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In high-energy astronomy, spectro-imaging instruments such as X-ray detectors allow investigation of the spatial and spectral properties of extended sources including galaxy clusters, galaxies, diffuse interstellar medium, supernova remnants, and pulsar wind nebulae. In these sources, each physical component possesses a different spatial and spectral signature, but the components are entangled. Extracting the intrinsic spatial and spectral information of the individual components from this data is a challenging task. Current analysis methods do not fully exploit the 2D-1D (x, y, E) nature of the data, as spatial information is considered separately from spectral information. Here we investigate the application of a blind source separation (BSS) algorithm that jointly exploits the spectral and spatial signatures of each component in order to disentangle them. We explore the capabilities of a new BSS method (the general morphological component analysis; GMCA), initially developed to extract an image of the cosmic microwave background from Planck data, in an X-ray context. The performance of the GMCA on X-ray data is tested using Monte-Carlo simulations of supernova remnant toy models designed to represent typical science cases. We find that the GMCA is able to separate highly entangled components in X-ray data even in high-contrast scenarios, and can extract the spectrum and map of each physical component with high accuracy. A modification of the algorithm is proposed in order to improve the spectral fidelity in the case of strongly overlapping spatial components, and we investigate a resampling method to derive realistic uncertainties associated to the results of the algorithm. Applying the modified algorithm to the deep Chandra observations of Cassiopeia A, we are able to produce detailed maps of the synchrotron emission at low energies (0.6–2.2 keV), and of the red- and blueshifted distributions of a number of elements including Si and Fe K. [ABSTRACT FROM AUTHOR]

Context. Determining the radial positions of galaxies up to a high accuracy depends on the correct identification of salient features in their spectra. Classical techniques for spectroscopic redshift estimation make use of template matching with cross-correlation. These templates are usually constructed from empirical spectra or simulations based on the modeling of local galaxies. Aims. We propose two new spectroscopic redshift estimation schemes based on new learning techniques for galaxy spectra representation, using either a dictionary learning technique for sparse representation or denoising autoencoders. We investigate how these representations impact redshift estimation. Methods. We first explored dictionary learning to obtain a sparse representation of the rest-frame galaxy spectra modeling both the continuum and line emissions. As an alternative, denoising autoencoders were considered to learn non-linear representations from rest-frame emission lines extracted from the data. In both cases, the redshift was then determined by redshifting the learnt representation and selecting the redshift that gave the lowest approximation error among the tested values. Results. These methods have been tested on realistic simulated galaxy spectra, with photometry modeled after the Large Synoptic Survey Telescope (LSST) and spectroscopy reproducing properties of the Sloan Digital Sky Survey (SDSS). They were compared to Darth Fader, a robust technique extracting line features and estimating redshift through eigentemplates cross-correlations. We show that both dictionary learning and denoising autoencoders provide improved accuracy and reliability across all signal-to-noise (S/N) regimes and galaxy types. Furthermore, the former is more robust at high noise levels; the latter is more accurate on high S/N regimes. Combining both estimators improves results at low S/N. Conclusions. The representation learning framework for spectroscopic redshift analysis introduced in this work offers high performance in feature extraction and redshift estimation, improving on a classical eigentemplates approach. This is a necessity for next-generation galaxy surveys, and we demonstrate a successful application in realistic simulated survey data. [ABSTRACT FROM AUTHOR]

