Predicted Planck Extragalactic Point Source Catalogue

An estimation of the number and amplitude (in flux) of the extragalactic point sources that will be observed by the Planck Mission is presented in this paper. The study is based on the Mexican Hat wavelet formalism introduced by Cayon et al. 2000. Simulations at Planck observing frequencies are analysed, taking into account all the possible cosmological, Galactic and Extragalactic emissions together with noise. With the technique used in this work the Planck Mission will produce a catalogue of extragalactic point sources above fluxes: 1.03 Jy (857 GHz), 0.53 Jy (545 GHz), 0.28 Jy (353 GHz), 0.24 Jy (217 GHz), 0.32 Jy (143 GHz), 0.41 Jy (100 GHz HFI), 0.34 Jy (100 GHz LFI), 0.57 Jy (70 GHz), 0.54 Jy (44 GHz) and 0.54 Jy (30 GHz), which are only slightly model dependent (see text). Amplitudes of these sources are estimated with errors below 15%. Moreover, we also provide a complete catalogue (for the point sources simulation analysed) with errors in the estimation of the amplitude below 10%. In addition we discuss the possibility of identifying different point source populations in the Planck catalogue by estimating their spectral indices.Comment: 13 pages, 2 figures, submitted to MNRA

Partially gapped fermions in 2D

We compute mean field phase diagrams of two closely related interacting fermion models in two spatial dimensions (2D). The first is the so-called 2D t-t'-V model describing spinless fermions on a square lattice with local hopping and density-density interactions. The second is the so-called 2D Luttinger model that provides an effective description of the 2D t-t'-V model and in which parts of the fermion degrees of freedom are treated exactly by bosonization. In mean field theory, both models have a charge-density-wave (CDW) instability making them gapped at half-filling. The 2D t-t'-V model has a significant parameter regime away from half-filling where neither the CDW nor the normal state are thermodynamically stable. We show that the 2D Luttinger model allows to obtain more detailed information about this mixed region. In particular, we find in the 2D Luttinger model a partially gapped phase that, as we argue, can be described by an exactly solvable model.Comment: v1: 36 pages, 10 figures, v2: minor corrections; equation references to arXiv:0903.0055 updated

Deep learning-based breast region segmentation in raw and processed digital mammograms:generalization across views and vendors

Purpose: We developed a segmentation method suited for both raw (for processing) and processed (for presentation) digital mammograms (DMs) that is designed to generalize across images acquired with systems from different vendors and across the two standard screening views. Approach: A U-Net was trained to segment mammograms into background, breast, and pectoral muscle. Eight different datasets, including two previously published public sets and six sets of DMs from as many different vendors, were used, totaling 322 screen film mammograms (SFMs) and 4251 DMs (2821 raw/processed pairs and 1430 only processed) from 1077 different women. Three experiments were done: first training on all SFM and processed images, second also including all raw images in training, and finally testing vendor generalization by leaving one dataset out at a time. Results: The model trained on SFM and processed mammograms achieved a good overall performance regardless of projection and vendor, with a mean (±std. dev.) dice score of 0.96 0.06 for all datasets combined. When raw images were included in training, the mean (±std. dev.) dice score for the raw images was 0.95 0.05 and for the processed images was 0.96 0.04. Testing on a dataset with processed DMs from a vendor that was excluded from training resulted in a difference in mean dice varying between −0.23 to þ0.02 from that of the fully trained model. Conclusions: The proposed segmentation method yields accurate overall segmentation results for both raw and processed mammograms independent of view and vendor. The code and model weights are made available.</p

MAIA, un modèle de données support de la démarche d'adaptation

To face climate change, mid-mountain territorial communities set up sectoral adaptations. This article presents a model of capitalization and linking of these adaptation practices (already tested or to be considered). This model makes it possible to move from a sectoral to an integrated and intelligent adaptation, aware of implications and synergies between several sectors of activities over a given territory. The model's concepts are presented in UML (Unified Modeling Language), a graphical modeling language used to design computer systems. From this, objective would be to use these concepts to build a web application dedicated to climate change adaptation practices, which could support governance of territorial communities. Conducted within the framework of the GICC-ONERC AdaMont (2015-2017) project, this operational aim may justify the setting up of new scientific and technical partnerships.Face au changement climatique, les communautés territoriales de moyenne montagne mettent en place, de façon sectorielle, des adaptations. Cet article présente un modèle de capitalisation et de mise en relation de ces pratiques d'adaptation (éprouvées ou envisagées). Celui-ci permet de passer d'une adaptation métier, sectorielle, à une adaptation intégrée, intelligente, consciente des implications et synergies entre les secteurs d'activités présents sur un territoire. Les concepts qui structurent le modèle sont présentés en UML (Unified Modeling Language), un langage de modélisation informatique graphique servant de support à la conception de systèmes informatisés. L'ambition, à termes, est de développer ces concepts jusqu'à la réalisation d'une application web destinée à la gouvernance des communautés territoriales qui traite des pratiques d'adaptation au changement climatique. Mené dans le cadre du projet GICC-ONERC AdaMont (2015-2017), cette visée opérationnelle pourra justifier la mise en place de nouveaux partenariats scientifiques et techniques

Exact solution of a 2D interacting fermion model

We study an exactly solvable quantum field theory (QFT) model describing interacting fermions in 2+1 dimensions. This model is motivated by physical arguments suggesting that it provides an effective description of spinless fermions on a square lattice with local hopping and density-density interactions if, close to half filling, the system develops a partial energy gap. The necessary regularization of the QFT model is based on this proposed relation to lattice fermions. We use bosonization methods to diagonalize the Hamiltonian and to compute all correlation functions. We also discuss how, after appropriate multiplicative renormalizations, all short- and long distance cutoffs can be removed. In particular, we prove that the renormalized two-point functions have algebraic decay with non-trivial exponents depending on the interaction strengths, which is a hallmark of Luttinger-liquid behavior.Comment: 59 pages, 3 figures, v2: further references added; additional subsections elaborating mathematical details; additional appendix with details on the relation to lattice fermion

Stearoyl-CoA desaturase-1 impairs the reparative properties of macrophages and microglia in the brain

Failure of remyelination underlies the progressive nature of demyelinating diseases such as multiple sclerosis. Macrophages and microglia are crucially involved in the formation and repair of demyelinated lesions. Here we show that myelin uptake temporarily skewed these phagocytes toward a disease-resolving phenotype, while sustained intracellular accumulation of myelin induced a lesion-promoting phenotype. This phenotypic shift was controlled by stearoyl-CoA desaturase-1 (SCD1), an enzyme responsible for the desaturation of saturated fatty acids. Monounsaturated fatty acids generated by SCD1 reduced the surface abundance of the cholesterol efflux transporter ABCA1, which in turn promoted lipid accumulation and induced an inflammatory phagocyte phenotype. Pharmacological inhibition or phagocyte-specific deficiency of Scd1 accelerated remyelination ex vivo and in vivo. These findings identify SCD1 as a novel therapeutic target to promote remyelination