We isolated an MgATG1 gene encoding a serine/threonine protein kinase from the rice blast fungus Magnaporthe grisea. In the ⌬Mgatg1 mutant, in which the MgATG1 gene had been deleted, autophagy was blocked; the mutant also showed fewer lipid droplets in its conidia, lower turgor pressure of the appressorium, and such defects in morphogenesis as delayed initiation and slower germination of conidia. As a result of lower turgor pressure of the appressorium, the ⌬Mgatg1 mutant lost its ability to penetrate and infect the two host plants, namely, rice and barley. However, normal values of the parameters and infective abilities were restored on reintroducing an intact copy of the MgATG1 gene into the mutant. Autophagy is thus necessary for turnover of organic matter during the formation of conidia and appressoria and for normal development and pathogenicity in M. grisea.Magnaporthe grisea, a filamentous ascomycete fungus, is best known as the causal agent of rice blast, the most serious disease of cultivated rice throughout the world (15), and has been developed as a model organism for investigating fungus-host interactions (4, 27). The appressorium, a specialized cell necessary for infection by the rice blast fungus, generates tremendous intracellular turgor pressure (as much as 8.0 MPa) to penetrate the leaf cuticle (5, 26). Such enormous turgor in the appressorium is a consequence of accumulation of very large quantities of glycerol in the cell, and potential sources of glycerol biosynthesis are lipid, glycogen, and two sugars, trehalose and mannitol, in the conidium (21).The process of development from the conidium to the appressorium, and then from the appressorium to the penetration peg or infectious hypha, requires a cell to undergo significant phenotypic changes accompanied by the breakdown and recycling of old cellular components in about 30 h (21). In M. grisea, appressorium formation involves autophagy, but nuclei in conidia of the ⌬Mgatg8 mutant were not degraded during appressorium formation, and the mutant failed to infect the plant through the appressoria (29). Also, the mutant produced far fewer conidia. Autophagy is a common and evolutionarily preserved process that degrades and recycles old proteins and organelles in all eukaryotic cells (8,9,18,31). For many years, autophagy was believed to be involved in changes in cellular architecture during differentiation and development, presumably through its role in the turnover of organelles and proteins (9). Our study sought to find out whether autophagy has a role in the turnover of organic matter that enables a hypha to form a conidium, which then goes on to develop an appressorium, and in generating the turgor pressure in the appressorium required for successful infection.An expressed sequence tag (clone s197; GenBank accession no. CK828251) for MgATG1 (autophagy-related gene 1), homologous to ATG1 of yeast in its protein sequence, was found in the appressorium of the rice blast fungus (10). Because the fungus is used as a primary model for host-pathogen...

Most of the existing denoising algorithms are developed for grayscale images. It is not trivial to extend them for color image denoising since the noise statistics in R, G, and B channels can be very different for real noisy images. In this paper, we propose a multi-channel (MC) optimization model for real color image denoising under the weighted nuclear norm minimization (WNNM) framework. We concatenate the RGB patches to make use of the channel redundancy, and introduce a weight matrix to balance the data fidelity of the three channels in consideration of their different noise statistics. The proposed MC-WNNM model does not have an analytical solution. We reformulate it into a linear equality-constrained problem and solve it via alternating direction method of multipliers. Each alternative updating step has a closed-form solution and the convergence can be guaranteed. Experiments on both synthetic and real noisy image datasets demonstrate the superiority of the proposed MC-WNNM over state-of-the-art denoising methods.

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Most of existing image denoising methods assume the corrupted noise to be additive white Gaussian noise (AWGN). However, the realistic noise in real-world noisy images is much more complex than AWGN, and is hard to be modeled by simple analytical distributions. As a result, many state-of-the-art denoising methods in literature become much less effective when applied to real-world noisy images captured by CCD or CMOS cameras. In this paper, we develop a trilateral weighted sparse coding (TWSC) scheme for robust real-world image denoising. Specifically, we introduce three weight matrices into the data and regularization terms of the sparse coding framework to characterize the statistics of realistic noise and image priors. TWSC can be reformulated as a linear equality-constrained problem and can be solved by the alternating direction method of multipliers. The existence and uniqueness of the solution and convergence of the proposed algorithm are analyzed. Extensive experiments demonstrate that the proposed TWSC scheme outperforms state-of-the-art denoising methods on removing realistic noise.

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Abstract-The palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Current palmprint-based systems are more user friendly, more cost effective, and require fewer data signatures than traditional fingerprint-based identification systems. The principal lines and wrinkles captured in a low-resolution palmprint image provide more than enough information to uniquely identify an individual. This paper presents a palmprint identification scheme that characterizes a palmprint using a set of statistical signatures. The palmprint is first transformed into the wavelet domain, and the directional context of each wavelet subband is defined and computed in order to collect the predominant coefficients of its principal lines and wrinkles. A set of statistical signatures, which includes gravity center, density, spatial dispersivity and energy, is then defined to characterize the palmprint with the selected directional context values. A classification and identification scheme based on these signatures is subsequently developed. This scheme exploits the features of principal lines and prominent wrinkles sufficiently and achieves satisfactory results. Compared with the line-segments-matching or interesting-points-matching based palmprint verification schemes, the proposed scheme uses a much smaller amount of data signatures. It also provides a convenient classification strategy and more accurate identification.