Rapid Reconstitution of Biosynthetic Machinery for Fungal Metabolites in Aspergillus oryzae: Total Biosynthesis of Aflatrem

Reconstitution of the biosynthetic machinery for fungal secondary metabolites in Aspergillus oryzae provides an opportunity both for stepwise determination of the biosynthetic pathways and the total biosynthesis of fungal natural products. However, to maximize the utility of the reconstitution system, a simple and rapid strategy for the introduction of heterologous genes into A. oryzae is required. In this study, we demonstrated an effective method for introducing multiple genes involved in the biosynthesis of fungal metabolites by using the expression vectors pUARA2 and pUSA2, each of which contains two cloning sites. The successful introduction of all the aflatrem biosynthetic genes (seven genes in total) after two rounds of transformation enabled the total biosynthesis of aflatrem. This rapid reconstitution strategy will facilitate the functional analysis of the biosynthetic machinery of fungal metabolites.     

Reduction and Sintering of a Nickel–Dispersed-Alumina Composite and Its Properties

High-density nickel–dispersed-alumina (Al₂O₃/nickel) composites with superior mechanical properties were obtained by the hydrogen reduction and the hot pressing of alumina–nickel oxide (Al₂O₃/NiO) mixed powders. The mixtures were prepared by using NiO or nickel nitrate (Ni(NO₃)₂· n H₂O) as a dispersion source of nickel metal. Microstructural investigations of the composite fabricated using nitrate powder revealed that fine nickel particles, } 100 nm in diameter, dispersed homogeneously at the matrix grain boundaries, forming the intergranular nanocomposite. High strength (.1 GPa) and high-temperature hardness were registered for the composite that contained a small amount of nickel dispersion. The ferromagnetic properties of nickel, such as high coercive force, were observed, because of the fine magnetic dispersions, which indicates a functional value of structural composites.     

Electrical and electroluminescent properties of poly(p-phenylene vinylene) LB films based LEDs using aluminum metal as the electrode

The electrical properties of poly(p-phenylene vinylene) (PPV) Langmuir-Blodgett (LB) films were investigated, wherein transparent indium-tin oxide (ITO) and aluminum (Al) were used as the electrodes. Their I–V characteristics depended strongly on the thickness of PPV LB film, and the electric conductivity of the PPV LB film was in the range of 10⁻²–10⁻¹⁵ (S/cm). Their C–V characteristics showed that the capacitance was reversibly proportional to the thickness of PPV LB film and was kept constant when the applied voltage changed from −1.0 V to +1.0 V. These results indicated that PPV LB film was an insulator in this range of applied voltage without doping. Under forward bias, yellow-green light emission was observed in PPV LB film based light emitting diodes (LEDs), the highest light emission reached more than 100 cd/m² in the case that PPV LB film was deposited for 80 layers, i.e. a ITO/PPV(80L)/Al device.     

Effect of the preparation condition on the properties of titania-silica derived from tetraisopropyl titanate(IV) and tetraethyl orthosilicate

Titania-silica mixed oxide was prepared by hydrolyzing tetraethyl orthosilicate and tetraisopropyl titanate(IV) with a mixture of ethanol and 0.01 N aqueous acetic acid. Surface Ti content, BET surface area, and the number of acid sites increased with an increase in 0.01 N aqueous acetic acid. Thus hydrolysis of both alkoxides occurred simultaneously with a large amount of the acetic acid solution, and there was extensive interaction between TiO₂ and SiO₂ phases. With small amount of 0.01 N aqueous acetic acid, however, titanium hydroxide was first formed and, then, it was covered with the silica phase produced in a later stage of the hydrolysis. Increase in the acetic acid solution also led to the formation of a large amount of tetrahedral Ti species, which were active for the epoxidation of oct-1-ene usingt-butyl hydroperoxide as an oxidant.     

