Nitrate reductase is required for sclerotial development and virulence of Sclerotinia sclerotiorum

Sclerotinia sclerotiorum, the causal agent of Sclerotinia stem rot (SSR) on more than 450 plant species, is a notorious fungal pathogen. Nitrate reductase (NR) is required for nitrate assimilation that mediates the reduction of nitrate to nitrite and is the major enzymatic source for NO production in fungi. To explore the possible effects of nitrate reductase SsNR on the development, stress response, and virulence of S. sclerotiorum, RNA interference (RNAi) of SsNR was performed. The results showed that SsNR-silenced mutants showed abnormity in mycelia growth, sclerotia formation, infection cushion formation, reduced virulence on rapeseed and soybean with decreased oxalic acid production. Furthermore SsNR-silenced mutants are more sensitive to abiotic stresses such as Congo Red, SDS, H2O2, and NaCl. Importantly, the expression levels of pathogenicity-related genes SsGgt1, SsSac1, and SsSmk3 are down-regulated in SsNR-silenced mutants, while SsCyp is up-regulated. In summary, phenotypic changes in the gene silenced mutants indicate that SsNR plays important roles in the mycelia growth, sclerotia development, stress response and fungal virulence of S. sclerotiorum

Tuning crystal-phase of bimetallic single-nanoparticle for catalytic hydrogenation

Bimetallic nanoparticles afford geometric variation and electron redistribution via strong metal-metal interactions that substantially promote the activity and selectivity in catalysis. Quantitatively describing the atomic configuration of the catalytically active sites, however, is experimentally challenged by the averaging ensemble effect that is caused by the interplay between particle size and crystal-phase at elevated temperatures and under reactive gases. Here, we report that the intrinsic activity of the body-centered cubic PdCu nanoparticle, for acetylene hydrogenation, is one order of magnitude greater than that of the face-centered cubic one. This finding is based on precisely identifying the atomic structures of the active sites over the same-sized but crystal-phase-varied single-particles. The densely-populated Pd-Cu bond on the chemically ordered nanoparticle possesses isolated Pd site with a lower coordination number and a high-lying valence d-band center, and thus greatly expedites the dissociation of H$_2$ over Pd atom and efficiently accommodates the activated H atoms on the particle top/subsurfaces

Reconstruction from limited-angle projections based on spectrum analysis

This paper proposes a sparse representation of an image using discrete δ-u functions. A δ-u function is defined as the product of a Kronecker delta function and a step function. Based on the sparse representation, we have developed a novel and effective method for reconstructing an image from limited-angle projections. The method first estimates the parameters of the sparse representation from the incomplete projection data, and then directly calculates the image to be reconstructed. Experiments have shown that the proposed method can effectively recover the missing data and reconstruct images more accurately than the total-variation (TV) regularized reconstruction method

Generic Half-Quadratic Optimization for Image Reconstruction

International audienceWe study the global and local convergence of a generic half-quadratic optimization algorithm inspired from the dual energy formulation of Geman and Reynolds [IEEE Trans. Pattern Anal. Mach. Intell., 14 (1992), pp. 367--383]. The target application is the minimization of $C^{1}$ convex and nonconvex objective functionals arising in regularized image reconstruction. Our global convergence proofs are based on a monotone convergence theorem of Meyer [J. Comput. System Sci., 12 (1976), pp. 108--121]. Compared to existing results, ours extend to a larger class of objectives and apply under weaker conditions; in particular, we cover the case where the set of stationary points is not discrete. Our local convergence results use a majorization-minimization interpretation to derive an insightful characterization of the basins of attraction; this new perspective grounds a formal description of the intuitive water-flooding analogy. We conclude with image restoration experiments to illustrate the efficiency of the algorithm under various nonconvex scenarios.Read More: http://epubs.siam.org/doi/abs/10.1137/14098784

A Comparative Study of Adaptive Registration Methods for Two Dimensional Gel Electrophoresis Images.

International audienceTwo-dimensional gel electrophoresis (2DGE) images play a major role in techniques for protein separation. The registration of 2DGE images is considered as one of key elements in protein identification. This paper proposes a single adaptive registration scheme for 2DGE images. Within the scheme, three registration methods based on the combined use of global linear affine and local nonlinear thin-plate spline (TPS), Demons, B-spline algorithms are explored and compared. The results show that the method using multi-resolution B-spline is preferred

INEXACT HALF-QUADRATIC OPTIMIZATION FOR LINEAR INVERSE PROBLEMS

International audienceWe study the convergence of a generic half-quadratic algorithm for minimizing a wide class of objective functions that occur in inverse imaging problems; this algorithm amounts to solving a sequence of positive definite systems (the inner systems) and has the advantages of simplicity and versatility. Half-quadratic optimization has been meticulously studied, both theoretically and experimentally, but two difficulties remain: first, the practical solutions of the inner systems are generally approximate, which may hamper convergence, and, second, convergence to a stationary point of the objective is not guaranteed if the set of such points contains a continuum. We present new results that do not suffer from these limitations and hence extend our work in [SIAM J. Imaging Sci. 8 (2015), no. 3, 1752–1797]. We consider the inexact process in which the inner systems are solved to a fixed arbitrary accuracy defined in terms of the energy norm of the error. We show that this process converges to a stationary point of the objective under minimal conditions ubiquitous in regularized reconstruction and restoration. Our main results are based on the assumption that the objective has the Kurdyka-Lojasiewicz property, for which we provide constructing rules using the concept of tameness from the theory of o-minimal structures. We also propose an implementation using a truncated conjugate gradient method that controls the accuracy at negligible additional cost. Experiments on three different inverse problems show that the resulting algorithm performs well in various nonconvex scenarios and converges to solutions accurate to full machine precision

A stochastic approach to full inverse treatment planning for charged-particle therapy

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