Purpose: Sentinel-lymph-node (SLN) resection seems to minimize systematic axillary-lymph-node dissection (sALND) side effects in operated breast cancer patients. We explored whether SLN resection achieves similar therapeutic outcomes as sALND but with fewer side effects.Methods: A randomized, controlled, open-label trial with parallel-group design compared sALND restricted to cases with positive SLN biopsy (test arm, n = 774) versus SLN biopsy followed by sALND (control arm, n = 770). Results: The five-year overall survivals in control and test arms were 96.42 and 95.64% (P = 0.2925). The estimated difference was nearly zero (precisely, − 0.79%, one-tailed 95% confidence interval (CI) limit − 2.44%). In a multivariate Cox model, the adjusted hazard ratio in the test arm was HR 0.81 (upper 95% CI limit 1.17). Advanced age (HR 1.05 per additional year, CI [1.03-1.08]), negative progesterone receptor (HR 2.17 [1.35-3.45]), SLN metastasis (HR 1.69 [1.03-2.79]), and only one SLN identification technique (HR 4.14 [1.21-14.18]) were associated with lower survival. Patients with ≥ 1 severe side effect at 1 month in control and test arms were 173/703 = 24.6% [21.5-28.0%] and 91/693 = 13.1% [10.7-15.9%] (P < 0.001). The estimated sensitivity of SLN biopsy (control arm) was 145/178 = 81.5% [74.8-86.7%]. Conclusions: Restricting ALND to cases with positive SLN biopsy does not affect the overall survival but reduces by 11.5% [7.5-15.6%] (P < 0.001) the risk of severe short-time side effects of sALND. [ABSTRACT FROM AUTHOR]

With the increasing number of deep multi-wavelength galaxy surveys, the spectral energy distribution (SED) of galaxies has become an invaluable tool for studying the formation of their structures and their evolution. In this context, standard analysis relies on simple spectro-photometric selection criteria based on a few SED colors. If this fully supervised classification already yielded clear achievements, it is not optimal to extract relevant information from the data. In this article, we propose to employ very recent advances in machine learning, and more precisely in feature learning, to derive a data-driven diagram. We show that the proposed approach based on denoising autoencoders recovers the bi-modality in the galaxy population in an unsupervised manner, without using any prior knowledge on galaxy SED classification. This technique has been compared to principal component analysis (PCA) and to standard color/color representations. In addition, preliminary results illustrate that this enables the capturing of extra physically meaningful information, such as redshift dependence, galaxy mass evolution and variation over the specific star formation rate. PCA also results in an unsupervised representation with physical properties, such as mass and sSFR, although this representation separates out less other characteristics (bimodality, redshift evolution) than denoising autoencoders. [ABSTRACT FROM AUTHOR]

We describe a new estimate of the cosmic microwave background (CMB) intensity map reconstructed by a joint analysis of the full Planck 2015 data (PR2) and nine years of WMAP data. The proposed map provides more than a mere update of the CMB map introduced in a previous paper since it benefits from an improvement of the component separation method L-GMCA (Local-Generalized Morphological Component Analysis), which facilitates efficient separation of correlated components. Based on the most recent CMB data, we further confirm previous results showing that the proposed CMB map estimate exhibits appealing characteristics for astrophysical and cosmological applications: i) it is a full-sky map as it did not require any inpainting or interpolation postprocessing; ii) foreground contamination is very low even on the galactic center; and iii) the map does not exhibit any detectable trace of thermal Sunyaev-Zel’dovich contamination. We show that its power spectrum is in good agreement with the Planck PR2 official theoretical best-fit power spectrum. Finally, following the principle of reproducible research, we provide the codes to reproduce the L-GMCA, which makes it the only reproducible CMB map. [ABSTRACT FROM AUTHOR]

