OBJECTIVES: Linking information on Japanese pharmaceutical products to global knowledge bases (KBs) would enhance international collaborative research and yield valuable insights. However, public access to mappings of Japanese pharmaceutical products that use international controlled vocabularies remains limited. This study mapped YJ codes to RxNorm ingredient classes, providing new insights by comparing Japanese and international drug-drug interaction (DDI) information using a case study methodology. MATERIALS AND METHODS: Tables linking YJ codes to RxNorm concepts were created using the application programming interfaces of the Kyoto Encyclopedia of Genes and Genomes and the National Library of Medicine. A comparative analysis of Japanese and international DDI information was thus performed by linking to an international DDI KB. RESULTS: There was limited agreement between the Japanese and international DDI severity classifications. Cross-tabulation of Japanese and international DDIs by severity showed that 213 combinations classified as serious DDIs by an international KB were missing from the Japanese DDI information. DISCUSSION: It is desirable that efforts be undertaken to standardize international criteria for DDIs to ensure consistency in the classification of their severity. CONCLUSION: The classification of DDI severity remains highly variable. It is imperative to augment the repository of critical DDI information, which would revalidate the utility of fostering collaborations with global KBs.

In our previous research, we showed that the maximum flow and the minimum cut problem can be solved by using a resistive circuit consisting of nonlinear devices with a saturation characteristic. Usually, the flow network consists of three elements, such as connectivity between nodes, branch capacities, and flows. The network information obtained from nonlinear resistive circuit analysis also has conventional three elements such as the connectivity of edge, resistances of edge (= branch capacities) and currents (= flows). In addition, each node voltage is obtained as a 4th element. In this paper, a novel minimum cut solution by using node information as 4th element is proposed in a completely different way from conventional methods. When a number of minimum cuts exist in a given flow network, there is no conventional algorithm which can simultaneously give these cuts at the moment. However, all minimum cuts can be obtained at the same time by using proposed method. Moreover, since the proposed method is realizable by a nonlinear resistive circuit, speed improvement of the minimum cut algorithm can be expected.

This paper proposes a novel hierarchical lossless image coding scheme using cellular neural network (CNN). The coding architecture of proposed method is composed of three steps: split, predict, and entropy coding. The coding performance of proposed method highly depends on that of CNN predictors. The resulting prediction errors are encoded by the adaptive arithmetic coder. To achieve the high coding efficiency, the type of space-variant CNN templates and their parameters are optimized to minimize the actual coding bits of prediction residuals by the minimum coding rate learning with backward error propagation. Experimental results in 21 kinds of standard grayscale test images show that the average coding rates of the proposed scheme is better than that of the conventional schemes.

This paper proposes a new interpolation method for an incomplete image using sigma-delta modulation type of Discrete-Time Cellular Neural Networks. Missing pixels in an image are interpolated by function of its nearest values using B-template with Gaussian filter. We can reconstruct analog image which has missing values into digital image by using this framework. We evaluated our new proposed method with six standard images which have missing pixels at various percentages of missing values. The experimental results show that, by using sigma-delta modulation type of Discrete-Time Cellular Neural Networks, we can achieve a high peak signal-to-noise ratio for various image datasets and at different rates of missingness.

The sigma-delta cellular neural network (SD-CNN) is a novel framework of spatial domain sigma-delta modulation utilizing neuro dynamics. Also, it has signal reconstruction and noise shaping characteristics that are important sigma-delta properties. Although the noise shaping effect with the oversampling technique plays very important role for drastic quantization noise reduction in binary digital sequences, the conventional SD-CNN could not use it effectively since it can be thought that the time-domain and spatial-domain oversampling are effective for the SD-CNN. In this paper, a novel SD-CNN with the oversampling technique for an analogue DC input is proposed. Experimental results of various standard test images in several oversampling ratios suggest that the proposed oversampling SD-CNN has an excellent AD and DA performance.

In advance of network communication society by the Internet, the way how to send data fast with a little loss has become an important transportation problem. A generalized maximum flow algorithm provides the best solution to the transportation problem of determining which route is appropriated to exchange data. Therefore, the importance of the maximum flow algorithm continues to grow. In this paper, we propose a Maximum-Flow Neural Network (MF-NN) in which branch nonlinearity has a saturation characteristic and by which the maximum flow problem can be solved with analog high-speed parallel processing. Moreover, the stability of proposed network is discussed. The proposed neural network for the maximum flow problem can be achieved by using a nonlinear resistive circuit where each connection weight between nodal neurons has a sigmoidal function. The parallel hardware of the MF-NN will be easily implemented.

In this paper, a novel spatial domain sigma-delta modulation using two-layered discrete-time cellular neural networks (DT-CNNs) is proposed. Since the nature of CNN dynamics with the output function which has two saturation regions is to binarize the input image, the dynamics has a capabilities for a digital image halftoning. In the proposed architecture, the nonlinear interpolative dynamics is exploited to obtain an optimal reconstruction image from the bilevel modulated image, and quantization noises are spatially distributed by the noise shaping property of the dynamics. The experimental results show a excellent reconstruction performance and capabilities of the CNN as a sigma-delta modulation.

