National Institute of Advanced Industrial Science and Technology

About: National Institute of Advanced Industrial Science and Technology is a government organization based out in Tsukuba, Ibaraki, Japan. It is known for research contribution in the topics: Catalysis & Thin film. The organization has 22114 authors who have published 65856 publications receiving 1669827 citations. The organization is also known as: Sangyō Gijutsu Sōgō Kenkyū-sho.

One hundred fold increase in current carrying capacity in a carbon nanotube–copper composite

Abstract: High electrical conductivity and ampacity are usually mutually exclusive properties. Here, in a carbon nanotube–copper composite, Subramaniam et al. achieve a similar conductivity to copper, but with a hundred fold increase in current carrying capacity.

Dynamic modeling of genetic networks using genetic algorithm and S-system

Abstract: Motivation: The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. Results: The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.

Atom-by-atom spectroscopy at graphene edge

Abstract: The properties of many nanoscale devices are sensitive to local atomic configurations, and so elemental identification and electronic state analysis at the scale of individual atoms is becoming increasingly important. For example, graphene is regarded as a promising candidate for future devices, and the electronic properties of nanodevices constructed from this material are in large part governed by the edge structures. The atomic configurations at graphene boundaries have been investigated by transmission electron microscopy and scanning tunnelling microscopy, but the electronic properties of these edge states have not yet been determined with atomic resolution. Whereas simple elemental analysis at the level of single atoms can now be achieved by means of annular dark field imaging or electron energy-loss spectroscopy, obtaining fine-structure spectroscopic information about individual light atoms such as those of carbon has been hampered by a combination of extremely weak signals and specimen damage by the electron beam. Here we overcome these difficulties to demonstrate site-specific single-atom spectroscopy at a graphene boundary, enabling direct investigation of the electronic and bonding structures of the edge atoms-in particular, discrimination of single-, double- and triple-coordinated carbon atoms is achieved with atomic resolution. By demonstrating how rich chemical information can be obtained from single atoms through energy-loss near-edge fine-structure analysis, our results should open the way to exploring the local electronic structures of various nanodevices and individual molecules.

Atg101, a novel mammalian autophagy protein interacting with Atg13

Abstract: Autophagy is a major route by which cytoplasmic contents are delivered to the lysosome for degradation. Many autophagy-related (ATG) genes have been identified in yeast. Although most of them are conserved in human, the molecular composition of the Atg1 complex appears to differ between yeast and mammals. In yeast, Atg1 forms a complex with Atg11, Atg13, Atg17, Atg29 and Atg31, whereas mammalian Atg1 (ULK1/2) interacts with Atg13 and FIP200. Here, we identify a novel mammalian Atg13 binding protein, named Atg101. Atg101 shows no homology with other Atg proteins, and is conserved in various eukaryotes, but not in Saccharomyces cerevisiae. Atg101 associates with the ULK-Atg13-FIP200 complex, most likely through direct interaction with Atg13. In Atg13 siRNA-treated cells, Atg101 is present solely as a monomer. Interaction between Atg101 and the ULK-Atg13-FIP200 complex is stable, and is not regulated by nutrient conditions. GFP-Atg101 localizes to the isolation membrane/phagophore. GFP-LC3 dot formation is suppressed and endogenous LC3-I accumulates in Atg101 siRNA-treated cells, suggesting that Atg101 is a critical factor for autophagy. Furthermore, Atg101 is important for the stability and basal phosphorylation of Atg13 and ULK1. These data suggest that Atg101 is a novel Atg protein that functions together with ULK, Atg13 and FIP200.