Planck data when combined with ancillary data provide a unique opportunity to separate the diffuse emission components of the inner Galaxy. The purpose of the paper is to elucidate the morphology of the various emission components in the strong star-formation region lying inside the solar radius and to clarify the relationship between the various components. The region of the Galactic plane covered is l = 300° ! 0° ! 60° where star-formation is highest and the emission is strong enough to make meaningful component separation. The latitude widths in this longitude range lie between 1° and 2°, which correspond to FWHM z-widths of 100-200 pc at a typical distance of 6 kpc. The four emission components studied here are synchrotron, free-free, anomalous microwave emission (AME), and thermal (vibrational) dust emission. These components are identified by constructing spectral energy distributions (SEDs) at positions along the Galactic plane using the wide frequency coverage of Planck (28.4-857 GHz) in combination with low-frequency radio data at 0.408-2.3 GHz plus WMAP data at 23-94 GHz, along with far-infrared (FIR) data from COBE-DIRBE and IRAS. The free-free component is determined from radio recombination line (RRL) data. AME is found to be comparable in brightness to the free-free emission on the Galactic plane in the frequency range 20-40 GHz with a width in latitude similar to that of the thermal dust; it comprises 45 ± 1% of the total 28.4 GHz emission in the longitude range l = 300° ! 0° ! 60°. The free-free component is the narrowest, reflecting the fact that it is produced by current star-formation as traced by the narrow distribution of OB stars. It is the dominant emission on the plane between 60 and 100 GHz. RRLs from this ionized gas are used to assess its distance, leading to a free-free z-width of FWHM ≈ 100 pc. The narrow synchrotron component has a low-frequency brightness spectral index βsynch -2:7 that is similar to the broad synchrotron component indicating that they are both populated by the cosmic ray electrons of the same spectral index. The width of this narrow synchrotron component is significantly larger than that of the other three components, suggesting that it is generated in an assembly of older supernova remnants that have expanded to sizes of order 150 pc in 3×105 yr; pulsars of a similar age have a similar spread in latitude. The thermal dust is identified in the SEDs with average parameters of Tdust = 20:4 ± 0:4 K, βFIR = 1:94 ± 0:03 (>353 GHz), and βmm = 1:67 ± 0:02 (<353 GHz). The latitude distributions of gamma-rays, CO, and the emission in high-frequency Planck bands have similar widths, showing that they are all indicators of the total gaseous matter on the plane in the inner Galaxy. [ABSTRACT FROM AUTHOR]

Aims. The primordial power spectrum describes the initial perturbations that seeded the large-scale structure we observe today. It provides an indirect probe of inflation or other structure-formation mechanisms. In this Letter, we recover the primordial power spectrum from the Planck PR1 dataset, using our recently published algorithm PRISM. Methods. PRISM is a sparsity-based inversion method that aims at recovering features in the primordial power spectrum from the empirical power spectrum of the cosmic microwave background (CMB). This ill-posed inverse problem is regularised using a sparsity prior on features in the primordial power spectrum in a wavelet dictionary. Although this non-parametric method does not assume a strong prior on the shape of the primordial power spectrum, it is able to recover both its general shape and localised features. As a results, this approach presents a reliable way of detecting deviations from the currently favoured scale-invariant spectrum. Results. We applied PRISM to 100 simulated Planck data to investigate its performance on Planck-like data.We then applied PRISM to the Planck PR1 power spectrum to recover the primordial power spectrum.We also tested the algorithm's ability to recover a small localised feature at k ∼ 0:125 Mpc-1, which caused a large dip at ࡁ ∼ 1800 in the angular power spectrum. Conclusions. We find no significant departures from the fiducial Planck PR1 near scale-invariant primordial power spectrum with As = 2:215 x 10-9 and ns = 0:9624. [ABSTRACT FROM AUTHOR]

