De 20 000 à 18 000 BP en Quercy : apports de la séquence du Cuzoul de Vers à la compréhension de l'évolution des comportements socio-économiques entre Solutréen récent et Badegoulien

Essai de synthèse des travaux menés autour du gisement du Cuzoul de Vers (Lot)

Thermal Stability of the Human Immunodeficiency Virus Type 1 (HIV-1) Receptors, CD4 and CXCR4, Reconstituted in Proteoliposomes

BACKGROUND: The entry of human immunodeficiency virus (HIV-1) into host cells involves the interaction of the viral exterior envelope glycoprotein, gp120, and receptors on the target cell. The HIV-1 receptors are CD4 and one of two chemokine receptors, CCR5 or CXCR4. METHODOLOGY/PRINCIPAL FINDINGS: We created proteoliposomes that contain CD4, the primary HIV-1 receptor, and one of the coreceptors, CXCR4. Antibodies against CD4 and CXCR4 specifically bound the proteoliposomes. CXCL12, the natural ligand for CXCR4, and the small-molecule CXCR4 antagonist, AMD3100, bound the proteoliposomes with affinities close to those associated with the binding of these molecules to cells expressing CXCR4 and CD4. The HIV-1 gp120 exterior envelope glycoprotein bound tightly to proteoliposomes expressing only CD4 and, in the presence of soluble CD4, bound weakly to proteoliposomes expressing only CXCR4. The thermal stability of CD4 and CXCR4 inserted into liposomes was examined. Thermal denaturation of CXCR4 followed second-order kinetics, with an activation energy (E(a)) of 269 kJ/mol (64.3 kcal/mol) and an inactivation temperature (T(i)) of 56°C. Thermal inactivation of CD4 exhibited a reaction order of 1.3, an E(a) of 278 kJ/mol (66.5 kcal/mol), and a T(i) of 52.2°C. The second-order denaturation kinetics of CXCR4 is unusual among G protein-coupled receptors, and may result from dimeric interactions between CXCR4 molecules. CONCLUSIONS/SIGNIFICANCE: Our studies with proteoliposomes containing the native HIV-1 receptors allowed an examination of the binding of biologically important ligands and revealed the higher-order denaturation kinetics of these receptors. CD4/CXCR4-proteoliposomes may be useful for the study of virus-target cell interactions and for the identification of inhibitors

180 Hépatectomies droites avec hémi-clampage pédiculaire

LILLE2-BU Santé-Recherche (593502101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Ordinal Non-negative Matrix Factorization for Recommendation

International audienceWe introduce a new non-negative matrix factorization (NMF) method for ordinal data, called OrdNMF. Ordinal data are categorical data which exhibit a natural ordering between the categories. In particular, they can be found in recommender systems, either with explicit data (such as ratings) or implicit data (such as quantized play counts). OrdNMF is a probabilistic latent factor model that generalizes Bernoulli-Poisson factorization (Be-PoF) and Poisson factorization (PF) applied to binarized data. Contrary to these methods, Ord-NMF circumvents binarization and can exploit a more informative representation of the data. We design an efficient variational algorithm based on a suitable model augmentation and related to variational PF. In particular, our algorithm preserves the scalability of PF and can be applied to huge sparse datasets. We report recommendation experiments on explicit and implicit datasets, and show that OrdNMF outperforms BePoF and PF applied to binarized data

Matrix co-factorization for cold-start recommendation

International audienceSong recommendation from listening counts is now a classical problem, addressed by different kinds of collaborative filtering (CF) techniques. Among them, Poisson matrix factorization (PMF) has raised a lot of interest, since it seems well-suited to the implicit data provided by listening counts. Additionally, it has proven to achieve state-of-the-art performance while being scalable to big data. Yet, CF suffers from a critical issue, usually called cold-start problem: the system cannot recommend new songs, i.e., songs which have never been listened to. To alleviate this, one should complement the listening counts with another modality. This paper proposes a multi-modal extension of PMF applied to listening counts and tag labels extracted from the Million Song Dataset. In our model, every song is represented by the same activation pattern in each modality but with possibly different scales. As such, the method is not prone to the cold-start problem, i.e., it can learn from a single modality when the other one is not informative. Our model is symmetric (it equally uses both modalities) and we evaluate it on two tasks: new songs recommendation and tag labeling

Recommendation from raw data with adaptive compound Poisson factorization

International audienceCount data are often used in recommender sys-tems: they are widespread (song play counts,product purchases, clicks on web pages) andcan reveal user preference without any explicitrating from the user. Such data are known to besparse, over-dispersed and bursty, which makestheir direct use in recommender systems chal-lenging, often leading to pre-processing stepssuch as binarization. The aim of this paper isto build recommender systems from these rawdata, by means of the recently proposed com-pound Poisson Factorization (cPF). The papercontributions are three-fold: we present a uni-fied framework for discrete data (dcPF), lead-ing to an adaptive and scalable algorithm; weshow that our framework achieves a trade-offbetween Poisson Factorization (PF) applied toraw and binarized data; we study four specificinstances that are relevant to recommendationand exhibit new links with combinatorics. Ex-periments with three different datasets showthat dcPF is able to effectively adjust to over-dispersion, leading to better recommendationscores when compared with PF on either rawor binarized data

Negative Binomial Matrix Factorization

International audienceWe introduce negative binomial matrix factoriza-tion (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an extension of Poisson factorization (PF) perturbed by a multiplicative term which models exposure. This term brings a degree of freedom for controlling the dispersion, making NBMF more robust to outliers. We describe a majorization-minimization (MM) algorithm for a maximum likelihood estimation of the parameters. We provide results on a recommendation task and demonstrate the ability of NBMF to efficiently exploit raw data

Fusion d'images multispectrales et hyperspectrales pour l'observation en astronomie infrarouge

National audienceDans cet article, nous présentons une méthode de fusion d’images multispectrales et hyperspectrales dans un contexte d’observation astrophysique. Nous formulons un modèle direct d’observation et résolvons un problème inverse régularisé par un algorithme de type descente de gradient. Le modèle de fusion est testé sur des données simulées d’observations de la barre d’Orion par l’imageur NIRCam et le spectromètre NIRSpec, embarqués à bord du télescope spatial James Webb. Notre méthode de fusion montre une bonne reconstruction spatiale et spectrale de l’objet observé

Informed spatial regularizations for fast fusion of astronomical images

International audienceThis paper introduces two informed spatial regularizations dedicated to multiband image fusion. The fusion process combines a multispectral image with high spatial resolution and a hyperspectral image with high spectral resolution, with the aim of recovering a full resolution data-cube. In this work, we propose two spatial regularizations that exploit the spatial information of the multispectral image. A weighted Sobolev regularization identifies the sharp structures locations to locally mitigate a smoothness-promoting Sobolev regularization. A dictionary-based regularization takes advantage of spatial redundancy to recover spatial textures using a dictionary learned on the multispectral image. The proposed regularizations are evaluated on realistic simulations of James Webb Space Telescope (JWST) observations of the Orion Bar and show a better reconstruction of sharp structures compared to a non-informed regularization. Since JWST is now in orbit, we expect to use this method on real data in the near future