Implementation of Fault-Tolerant GridRPC Applications

A task parallel application is implemented with Ninf-G, a GridRPC system. A series of experiments are conducted on the Grid testbed in Asia Pacific for three months. Through tens of long executions, typical fault patterns were collected, and instability of the network throughput was determined to be a major reason of the faults. Several important points are stressed to avoid task throughput decline due to the fault-recovery operations: Timeout minimization for fault detection, background recovery, redundant task assignments, and so on. This study also issues a steer for design of the automated fault-tolerant mechanism in an upper layer of the GridRPC framework.     

A new algorithm for packet routing problems using chaotic neurodynamics and its surrogate analysis

We propose a new algorithm for routing packets which effectively avoids packet congestion in computer networks. The algorithm involves chaotic neurodynamics. To realize effective packet routing, we first composed a basic method by a neural network, which routes packets with shortest path information between two nodes in the computer network. When the computer network has an irregular topology, the basic routing method does not work well, because most of packets cannot be transmitted to their destinations due to packet congestion in the computer network. To avoid such an undesirable problem, we employed chaotic neurodynamics to extend the basic method. Numerical experiments show that our proposed method exhibits good performance for scale-free networks. We also analyze why the proposed routing method is effective, comparing the proposed method with several stochastic methods. We introduced the method of surrogate data, a statistical hypothesis testing which is often used in the field of nonlinear time-series analysis. Analysis of the proposed method by the method of surrogate data reveals that the chaotic neurodynamics is most effective to decentralize the packet congestion in the computer network.     

Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments

We extend an abstract agent-based swarming model based on the evolution of neural network controllers, to explore further the emergence of swarming. Our model is grounded in the ecological situation, in which agents can access some information from the environment about the resource location, but through a noisy channel. Swarming critically improves the efficiency of group foraging, by allowing agents to reach resource areas much more easily by correcting individual mistakes in group dynamics. As high levels of noise may make the emergence of collective behavior depend on a critical mass of agents, it is crucial to reach sufficient computing power to allow for the evolution of the whole set of dynamics in simulation. Since simulating neural controllers and information exchanges between agents are computationally intensive, to scale up simulations to model critical masses of individuals, the implementation requires careful optimization. We apply techniques from astrophysics known as treecodes to compute the signal propagation, and efficiently parallelize for multi-core architectures. Our results open up future research on signal-based emergent collective behavior as a valid collective strategy for uninformed search over a domain space.     

A stochastic realization algorithm via block LQ decomposition in Hilbert space

A stochastic realization problem of a stationary stochastic process is re-visited, and a new stochastically balanced realization algorithm is derived in a Hilbert space generated by second-order stationary processes. The present algorithm computes a stochastically balanced realization by means of the singular value decomposition of a weighted block Hankel matrix derived by a “block LQ decomposition”. Extension to a stochastic subspace identification method explains how the proposed abstract algorithm is implemented in system identification.     

Interactive segmentation of the cerebral lobes with fuzzy inference in 3T MR images.

Measurement of volume and surface area of the frontal, parietal, temporal and occipital lobes from magnetic resonance (MR) images shows promise as a method for use in diagnosis of dementia. This article presents a novel computer-aided system for automatically segmenting the cerebral lobes from 3T human brain MR images. Until now, the anatomical definition of cerebral lobes on the cerebral cortex is somewhat vague for use in automatic delineation of boundary lines, and there is no definition of cerebral lobes in the interior of the cerebrum. Therefore, we have developed a new method for defining cerebral lobes on the cerebral cortex and in the interior of the cerebrum. The proposed method determines the boundaries between the lobes by deforming initial surfaces. The initial surfaces are automatically determined based on user-given landmarks. They are smoothed and deformed so that the deforming boundaries run along the hourglass portion of the three-dimensional shape of the cerebrum with fuzzy rule-based active contour and surface models. The cerebrum is divided into the cerebral lobes according to the boundaries determined using this method. The reproducibility of our system with a given subject was assessed by examining the variability of volume and surface area in three healthy subjects, with measurements performed by three beginners and one expert user. The experimental results show that our system segments the cerebral lobes with high reproducibility.