The dust-Hi correlation is used to characterize the emission properties of dust in the diffuse interstellar medium (ISM) from far infrared wavelengths to microwave frequencies. The field of this investigation encompasses the part of the southern sky best suited to study the cosmic infrared and microwave backgrounds. We cross-correlate sky maps from Planck, the Wilkinson Microwave Anisotropy Probe (WMAP), and the diffuse infrared background experiment (DIRBE), at 17 frequencies from 23 to 3000 GHz, with the Parkes survey of the 21 cm line emission of neutral atomic hydrogen, over a contiguous area of 7500 deg2 centred on the southern Galactic pole. We present a general methodology to study the dust-Hi correlation over the sky, including simulations to quantify uncertainties. Our analysis yields four specific results. (1) We map the temperature, submillimetre emissivity, and opacity of the dust per H-atom. The dust temperature is observed to be anti-correlated with the dust emissivity and opacity. We interpret this result as evidence of dust evolution within the diffuse ISM. The mean dust opacity is measured to be (7.1 ± 0.6) × 10-27 cm2 H-1 × (ν/ 353 GHz)1.53 ± 0.03 for 100 ≤ ν ≤ 353 GHz. This is a reference value to estimate hydrogen column densities from dust emission at submillimetre and millimetre wavelengths. (2) We map the spectral index βmm of dust emission at millimetre wavelengths (defined here as ν ≤ 353 GHz), and find it to be remarkably constant at βmm = 1.51 ± 0.13. We compare it with the far infrared spectral index βFIR derived from greybody fits at higher frequencies, and find a systematic difference, βmm − βFIR = − 0.15, which suggests that the dust spectral energy distribution (SED) flattens at ν ≤ 353 GHz. (3) We present spectral fits of the microwave emission correlated with Hi from 23 to 353 GHz, which separate dust and anomalous microwave emission (AME). We show that the flattening of the dust SED can be accounted for with an additional component with a blackbody spectrum. This additional component, which accounts for (26 ± 6)% of the dust emission at 100 GHz, could represent magnetic dipole emission. Alternatively, it could account for an increasing contribution of carbon dust, or a flattening of the emissivity of amorphous silicates, at millimetre wavelengths. These interpretations make different predictions for the dust polarization SED. (4) We analyse the residuals of the dust-Hi correlation. We identify a Galactic contribution to these residuals, which we model with variations of the dust emissivity on angular scales smaller than that of our correlation analysis. This model of the residuals is used to quantify uncertainties of the CIB power spectrum in a companion Planck paper. [ABSTRACT FROM AUTHOR]

We explore the 2013 Planck likelihood function with a high-precision multi-dimensional minimizer (Minuit). This allows a refinement of the ΛCDM best-fit solution with respect to previously-released results, and the construction of frequentist confidence intervals using profile likelihoods. The agreement with the cosmological results from the Bayesian framework is excellent, demonstrating the robustness of the Planck results to the statistical methodology. We investigate the inclusion of neutrino masses, where more significant differences may appear due to the non-Gaussian nature of the posterior mass distribution. By applying the Feldman-Cousins prescription, we again obtain results very similar to those of the Bayesian methodology. However, the profile-likelihood analysis of the cosmic microwave background (CMB) combination (Planck+WP+highL) reveals a minimum well within the unphysical negative-mass region. We show that inclusion of the Planck CMB-lensing information regularizes this issue, and provide a robust frequentist upper limit ∑ mν ≤ 0.26 eV (95% confidence) from the CMB+lensing+BAO data combination. [ABSTRACT FROM AUTHOR]

Missions such as WMAP or Planck measure full-sky fluctuations of the cosmic microwave background and foregrounds, among which bright compact source emissions cover a significant fraction of the sky. To accurately estimate the diffuse components, the point-source emissions need to be separated from the data, which requires a dedicated processing. We propose a new technique to estimate the flux of the brightest point sources using a morphological separation approach: point sources with known support and shape are separated from diffuse emissions that are assumed to be sparse in the spherical harmonic domain. This approach is compared on both WMAP simulations and data with the standard local ?2 minimization, modelling the background as a low-order polynomial. The proposed approach generally leads to 1) lower biases in flux recovery; 2) an improved root mean-square error of up to 35%; and 3) more robustness to background fluctuations at the scale of the source. The WMAP 9-year point-source-subtracted maps are available online. [ABSTRACT FROM AUTHOR]

Aims. The primordial power spectrum describes the initial perturbations in the Universe which eventually grew into the large-scale structure we observe today, and thereby provides an indirect probe of inflation or other structure-formation mechanisms. Here, we introduce a new method to estimate this spectrum from the empirical power spectrum of cosmic microwave background maps. Methods. A sparsity-based linear inversion method, named PRISM, is presented. This technique leverages a sparsity prior on features in the primordial power spectrum in a wavelet basis to regularise the inverse problem. This non-parametric approach does not assume a strong prior on the shape of the primordial power spectrum, yet is able to correctly reconstruct its global shape as well as localised features. These advantages make this method robust for detecting deviations from the currently favoured scale-invariant spectrum. Results. We investigate the strength of this method on a set of WMAP nine-year simulated data for three types of primordial power spectra: a near scale-invariant spectrum, a spectrum with a small running of the spectral index, and a spectrum with a localised feature. This technique proves that it can easily detect deviations from a pure scale-invariant power spectrum and is suitable for distinguishing between simple models of the inflation. We process the WMAP nine-year data and find no significant departure from a near scale-invariant power spectrum with the spectral index ns = 0.972. Conclusions. A high-resolution primordial power spectrum can be reconstructed with this technique, where any strong local deviations or small global deviations from a pure scale-invariant spectrum can easily be detected. [ABSTRACT FROM AUTHOR]

