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Estrogen receptor alpha 36 (ERα36) is a variant of the canonical estrogen receptor alpha (ERα66), widely expressed in hormone sensitive cancer cells and whose high expression level correlates with a poor survival prognosis for breast cancer patients. While ERα36 activity have been related to breast cancer progression or acquired resistance to treatment, expression level and location of ERα36 are poorly documented in the normal mammary gland. Therefore, we explored the consequences of a ERα36 overexpression in vitro in MCF-10A normal mammary epithelial cells and in vivo in a unique model of MMTV-ERα36 transgenic mouse strain wherein ERα36 mRNA was specifically expressed in the mammary gland. By a combination of bioinformatics and computational analyses of microarray data, we identified hierarchical gene networks, downstream of ERα36 and modulated by the JAK2/STAT3 signaling pathway. Concomitantly, ERα36 overexpression lowered proliferation rate but enhanced migration potential and resistance to staurosporin-induced apoptosis of the MCF-10A cell line. In vivo, ERα36 expression led to duct epithelium thinning and disruption in adult but not in prepubescent mouse mammary gland. These phenotypes correlated with a loss of E-cadherin expression. Here, we show that an enhanced expression of ERα36 is sufficient, by itself, to disrupt normal breast epithelial phenotype in vivo and in vitro through a dominant-positive effect on nongenomic estrogen signaling pathways. These results also suggest that, in the presence of adult endogenous steroid levels, ERα36 overexpression in vivo contributes to alter mammary gland architecture which may support pre-neoplastic lesion and augment breast cancer risk.

Orthogonal greedy algorithms are popular sparse signal reconstruction algorithms. Their principle is to select atoms one by one. A series of unconstrained least-squares subproblems of gradually increasing size is solved to compute the approximation coefficients, which is efficiently performed using a fast recursive update scheme. When dealing with nonnegative sparse signal reconstruction, a series of non-negative least-squares subproblems have to be solved. Fast implementation becomes tricky since each subproblem does not have a closedform solution anymore. Recently, non-negative extensions of the classical orthogonal matching pursuit and orthogonal least squares algorithms were proposed, using slow (i.e., non-recursive) or recursive but inexact implementations. In this paper, we revisit these algorithms in a unified way. We define a class of non-negative orthogonal algorithms and exhibit their structural properties. We propose a fast and exact implementation based on the active-set resolution of non-negative least-squares and exploiting warm start initializations. The algorithms are assessed in terms of accuracy and computational complexity for a sparse spike deconvolution problem. We also present an application to near-infrared spectra decomposition.

This paper aims at studying a method to automatically estimate the regularization parameters of non-negative hyperspectral image deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem and the properties of the corresponding response surface are studied. Based on these properties, the minimum distance criterion (MDC) and the maximum curvature criterion (MCC) are proposed to estimate regularization parameters especially for the non-negativity constrained deconvolution problem. MDC has good theoretical properties (convexity and uniqueness) but requires to choose a reference point. On the contrary, MCC does not need to choose any reference point but does not have interesting theoretical properties. A grid-search-based approach to minimize the computational cost of MDC and MCC is proposed. It results in fast approaches to estimate the regularization parameters. Based on simulated 2D images, the proposed approaches are compared with the state-of-the-art methods, confirming the effectiveness of the MDC and MCC for the non-negativity constrained image deconvolution problem. In the case of non-negative hyperpsectral image deconvolution, the fast MDC yields better performances than the fast MCC. An application to real-world hyperspectral fluorescence microscopy images is also provided; it confirms the superiority of MDC.

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We address the problem of multidimensional modal estimation using sparse estimation techniques coupled with an efficient multigrid approach. Modal dictionaries are obtained by discretizing modal functions (damped complex exponentials). To get a good resolution, it is necessary to choose a fine discretization grid resulting in intractable computational problems due to the huge size of the dictionaries. The idea behind the multigrid approach amounts to refine the dictionary over several levels of resolution. The algorithm starts from a coarse grid and adaptively improves the resolution in dependence of the active set provided by sparse approximation methods. The proposed method is quite general in the sense that it allows one to process in the same way mono-and multidimensional signals. We show through simulations that, as compared to high-resolution modal estimation methods, the proposed sparse modal method can greatly enhance the estimation accuracy for noisy signals and shows good robustness with respect to the choice of the number of components.

International audienceThis paper investigates some issues in physical modeling of metal inert gas/metal active gas (MIG/MAG) welding process in the short arc mode. In this mode, a metal supply is molten in the arc state and then transferred to the weld pool during the short-circuit state. A hybrid model having two distinct continuous states whose switchings are controlled by two guard conditions is proposed. Due to the complexity of the physical phenomena involved in the welding process, simplifications are used to obtain a model accounting for the main physical contributions but simple enough to yield an efficient, fast and numerically tractable simulator which can be used intensively for evaluating different control strategies. In an attempt to validate the proposed model, different measurements have been made including supply voltage and current sampled synchronously with high speed digital video. In order to extract some relevant quantities representative of the metal transfer from image sequences, an active contour algorithm is developed and tested. The effectiveness of the proposed model in the prediction of major tendencies of a welding process, especially in the arc state, is shown using experimental data. Some limitations of the model during the metal transfer are also stressed and possible remedies are then proposed

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