with measurements derived from the CMB and from direct observations of peculiar motions and is consistent with the predictions of LambdaCDM.Comment: 6 pages, 5 figures; accepted at MNRAS after minor correction

Tear proteome analysis in ocular surface diseases using label-free LC-MS/MS and multiplexedmicroarray biomarker validation

We analyzed the tear film proteome of patients with dry eye (DE), meibomian gland dysfunction (MGD), and normal volunteers (CT). Tear samples were collected from 70 individuals. Of these, 37 samples were analyzed using spectral-counting-based LC-MS/MS label-free quantitation, and 33 samples were evaluated in the validation of candidate biomarkers employing customized antibody microarray assays. Comparative analysis of tear protein profiles revealed differences in the expression levels of 26 proteins, including protein S100A6, annexin A1, cystatin-S, thioredoxin, phospholipase A2, antileukoproteinase, and lactoperoxidase. Antibody microarray validation of CST4, S100A6, and MMP9 confirmed the accuracy of previously reported ELISA assays, with an area under ROC curve (AUC) of 87.5%. Clinical endpoint analysis showed a good correlation between biomarker concentrations and clinical parameters. In conclusion, different sets of proteins differentiate between the groups. Apolipoprotein D, S100A6, S100A8, and ceruloplasmin discriminate best between the DE and CT groups. The differences between antileukoproteinase, phospholipase A2, and lactoperoxidase levels allow the distinction between MGD and DE, and the changes in the levels of annexin A1, clusterin, and alpha-1-acid glycoprotein 1, between MGD and CT groups. The functional network analysis revealed the main biological processes that should be examined to identify new candidate biomarkers and therapeutic targets

The velocity function in the local environment from LCDM and LWDM constrained simulations

Using constrained simulations of the local Universe for generic cold dark matter and for 1keV warm dark matter, we investigate the difference in the abundance of dark matter halos in the local environment. We find that the mass function within 20 Mpc/h of the Local Group is ~2 times larger than the universal mass function in the 10^9-10^13 M_odot/h mass range. Imposing the field of view of the on-going HI blind survey ALFALFA in our simulations, we predict that the velocity function in the Virgo-direction region exceeds the universal velocity function by a factor of 3. Furthermore, employing a scheme to translate the halo velocity function into a galaxy velocity function, we compare the simulation results with a sample of galaxies from the early catalog release of ALFALFA. We find that our simulations are able to reproduce the velocity function in the 80-300 km/s velocity range, having a value ~10 times larger than the universal velocity function in the Virgo-direction region. In the low velocity regime, 35-80 km/s, the warm dark matter simulation reproduces the observed flattening of the velocity function. On the contrary, the simulation with cold dark matter predicts a steep rise in the velocity function towards lower velocities; for V_max=35 km/s, it forecasts ~10 times more sources than the ones observed. If confirmed by the complete ALFALFA survey, our results indicate a potential problem for the cold dark matter paradigm or for the conventional assumptions about energetic feedback in dwarf galaxies.Comment: 24 pages, 14 figures, 1 table, accepted for publication in Ap

Direct electrochemistry of Heme Proteins on Electrodes Modified with Didodecyldimethyl Ammonium Bromide and Carbon Black

A novel matrix based on commercially available carbon black (CB) N220 and didodecyldimethyl ammonium bromide (DDAB) was shown to be a reliable support for direct electron transfer reactions between screen printed electrode (SPE) and Fe(III)-heme proteins. Cytochrome c(cytc), myoglobin (Mb), horseradish peroxidase (HRP) and cytochromes P450 (CYP 51A1, CYP 3A4, CYP 2B4) generated well-shaped cyclic voltammograms on SPE/CB/ DDAB electrodes (both in solution and in immobilized state). The attractive performance characteristics of CB modified electrodes are advantageous over single-walled carbon nanotubes (SW CNT) based ones. The achieved direct electrochemistry of heme proteins on CB/DDAB-modified electrodes provided successful elaboration of the immunosensor for cardiac Mb. The immunosensor showed applicability for diagnostics of myocardial infarction displaying significant difference in cardiac Mb content of human blood plasma samples taken from the corresponding patients

Bayesian non-linear large scale structure inference of the Sloan Digital Sky Survey data release 7

In this work we present the first non-linear, non-Gaussian full Bayesian large scale structure analysis of the cosmic density field conducted so far. The density inference is based on the Sloan Digital Sky Survey data release 7, which covers the northern galactic cap. We employ a novel Bayesian sampling algorithm, which enables us to explore the extremely high dimensional non-Gaussian, non-linear log-normal Poissonian posterior of the three dimensional density field conditional on the data. These techniques are efficiently implemented in the HADES computer algorithm and permit the precise recovery of poorly sampled objects and non-linear density fields. The non-linear density inference is performed on a 750 Mpc cube with roughly 3 Mpc grid-resolution, while accounting for systematic effects, introduced by survey geometry and selection function of the SDSS, and the correct treatment of a Poissonian shot noise contribution. Our high resolution results represent remarkably well the cosmic web structure of the cosmic density field. Filaments, voids and clusters are clearly visible. Further, we also conduct a dynamical web classification, and estimated the web type posterior distribution conditional on the SDSS data.Comment: 18 pages, 11 figure