Anomalous microwave emission (AME) is believed to be due to electric dipole radiation from small spinning dust grains. The aim of this paper is a statistical study of the basic properties of AME regions and the environment in which they emit. We used WMAP and Planck maps, combined with ancillary radio and IR data, to construct a sample of 98 candidate AME sources, assembling SEDs for each source using aperture photometry on 1◦-smoothed maps from 0.408 GHz up to 3000 GHz. Each spectrum is fitted with a simple model of free-free, synchrotron (where necessary), cosmic microwave background (CMB), thermal dust, and spinning dust components. We find that 42 of the 98 sources have significant (>5σ) excess emission at frequencies between 20 and 60 GHz. An analysis of the potential contribution of optically thick free-free emission from ultracompact Hii regions, using IR colour criteria, reduces the significant AME sample to 27 regions. The spectrum of the AME is consistent with model spectra of spinning dust. Peak frequencies are in the range 20−35 GHz except for the California nebula (NGC1499), which appears to have a high spinning dust peak frequency of (50 ± 17) GHz. The AME regions tend to be more spatially extended than regions with little or no AME. The AME intensity is strongly correlated with the sub-millimetre/IR flux densities and comparable to previous AME detections in the literature. AME emissivity, defined as the ratio of AME to dust optical depth, varies by an order of magnitude for the AME regions. The AME regions tend to be associated with cooler dust in the range 14−20K and an average emissivity index, βd, of +1.8, while the non-AME regions are typically warmer, at 20−27 K. In agreement with previous studies, the AME emissivity appears to decrease with increasing column density. This supports the idea of AME originating from small grains that are known to be depleted in dense regions, probably due to coagulation onto larger grains. We also find a correlation between the AME emissivity (and to a lesser degree the spinning dust peak frequency) and the intensity of the interstellar radiation field, G0. Modelling of this trend suggests that both radiative and collisional excitation are important for the spinning dust emission. The most significant AME regions tend to have relatively less ionized gas (free-free emission), although this could be a selection effect. The infrared excess, a measure of the heating of dust associated with Hii regions, is typically >4 for AME sources, indicating that the dust is not primarily heated by hot OB stars. The AME regions are associated with known dark nebulae and have higher 12 μm/25 μm ratios. The emerging picture is that the bulk of the AME is coming from the polycyclic aromatic hydrocarbons and small dust grains from the colder neutral interstellar medium phase. [ABSTRACT FROM AUTHOR]

The primordial power spectrum is an indirect probe of inflation or other structure-formation mechanisms. We introduce a new method, named PRISM, to estimate this spectrum from the empirical cosmic microwave background (CMB) power spectrum. This is a sparsity-based inversion method, which leverages a sparsity prior on features in the primordial spectrum in a wavelet dictionary to regularise the inverse problem. This non-parametric approach is able to reconstruct the global shape as well as localised features of the primordial spectrum accurately and proves to be robust for detecting deviations from the currently favoured scale-invariant spectrum. We investigate the strength of this method on a set of WMAP nine-year simulated data for three types of primordial spectra and then process the WMAP nine-year data as well as the Planck PR1 data. We find no significant departures from a near scale-invariant spectrum. [ABSTRACT FROM PUBLISHER]

