Evaluation of Lysophospholipid Measurement in Cerebrospinal Fluid Samples using Liquid Chromatography–Tandem Mass Spectrometry

A quantification system for lysophospholipids (lysoPL) was developed, especially for blood samples, using liquid chromatography–tandem mass spectrometry (LC–MS/MS). However, the lysoPL measurement in cerebrospinal fluid (CSF) has not been validated. Therefore, the present study aimed to validate the lysoPL measurement using CSF samples and to elucidate the possible clinical significance of the lysoPL measurement in CSF. For the validation, we observed a good precision and linearity in a sample with high lysoPL levels. The concentrations of lysoPL changed after incubation but the changes were smaller than those observed for serum samples. Moreover, we observed that the CSF levels of 16:0, 18:0 lysophosphatidylcholine, and 18:0, 18:1, and 20:4 lysophosphatidic acid were significantly higher in subjects with central nervous system invasion caused by hematological malignancies or carcinoma than in subjects with no abnormal CSF test results. In conclusion, an LC–MS/MS quantification system for lysoPL in CSF might be useful and could be applied to clinical laboratory testing.     

Inhibition of char deposition using a particle bed in heating section of supercritical water gasification

Supercritical water gasification (SCWG) has attracted attention as a technology for utilizing wet biomass. We used a fluidized bed of alumina particles to prevent blockage of a SCWG reactor. A glucose solution was heated in the reactor with and without fluidized alumina particles. In the absence of alumina particles, char particles formed homogeneously in the reactor, but the use of a fluidized bed resulted in accumulation of char particles at the reactor’s exit rather than inside the reactor. Therefore, the fluidized bed was effective at preventing blockage of the reactor. However, the alumina particles did not remove deposits from the reactor’s walls. Instead, the fluidized bed caused larger char particles to form, preventing their adhesion to the reactor’s wall.     

Nanostructured poly(3-hexylthiophene-2,5-diyl) films with tunable dimensions through self-assembly with polystyrene

Poly(3-hexylthiophene-2,5-diyl) is among the most widely used conjugated polymers for opto-electronic applications. To enhance its properties, researchers have attempted to nanostructure this polymer using various processes including breath figure arrays, nanolithography and elaborated organic synthesis. We here demonstrate a simple process to nanostructure the conjugated polymer using self-assembly with polystyrene and selective removal of one of the phases. The influence of the molecular weight of each polymer on the thin film morphology was systematically studied by atomic force microscopy. Using this approach, we observe two types of nanostructure, namely, nanoporous and nanoisland structures, of which the dimensions can be tuned by modifying the molecular weight of each polymer in the blend. This simple process introduces a cost-effective alternative to produce thin films of conjugated polymer with average nano-features from 100 nm up to 500 nm which could be used in a wide range of applications.     

Evaluation of robust control by universal learning networks

The characteristics of control system design using a universal learning network (ULN) are such that both the controlled systems and their controller are represented in a unified framework, and that the learning stage of the ULN can be executed by using not only first-order derivatives (gradient) but also the higher order derivatives of the criterion function with respect to parameters. ULNs have the same generalization ability as neural networks. So the ULN controller is able to control the system in a favorable way under an environment which is little different from the environment of the control system at the learning stage. However, stability cannot be sufficiently realized. In this paper, we propose a robust control method using a ULN and second-order derivatives of that ULN. Robust control, as considered here, is defined as follows. Even though the initial values of the node outputs are very different from those at the learning stage, the control system is able to reduce its influence to other node outputs and can control the system as in the case of no variation. In order to realize such robust control, a new term concerning the variation is added to the usual criterion function, and the parameters are adjusted so as to minimize the above-mentioned criterion function using second-order derivatives of the criterion function with respect to the parameters. Finally, it is shown that the ULN controller constructed by the proposed method works effectively in a simulation study of a non-linear crane system. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

Asymptotic statistical theory of overtraining and cross-validation

A statistical theory for overtraining is proposed. The analysis treats general realizable stochastic neural networks, trained with Kullback-Leibler divergence in the asymptotic case of a large number of training examples. It is shown that the asymptotic gain in the generalization error is small if we perform early stopping, even if we have access to the optimal stopping time. Based on the cross-validation stopping we consider the ratio the examples should be divided into training and cross-validation sets in order to obtain the optimum performance. Although cross-validated early stopping is useless in the asymptotic region, it surely decreases the generalization error in the nonasymptotic region. Our large scale simulations done on a CM5 are in good agreement with our analytical findings.     

Cylindrical coils created with 3D X-ray lithography and metallization

The demand for microactuators is increasing recently. The key technology to realizing practical microactuators is microfabrication process. In the production of microminiature components, the technologies for processing high-aspect-ratio structures are essential. As one of these technologies, the LIGA process is widely known. Our laboratory researches the LIGA process to three-dimensional microfabrication and established the cylindrical-microcoil production process. In this paper, we have fabricated the cylindrical-microcoil for the solenoidal electromagnetic type microactuator. We designed and analyzed microactuators, and fabricated and evaluated microactuator coils produced by the combination of three-dimensional X-ray lithography and level copper plating. We succeeded in creating threaded groove-shaped structures with 10 μm line width, 20 μm pitch, and aspect ratio of 5 on the surface of an acrylic pipe by means of three-dimensional X-ray lithography. As a measure to suppress void generation, which is one of the shortcomings of electrolytic plating processes, the sputtering apparatus and plating equipment were improved, a pretreatment process was additionally provided, and the actual electrolytic plating method was improved. As a result, a void-free metallic deposit could be formed on a thin coil line. The processing technology enables the formation of thin-wire coil lines whose current paths feature a large allowable current-carrying capacity, enabling the production of miniature, high-output microactuators.     

Network information criterion-determining the number of hiddenunits for an artificial neural network model

The problem of model selection, or determination of the number of hidden units, can be approached statistically, by generalizing Akaike's information criterion (AIC) to be applicable to unfaithful (i.e., unrealizable) models with general loss criteria including regularization terms. The relation between the training error and the generalization error is studied in terms of the number of the training examples and the complexity of a network which reduces to the number of parameters in the ordinary statistical theory of AIC. This relation leads to a new network information criterion which is useful for selecting the optimal network model based on a given training set.     

Color‐conversion method for a multi‐primary display to reduce power consumption and conversion time

Abstract— A color‐conversion method for a light‐emitting multi‐primary‐color display is proposed. While amulti‐primary‐color display uses four or more primary colors to reproduce a wide color gamut, multiple sets of primary‐color signals are needed to reproduce one color. Therefore, linear programming, which results in low power consumption, was adopted to uniquely determine the set of primary‐color signals. Although a highly accurate color conversion was achieved by using linear programming with low power consumption, it requires a very long time to convert colors of high‐resolution images. Therefore, by categorizing the color conversion of linear programming as a classification problem, colors are converted by using the decision‐tree method, which is a classification method. As a result, color conversion with high accuracy, low power consumption, and short conversion time was achieved.     

Development of ‘UR-System 2’: a training system for recovery of motor function of plegic upper limb after stroke

We developed a new training system, called ‘UR-System 2’, for the recovery of motor function of a plegic upper limb after stroke. UR-System 2, which was redesigned based on knowledge gained from clinical tests using UR-System 1, was newly equipped with a function for testing the motor function of patients, such as isolated movement. In order to verify the effectiveness of UR-System 2, we conducted the following procedures: we verified the safety of training in healthy persons; we gathered standard data on pronation and supination of healthy forearms during tests; and, we verified that the test function was effective in evaluating the recovery effect of isolated movement in two hemiplegic patients.