Simulating the Formation of the Local Galaxy Population

We simulate the formation and evolution of the local galaxy population starting from initial conditions with a smoothed linear density field which matches that derived from the IRAS 1.2 Jy galaxy survey. Our simulations track the formation and evolution of all dark matter haloes more massive than 10e+11 solar masses out to a distance of 8000 km/s from the Milky Way. We implement prescriptions similar to those of Kauffmann et al. (1999) to follow the assembly and evolution of the galaxies within these haloes. We focus on two variants of the CDM cosmology: an LCDM and a tCDM model. Galaxy formation in each is adjusted to reproduce the I-band Tully-Fisher relation of Giovanelli et al. (1997). We compare the present-day luminosity functions, colours, morphology and spatial distribution of our simulated galaxies with those of the real local population, in particular with the Updated Zwicky Catalog, with the IRAS PSCz redshift survey, and with individual local clusters such as Coma, Virgo and Perseus. We also use the simulations to study the clustering bias between the dark matter and galaxies of differing type. Although some significant discrepancies remain, our simulations recover the observed intrinsic properties and the observed spatial distribution of local galaxies reasonably well. They can thus be used to calibrate methods which use the observed local galaxy population to estimate the cosmic density parameter or to draw conclusions about the mechanisms of galaxy formation. To facilitate such work, we publically release our z=0 galaxy catalogues, together with the underlying mass distribution.Comment: 25 pages, 20 figures, submitted to MNRAS. High resolution copies of figures 1 and 3, halo and galaxy catalogues can be found at http://www.mpa-garching.mpg.de/NumCos/CR/index.htm

Bayesian reconstruction of the cosmological large-scale structure: methodology, inverse algorithms and numerical optimization

We address the inverse problem of cosmic large-scale structure reconstruction from a Bayesian perspective. For a linear data model, a number of known and novel reconstruction schemes, which differ in terms of the underlying signal prior, data likelihood, and numerical inverse extra-regularization schemes are derived and classified. The Bayesian methodology presented in this paper tries to unify and extend the following methods: Wiener-filtering, Tikhonov regularization, Ridge regression, Maximum Entropy, and inverse regularization techniques. The inverse techniques considered here are the asymptotic regularization, the Jacobi, Steepest Descent, Newton-Raphson, Landweber-Fridman, and both linear and non-linear Krylov methods based on Fletcher-Reeves, Polak-Ribiere, and Hestenes-Stiefel Conjugate Gradients. The structures of the up-to-date highest-performing algorithms are presented, based on an operator scheme, which permits one to exploit the power of fast Fourier transforms. Using such an implementation of the generalized Wiener-filter in the novel ARGO-software package, the different numerical schemes are benchmarked with 1-, 2-, and 3-dimensional problems including structured white and Poissonian noise, data windowing and blurring effects. A novel numerical Krylov scheme is shown to be superior in terms of performance and fidelity. These fast inverse methods ultimately will enable the application of sampling techniques to explore complex joint posterior distributions. We outline how the space of the dark-matter density field, the peculiar velocity field, and the power spectrum can jointly be investigated by a Gibbs-sampling process. Such a method can be applied for the redshift distortions correction of the observed galaxies and for time-reversal reconstructions of the initial density field.Comment: 40 pages, 11 figure

Active-site structure, binding and redox activity of the heme–thiolate enzyme CYP2D6 immobilized on coated Ag electrodes: a surface-enhanced resonance Raman scattering study

Surface-enhance resonance Raman scattering spectra of the heme–thiolate enzyme cytochrome P450 2D6 (CYP2D6) adsorbed on Ag electrodes coated with 11-mercaptoundecanoic acid (MUA) were obtained in various experimental conditions. An analysis of these spectra, and a comparison between them and the RR spectra of CYP2D6 in solution, indicated that the enzyme’s active site retained its nature of six-coordinated low-spin heme upon immobilization. Moreover, the spectral changes detected in the presence of dextromethorphan (a CYP2D6 substrate) and imidazole (an exogenous heme axial ligand) indicated that the immobilized enzyme also preserved its ability to reversibly bind a substrate and form a heme–imidazole complex. The reversibility of these processes could be easily verified by flowing alternately solutions of the various compounds and the buffer through a home-built spectroelectrochemical flow cell which contained a sample of immobilized protein, without the need to disassemble the cell between consecutive spectral data acquisitions. Despite immobilized CYP2D6 being effectively reduced by a sodium dithionite solution, electrochemical reduction via the Ag electrode was not able to completely reduce the enzyme, and led to its extensive inactivation. This behavior indicated that although the enzyme’s ability to exchange electrons is not altered by immobilization per se, MUA-coated electrodes are not suited to perform direct electrochemistry of CYP2D6