We use Planck HFI data combined with ancillary radio data to study the emissivity index of the interstellar dust emission in the frequency range 100-353 GHz, or 3-0.8 mm, in the Galactic plane. We analyse the region l = 20°-44° and ∣b∣ ⩽ 4° where the free-free emission can be estimated from radio recombination line data. We fit the spectra at each sky pixel with a modified blackbody model and two opacity spectral indices, βmm and βFIR, below and above 353 GHz, respectively. We find that βmm is smaller than βFIR, and we detect a correlation between this low frequency power-law index and the dust optical depth at 353 GHz, τ353. The opacity spectral index βmm increases from about 1.54 in the more diffuse regions of the Galactic disk, ∣b∣ = 3°-4° and τ353 ∼ 5 × 10-5, to about 1.66 in the densest regions with an optical depth of more than one order of magnitude higher. We associate this correlation with an evolution of the dust emissivity related to the fraction of molecular gas along the line of sight. This translates into βmm ∼ 1.54 for a medium that is mostly atomic and βmm ∼ 1.66 when the medium is dominated by molecular gas. We find that both the two-level system model and magnetic dipole emission by ferromagnetic particles can explain the results. These results improve our understanding of the physics of interstellar dust and lead towards a complete model of the dust spectrum of the Milky Way from far-infrared to millimetre wavelengths. [ABSTRACT FROM AUTHOR]

We present a novel estimate of the cosmological microwave background (CMB) map by combining the two latest full-sky microwave surveys: WMAP nine-year and Planck PR1. The joint processing benefits from a recently introduced component separation method coined "local-generalized morphological component analysis" (LGMCA) and based on the sparse distribution of the foregrounds in the wavelet domain. The proposed estimation procedure takes advantage of the IRIS 100 μm as an extra observation on the galactic center for enhanced dust removal. We show that this new CMB map presents several interesting aspects: i) it is a full sky map without using any inpainting or interpolating method; ii) foreground contamination is very low; iii) the Galactic center is very clean with especially low dust contamination as measured by the cross-correlation between the estimated CMB map and the IRIS 100 μm map; and iv) it is free of thermal SZ contamination. [ABSTRACT FROM AUTHOR]

The accurate and precise removal of 21-cm foregrounds from Epoch of Reionization (EoR) redshifted 21-cm emission data is essential if we are to gain insight into an unexplored cosmological era. We apply a non-parametric technique, Generalized Morphological Component Analysis (gmca), to simulated Low Frequency Array (LOFAR)-EoR data and show that it has the ability to clean the foregrounds with high accuracy. We recover the 21-cm 1D, 2D and 3D power spectra with high accuracy across an impressive range of frequencies and scales. We show that gmca preserves the 21-cm phase information, especially when the smallest spatial scale data is discarded. While it has been shown that LOFAR-EoR image recovery is theoretically possible using image smoothing, we add that wavelet decomposition is an efficient way of recovering 21-cm signal maps to the same or greater order of accuracy with more flexibility. By comparing the gmca output residual maps (equal to the noise, 21-cm signal and any foreground fitting errors) with the 21-cm maps at one frequency and discarding the smaller wavelet scale information, we find a correlation coefficient of 0.689, compared to 0.588 for the equivalently smoothed image. Considering only the pixels in a central patch covering 50 per cent of the total map area, these coefficients improve to 0.905 and 0.605, respectively, and we conclude that wavelet decomposition is a significantly more powerful method to denoise reconstructed 21-cm maps than smoothing. [ABSTRACT FROM AUTHOR]

The cosmic microwave background (CMB) is of premier importance for cosmologists in studying the birth of our universe. Unfortunately, most CMB experiments, such as COBE, WMAP, or Planck do not directly measure the cosmological signal, because the CMB is mixed up with galactic foregrounds and point sources. For the sake of scientific exploitation, measuring the CMB requires extracting several different astrophysical components (CMB, Sunyaev-Zel'dovich clusters, galactic dust) from multiwavelength observations. Mathematically speaking, the problem of disentangling the CMB map from the galactic foregrounds amounts to a component or source separation problem. In the field of CMB studies, a wide range of source separation methods have been applied that all differ in the way they model the data and in the criteria they rely on to separate components. Two main difficulties are i) that the instrument's beam varies across frequencies and ii) that the emission laws of most astrophysical components vary across pixels. This paper aims at introducing a very accurate modeling of CMB data, based on sparsity to account for beams' variability across frequencies, as well as for spatial variations of the components' spectral characteristics. Based on this new sparse modeling of the data, a sparsity-based component separation method coined local-generalized morphological component analysis (L-GMCA) is described. Extensive numerical experiments have been carried out with simulated Planck data. These experiments show the high efficiency of the proposed component separation methods for estimating a clean CMB map with a very low foreground contamination, which makes L-GMCA of prime interest for CMB studies. [ABSTRACT FROM AUTHOR]

