New isoforms and assembly of glutamine synthetase in the leaf of wheat (Triticum aestivum L.).

Glutamine synthetase (GS; EC 6.3.1.2) plays a crucial role in the assimilation and re-assimilation of ammonia derived from a wide variety of metabolic processes during plant growth and development. Here, three developmentally regulated isoforms of GS holoenzyme in the leaf of wheat (Triticum aestivum L.) seedlings are described using native-PAGE with a transferase activity assay. The isoforms showed different mobilities in gels, with GSII>GSIII>GSI. The cytosolic GSI was composed of three subunits, GS1, GSr1, and GSr2, with the same molecular weight (39.2kDa), but different pI values. GSI appeared at leaf emergence and was active throughout the leaf lifespan. GSII and GSIII, both located in the chloroplast, were each composed of a single 42.1kDa subunit with different pI values. GSII was active mainly in green leaves, while GSIII showed brief but higher activity in green leaves grown under field conditions. LC-MS/MS experiments revealed that GSII and GSIII have the same amino acid sequence, but GSII has more modification sites. With a modified blue native electrophoresis (BNE) technique and in-gel catalytic activity analysis, only two GS isoforms were observed: one cytosolic and one chloroplastic. Mass calibrations on BNE gels showed that the cytosolic GS1 holoenzyme was ~490kDa and likely a dodecamer, and the chloroplastic GS2 holoenzyme was ~240kDa and likely a hexamer. Our experimental data suggest that the activity of GS isoforms in wheat is regulated by subcellular localization, assembly, and modification to achieve their roles during plant development

Signaling and Transcriptional Control of Reproductive Development in Arabidopsis

Plant reproductive development is a complex process with diploid and haploid phases, including male and female organogenesis, meiosis, gametogenesis, pollination and fertilization. A number of regulatory mechanisms control both diploid and haploid cell division and differentiation, especially cell–cell signaling pathways mediated by receptor-linked protein kinases with prominent roles in early male development, and hormonal signaling pathways crucial for later events in male and female reproductive development. Furthermore, transcriptional networks control the proper formation of specific cell layers and embryo sac cell specification

Robust Ordinal Embedding from Contaminated Relative Comparisons

Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a unified framework to jointly identify the contaminated comparisons and derive reliable embeddings. The merits of our method are three-fold: (1) By virtue of the proposed unified framework, the sub-optimality of traditional methods is largely alleviated; (2) The proposed method is aware of global inconsistency by minimizing a corresponding cost, while traditional methods only involve local inconsistency; (3) Instead of considering the nuclear norm heuristics, we adopt an exact solution for rank equality constraint. Our studies are supported by experiments with both simulated examples and real-world data. The proposed framework provides us a promising tool for robust ordinal embedding from the contaminated comparisons.Comment: Accepted by AAAI 201

Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin

In the absence of prior knowledge, ordinal embedding methods obtain new representation for items in a low-dimensional Euclidean space via a set of quadruple-wise comparisons. These ordinal comparisons often come from human annotators, and sufficient comparisons induce the success of classical approaches. However, collecting a large number of labeled data is known as a hard task, and most of the existing work pay little attention to the generalization ability with insufficient samples. Meanwhile, recent progress in large margin theory discloses that rather than just maximizing the minimum margin, both the margin mean and variance, which characterize the margin distribution, are more crucial to the overall generalization performance. To address the issue of insufficient training samples, we propose a margin distribution learning paradigm for ordinal embedding, entitled Distributional Margin based Ordinal Embedding (\textit{DMOE}). Precisely, we first define the margin for ordinal embedding problem. Secondly, we formulate a concise objective function which avoids maximizing margin mean and minimizing margin variance directly but exhibits the similar effect. Moreover, an Augmented Lagrange Multiplier based algorithm is customized to seek the optimal solution of \textit{DMOE} effectively. Experimental studies on both simulated and real-world datasets are provided to show the effectiveness of the proposed algorithm.Comment: Accepted by AAAI 201

Control of Fluid Dynamics by Nanoparticles in Laser Melting

Effective control of fluid dynamics is of remarkable scientific and practical significance. It is hypothesized that nanoparticles could offer a novel means to control fluid dynamics. In this study, laser melting was used to investigate the feasibility of tuning fluid dynamics by nanoparticles and possibly breaking existing limits of conventional laser processing techniques. Alumina nanoparticles reinforced nickel samples, fabricated through electrocodeposition, were used for laser melting experiments. Since the melt pool surface is controlled by the fluid dynamics, surface topographies were carefully studied to reveal the nanoparticle effect on the fluid dynamics. Characterizations of surface topographies and microstructures of pure Ni and Ni/Al2O3 nanocomposite were carried out before and after laser melting. The surface roughness of the Ni/Al2O3 nanocomposite sample was reduced significantly by laser melting, which broke the existing limit of laser surface polishing of pure Ni. It is believed that the nanoparticles increased the viscosity of the molten metal, thereby enhancing the viscous damping of the capillary oscillations in the melt pool, to produce a much smoother surface. Moreover, the experimental study also revealed that the viscosity enhancement by the nanoparticles effectively suppressed the thermocapillary flows which would introduce artificial asperities on a surface. The experimental results suggest that nanoparticles are effective in controlling melt pool dynamics and overcoming the existing limits of laser processing. The new methodology, fluid dynamics control by nanoparticles, opens a new pathway to enrich liquid based processes for broad applications

Proving Secure Properties of Cryptographic Protocols with Knowledge Based Approach

Cryptographic protocols have been widely used to protect communications over insecure network environments. Existing cryptographic protocols usually contain flaws. To analyze these protocols and find potential flaws in them, the secure properties of them need be studied in depth. This paper attempts to provide a new framework to analyze and prove the secure properties in these protocols. A number of predicates and action functions are used to model the network communication environment. Domain rules are given to describe the transitions of principals\u27 knowledge and belief states. An example of public key authentication protocols has been studied and analysed

Nanocomposites of Carbon Nanotube (CNTs)/CuO with High Sensitivity to Organic Volatiles at Room Temperature

AbstractIn order to enhance the sensitivity of carbon nanotube based chemical sensors at room temperature operation, CNTs/CuO nanocomposite was prepared under hydrothermal reaction condition. The resulted-product was characterized with TEM (transmission electron microscopy), XRD (X-ray diffraction) and so on. A chemical prototype sensor was constructed based on CNTs/CuO nanocomposite and an interdigital electrode on flexible polymer substrate. The gas-sensing behavior of the sensor to some typical organic volatiles was investigated at room temperature operation. The results indicated that the carbon nanotube was dispersed well in CuO matrix, the CuO was uniformly coated on the surface of carbon nanotube, and the tubular structure of carbon nanotube was clearly observed. From morphology of TEM images, it can also be observed that a good interfacial adhesion between CNT and CuO matrix was formed, which maybe due to the results of strong interaction between CNTs with carboxyl groups and CuO containing some hydroxy groups. The CNTs/CuO nanocomposite showed dramatically enhanced sensitivity to some typical organic volatiles. This study would provide a simple, low-cost and general approach to functionalize the carbon nanotube. It is also in favor of developing chemical sensors with high sensitivity or catalysts with high activity to organic volatiles at low temperature