Differentiation of Arthroderma spp. by random amplification of polymorphic DNA (RAPD) and Southern hybridization

To develop the molecular differentiation analysis of dermatophytes, we carried out RAPD and Southern hybridization analyses using genomic DNAs of six Arthroderma species, including A. fulvum, A. grubyi, A. gypseum, A. incurvatum, A. otae and A. racemosum. The RAPD analysis gave different band patterns specific to each of the six Arthroderma fungi. However, minor differences in the banding patterns were observed between the strains of plus (+) and minus (-) mating types of A. gypseum, A. fulvum and A. incurvatum. Southern blot analysis using a probe (1S) obtained from A. grubyi DNA gave specific bands only in the DNA samples of A. grubyi and A. incurvatum. On the other hand, Southern blot analysis using a probe (C3) obtained from A. otae DNA gave specific bands in all six Arthroderma species examined, and the size of the bands were specific to each species. These findings indicate that RAPD and Southern hybridization analyses are useful in the differentiation of these Arthroderma species.     

The telecommunications market in Japan: entering the digital eraand the second phase of competition

After the liberalization of the Japanese telecommunications market in 1985, the market went through a first phase of competition, which focused on domestic telephony services, in both the long distance (domestic and international) and mobile segments. However, the growth of the Internet and the full digitalization of NTT's telecommunications network are ushering in a digital era and a second phase of competition. This article describes the current Japanese telecommunications market and the direction it is taking by referring to key statistical indicators     

Polarity control of GaN epilayers grown on ZnO templates

We report the successful growth of Ga-polar GaN epilayers on O-polar ZnO templates pre-deposited on c-sapphire. Prior to GaN growth, NH₃ is exposed onto the ZnO template. The polarity of the GaN layers is confirmed by etching of the surface and by conversion beam electron diffraction (CBED), while the O-polar ZnO is confirmed by CBED. It is suggested that the NH₃ pre-exposure helps form a Zn₃N₂ layer, which possesses inversion symmetry and inverts the crystal from anion polar to cation polar.     

Patterns of expression for the mRNA corresponding to the four isoforms of phospholipase Cbeta in mouse brain

Ligand binding to neurotransmitter and hormone receptors which couple to the Gq subclass of GTP-binding protein leads to the activation of phospholipase Cbeta (PLCbeta) which hydrolyses phosphatidyl-inositol 4,5-bisphosphate, yielding a pair of second messengers, diacylglycerol and inositol 1,4,5-trisphosphate (IP3). The expression of PLCbeta1-4 mRNAs was comparatively examined by in situ hybridization in the mouse brain. In adults, PLCbeta1 mRNA was expressed predominantly in the telencephalon, including the cerebral cortex, hippocampus, amygdala, lateral septum and olfactory bulb, with little expression in most thalamic nuclei. PLCbeta2 mRNA was distributed in the white matter, suggesting its expression in non-neuronal cells, most likely oligodendrocytes. PLCbeta3 mRNA was specifically expressed in cerebellar Purkinje cells. The highest levels of PLCbeta4 mRNA were detected in Purkinje cells. High levels of PLCbeta4 mRNA were also found in the thalamus and medial septum, whereas weak signals were detected in most telencephalic regions, thus showing an expression pattern almost reciprocal to that of PLCbeta1 mRNA. During development, such characteristic regional expression of PLCbeta1 and PLCbeta4 were observed starting in late foetal stages, while specific expression of PLCbeta2 and PLCbeta3 appeared in early postnatal stages. We conclude that despite the existence of four PLCbeta isoforms, only one or two of them is expressed in individual neurons and glial cells. The distinct expression of PLCbetas provides a molecular basis for analysing the nature of the specific signal transduction pathway leading to the production of diacylglycerol and IP3 in distinct cell types and in different regions of the brain.     

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

     

Level-of-Detail Rendering of Large-Scale Irregular Volume Datasets Using Particles

This paper describes a level-of-detail rendering technique for large-scale irregular volume datasets. It is well known that the memory bandwidth consumed by visibility sorting becomes the limiting factor when carrying out volume rendering of such datasets. To develop a sorting-free volume rendering technique, we previously proposed a particle-based technique that generates opaque and emissive particles using a density function constant within an irregular volume cell and projects the particles onto an image plane with sub-pixels. When the density function changes significantly in an irregular volume cell, the cell boundary may become prominent, which can cause blocky noise. When the number of the sub-pixels increases, the required frame buffer tends to be large. To solve this problem, this work proposes a new particle-based volume rendering which generates particles using metropolis sampling and renders the particles using the ensemble average. To confirm the effectiveness of this method, we applied our proposed technique to several irregular volume datasets, with the result that the ensemble average outperforms the sub-pixel average in computational complexity and memory usage. In addition, the ensemble average technique allowed us to implement a level of detail in the interactive rendering of a 71-million-cell hexahedral volume dataset and a 26-million-cell quadratic tetrahedral volume dataset.     

Particle-based multiple irregular volume rendering on CUDA

In this paper, we describe an improved particle-based volume rendering (PBVR) technique for previewing a large irregular volume dataset using the CUDA architecture. This technique allows for opaque and emissive particles to render translucent volumes without visibility sorting. Our GPU acceleration of PBVR provides the multi-volume rendering feature while remaining compatible with both regular and irregular volumes. We also reduce the memory cost required for storing all sub-pixel values by proposing a pixel repetition technique for a large sub-pixel level. By adjusting the repetition level, we achieved a very smooth level of detail (LOD) control for trading quality for speed. Our work demonstrates a full-detail rendering rate from 5 to 10 fps for irregular volume data with mega-scale cell numbers on an NVIDIA GeForce 8800GTS.     

Recurrent infomax generates cell assemblies, neuronal avalanches, and simple cell-like selectivity

Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly-like and synfire chain-like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view.     

Two-stage subspace identification for softsensor design and disturbance estimation

Softsensors or virtual sensors are key technologies in industry because important variables such as product quality are not always measured on-line. In the present work, two-stage subspace identification (SSID) is proposed to develop highly accurate softsensors that can take into account the influence of unmeasured disturbances on estimated key variables explicitly. The proposed two-stage SSID method can estimate unmeasured disturbances without the assumptions that the conventional Kalman filtering technique must make. Therefore, it can outperform the Kalman filtering technique when innovations are not Gaussian white noises or the characteristics of disturbances do not stay constant with time. The superiority of the proposed method over the conventional methods is demonstrated through numerical examples and application to an industrial ethylene fractionator.     

Platform design for large-scale artificial market simulation and preliminary evaluation on the K computer

Artificial market simulations have the potential to be a strong tool for studying rapid and large market fluctuations and designing financial regulations. High-frequency traders, that exchange multiple assets simultaneously within a millisecond, are said to be a cause of rapid and large market fluctuations. For such a large-scale problem, this paper proposes a software or computing platform for large-scale and high-frequency artificial market simulations (Plham: /pl\(\Lambda\)m). The computing platform, Plham, enables modeling financial markets composed of various brands of assets and a large number of agents trading on a short timescale. The design feature of Plham is the separation of artificial market models (simulation models) from their execution (execution models). This allows users to define their simulation models without parallel computing expertise and to choose one of the execution models they need. This computing platform provides a prototype execution model for parallel simulations, which exploits the variety in trading frequency among traders, that is, the fact that some traders do not require up-to-date information of markets changing in millisecond order. We evaluated a prototype implementation on the K computer using up to 256 computing nodes.