Taking advantage of the all-sky coverage and broad frequency range of the Planck satellite, we study the Sunyaev-Zeldovich (SZ) and pressure profiles of 62 nearby massive clusters detected at high significance in the 14-month nominal survey. Careful reconstruction of the SZ signal indicates that most clusters are individually detected at least out to R500. By stacking the radial profiles, we have statistically detected the radial SZ signal out to 3 × R500, i.e., at a density contrast of about 50-100, though the dispersion about the mean profile dominates the statistical errors across the whole radial range. Our measurement is fully consistent with previous Planck results on integrated SZ fluxes, further strengthening the agreement between SZ and X-ray measurements inside R500. Correcting for the effects of the Planck beam, we have calculated the corresponding pressure profiles. This new constraint from SZ measurements is consistent with the X-ray constraints from XMM-Newton in the region in which the profiles overlap (i.e., [0.1-1] R500), and is in fairly good agreement with theoretical predictions within the expected dispersion. At larger radii the average pressure profile is slightly flatter than most predictions from numerical simulations. Combining the SZ and X-ray observed profiles into a joint fit to a generalised pressure profile gives best-fit parameters [P0, c500, γ, α, β] = [6.41, 1.81, 0.31, 1.33, 4.13]. Using a reasonable hypothesis for the gas temperature in the cluster outskirts we reconstruct from our stacked pressure profile the gas mass fraction profile out to 3 R500. Within the temperature driven uncertainties, our Planck constraints are compatible with the cosmic baryon fraction and expected gas fraction in halos. [ABSTRACT FROM AUTHOR]

We evaluate the construction methodology of an all-sky catalogue of galaxy clusters detected through the Sunyaev-Zel'dovich (SZ) effect. We perform an extensive comparison of twelve algorithms applied to the same detailed simulations of the millimeter and submillimeter sky based on a Planck-like case. We present the results of this "SZ Challenge" in terms of catalogue completeness, purity, astrometric and photometric reconstruction. Our results provide a comparison of a representative sample of SZ detection algorithms and highlight important issues in their application. In our study case, we show that the exact expected number of clusters remains uncertain (about a thousand cluster candidates at ∣b∣ > 20 deg with 90% purity) and that it depends on the SZ model and on the detailed sky simulations, and on algorithmic implementation of the detection methods. We also estimate the astrometric precision of the cluster candidates which is found of the order of ∼2 arcmin on average, and the photometric uncertainty of about 30%, depending on flux. [ABSTRACT FROM AUTHOR]

Estimating the cosmological microwave background is of utmost importance for cosmology. However, its estimation from full-sky surveys such as WMAP or more recently Planck is challenging: CMB maps are generally estimated via the application of some source separation techniques which never prevent the final map from being contaminated with noise and foreground residuals. These spurious contaminations whether noise or foreground residuals are well known to be a plague for most cosmologically relevant tests or evaluations; this includes CMB lensing reconstruction or non-Gaussian signatures search. Noise reduction is generally performed by applying a simple Wiener filter in spherical harmonics; however, this does not account for the nonstationarity of the noise. Foreground contamination is usually tackled by masking the most intense residuals detected in the map, which makes CMB evaluation harder to perform. In this paper, we introduce a novel noise reduction framework coined LIW-Filtering for Linear Iterative Wavelet Filtering which is able to account for the noise spatial variability thanks to a wavelet-based modeling while keeping the highly desired linearity of the Wiener filter. We further show that the same filtering technique can effectively perform foreground contamination reduction thus providing a globally cleaner CMB map. Numerical results on simulated Planck data are provided. [ABSTRACT FROM AUTHOR]