Although the nonlinear interpolative dynamics of discrete-time cellular neural network (DT-CNN) is an effective method for prediction-based image coding schemes such like the lifting wavelet, the iterations of CNN dynamics are a bottleneck of processing time. This paper presents a novel lossless parallel image coding method based on lifting scheme using DT-CNNs. In the proposed method, split steps of the lifting scheme are extended in order to achieve fast image compression by parallel processing, and the subsampled image is interpolated by using the nonlinear interpolative dynamics of DT-CNN. Since the output function of DT-CNN works as a multi-level quantization function, the proposed method composes the integer lifting scheme for lossless coding. The experimental results show that the processing cost is greatly reduced by the proposed coding scheme

In this study, a novel way of processing observations before independent component analysis (ICA) using a Q-mode factor analysis (FA) was proposed for noisy blind source separation (BSS). The Q-mode analysis is a very efficient technique in classifying a data in cases where there are a large number of objects and where there is a little prior knowledge of the constituents. In the R-mode analyses, interrelationships between variables are analyzed. On the other hand, in the Q-mode analysis, interrelationships between objects are analyzed. Applying this approach to the experimental noisy data, we show that our proposed approach is more effective than the R-mode analysis for source separation of noisy data.

The lifting scheme is a flexible method for the construction of linear and nonlinear wavelet transforms. In this paper, we propose a novel lossless high dynamic range (HDR) image coding method based on the lifting scheme using discrete-time cellular neural networks (DT-CNNs). In our proposed method, the image is interpolated by using the nonlinear interpolative dynamics of DT-CNN. Because the output function of DT-CNN works as a multi-level quantization function, our method adapts for the prediction of HDR image, and composes the integer lifting scheme for lossless coding. Moreover, our method makes good use of the nonlinear interpolative dynamics by A-template compared with conventional CNN image coding methods using only B-template. The experimental results show a better coding performance compared with the conventional lifting method using linear filters.

An adaptive bandsplitting tree representation using wavelet packet or adaptive subband was exploited by various effective image coding methods. Unlike the works in the literature, in this paper we propose a cost function that includes both rate and distortion considering the spatial dependency of cross-subband coefficients. The spatial tree for octave-decomposed wavelet coefficients is expanded for the arbitrarily bandsplitted subbands, and the spatial tree is defined using the rearranged coefficients. Finally, experimental results for some images show that it generally tends to be effective to octave-decompose the lower frequency components and to uniform-decompose the higher frequency components and that our coder gives a better coding efficiency compared with various coding method previously reported.

Blind image deconvolution has possibility in various applications, for example, medical imaging, astronomical imaging, and one-dimensional gamma-ray spectra processing. In this paper, enhancing the ability of the NAS-RIF algorithm, which is known as a powerful blind deconvolution method, is provided. To improve the recovery ability, the cost function of the optimization routine of the NAS-RIF algorithm is redefined, and by incorporating interband prediction, the computational efficiency of the NAS-RIF algorithm is greatly improved.

We propose hysteresis neural networks for solving NP-hard problems, Traveling Salesperson Problems (TSP). Since hysteresis neural networks have no local minimum, they can be applied to various optimization problems. However, since conventional system for solving TSP which is proposed by Hopfield and Tank (1986) is not adaptive for hysteresis neural networks, they have not yet been applied to TSP. So, we propose a novel system for solving TSP. We obtain an improved result for the TSP with the proposed system based on hysteresis neural networks.

The image intensity conversion via CNN is presented. The intensity conversion is defined as a nonlinear optimization problem, and the templates of CNN for solving it are optimally designed. Since human visual sensitivity and linear quantization of original image are used to design the templates, it gives a smooth image preserving edge information such as character parts.

This paper describes dynamics of a associative model CNN-AM (Cellular Neural Network type of Associative Memory), which consists of combinations of Cellular Neural Network and Competitive Network. We prove that the CNN-AM makes more efficient improvement of the associative ability and the connection capacity rather than a orthogonal projection type of neural network.

In this paper, a multi-layer pattern structural compression method, a method memorizing this pattern in a multi-layer weighted resister network and a method associating the memorized pattern are proposed. Simulation results show the efficiency of these methods.>

This paper describes a novel image binarization/reconstruction system between natural and halftoning images, based on a non-linear large resistive network, which realizes a simple low level retina and image recognition. >

We have developed shortest path searching using a local current comparison method based on retinal information processing and analog dynamics. We will discuss applications of this method to robot walking and show some simulation results.>

In this paper, the chaotic behavior which occurs in a CMOS inverter ring is described. A ring having three CMOS inverters and operating with a discrete time model has been used in this study. The chaotic behavior has been found in the presented inverters ring along with a variation of an external input. A circuit model for the presented inverters ring is proposed and confirmed by employing SPICE circuit simulator as well as by observing a breadboard composed of discrete components.>

This paper describes dynamic depth extraction for binocular stereo visual information by CNN (cellular neural network). The quantization for the funneling information is done by parallel neurons. And, the correspondence problem can be solved by pattern recognition for analog images reconstructed from the transmitted funneling halftoning images. The competitive CNN is used. The computer simulation will show the verification for dynamic extraction process for analog stereo images.>

This paper describes new ways to construct a rewritable programmable logic array (R-PLA) of current mode logic (CML) and to control READ/WRITE operations of the R-PLA. The R-PLA is constructed by splitting a conventional Random Access Memory (RAM) of CML into two parts. Therefore, each cell structure of the new R-PLA is identified with that of the conventional RAM, differing from a complicated cell structure proposed in the past. Because of the identification the comparison for the number of cells between the new R-PLA and the RAM becomes possible according to the historical discussions of the PLA and the memory. It will be demonstrated logically and electrically that SEARCH and READ parts of the new R-PLA can perform logic-in-memory without using special AND gates in each cell in the READ mode and can enter a WRITE data from a word direction in the WRITE mode.