Background: This study evaluates the relation of the early oestrogen-regulated gene gabarapl1 to cellular growth and its prognostic significance in breast adenocarcinoma.Methods: First, the relation between GABARAPL1 expression and MCF-7 growth rate was analysed. Thereafter, by performing macroarray and reverse transcriptase quantitative-polymerase chain reaction (RT-qPCR) experiments, gabarapl1 expression was quantified in several histological breast tumour types and in a retrospective cohort of 265 breast cancers.Results: GABARAPL1 overexpression inhibited MCF-7 growth rate and gabarapl1 expression was downregulated in breast tumours. Gabarapl1 mRNA levels were found to be significantly lower in tumours presenting a high histological grade, with a lymph node-positive (pN+) and oestrogen and/or progesterone receptor-negative status. In univariate analysis, high gabarapl1 levels were associated with a lower risk of metastasis in all patients (hazard ratio (HR) 4.96), as well as in pN+ patients (HR 14.96). In multivariate analysis, gabarapl1 expression remained significant in all patients (HR 3.63), as well as in pN+ patients (HR 5.65). In univariate or multivariate analysis, gabarapl1 expression did not disclose any difference in metastasis risk in lymph node-negative patients.Conclusions: Our data show for the first time that the level of gabarapl1 mRNA expression in breast tumours is a good indicator of the risk of recurrence, specifically in pN+ patients. [ABSTRACT FROM AUTHOR]

Recent advances in signal processing have focused on the use of sparse representations in various applications. A new field of interest based on sparsity has recently emerged: compressed sensing. This theory is a new sampling framework that provides an alternative to the well-known Shannon sampling theory. In this paper, we investigate how compressed sensing (CS) can provide new insights into astronomical data compression. We first give a brief overview of the compressed sensing theory which provides very simple coding process with low computational cost, thus favoring its use for real-time applications often found onboard space mission. In practical situations, owing to particular observation strategies (for instance, raster scans) astronomical data are often redundant; in that context, we point out that a CS-based compression scheme is flexible enough to account for particular observational strategies. Indeed, we show also that CS provides a new fantastic way to handle multiple observations of the same field view, allowing us to recover low level details, which is impossible with standard compression methods. This kind of CS data fusion concept could lead to an elegant and effective way to solve the problem ESA is faced with, for the transmission to the earth of the data collected by PACS, one of the instruments onboard the Herschel spacecraft which will launched in late 2008/early 2009. We show that CS enables to recover data with a spatial resolution enhanced up to 30% with similar sensitivity compared to the averaging technique proposed by ESA. [ABSTRACT FROM PUBLISHER]

Abstract: Aims: The aim of this study was to determine, from a series of cases, the frequency and prognostic factors of invasion of non-sentinel lymph nodes when the axillary sentinel lymph node contains a metastasis ≤2mm, and thereby select a population in which completion axillary dissection could be omitted. Methods: Between July 1996 and July 2003, 62 patients, which axillary sentinel lymph node contained a metastasis ≤2mm had an evaluation of the axillary non-sentinel lymph nodes. Eleven patients had also an evaluation of internal mammary lymph nodes. Results: Eleven patients had axillary non-sentinel lymph node invasion: six by metastases ≤2mm and five by macrometastases. When internal mammary lymph nodes were also concerned, nodal invasion apart from the axillary sentinel lymph node was seen in 14 patients. Vascular lymphatic invasion was the only factor, statistically significant, linked to non-sentinel lymph node invasion (p=0.02). Conclusion: Whatever the size or method of histological detection (pN1mi or pN0(i+)), the presence of a metastasis ≤2mm in the axillary sentinel lymph node leads us to carry out completion axillary dissection to optimize staging and loco-regional control of the disease. [Copyright &y& Elsevier]