Quantitative determination of cardiac glycosides in Digitalis lanata leaves by reversed-phase thin-layer chromatography

We have recently chosen to undertake a comprehensive evaluation of the modulation of gene expression by oxidative stress, using mRNA as a marker. Our model system is HA-1 hamster fibroblasts, using conditions under which we observe an adaptive response. Under these conditions, the HA-1 cells respond to a minimally toxic "pretreatment" dose of hydrogen peroxide by synthesizing RNAs and proteins that protect them against subsequent exposure to a highly cytotoxic concentration of hydrogen peroxide. Using the differential display technique to screen for modulated RNAs, we have recently reported an RNA species, adapt15/gadd7, whose steady-state level is significantly induced by a pretreatment dose of hydrogen peroxide (D. R. Crawford, G. P. Schools, S. L. Salmon, and K. J. A. Davies (1996) Arch. Biochem. Biophys. 325, 256-264). Here we report a second induced mRNA, designated adapt33. Two homologous adapt33 mRNAs were revealed by Northern blot hybridization. Both of these species were inducible by hydrogen peroxide, and they were sized at 1.46 and 0.99 kb. These inductions appeared to be dependent upon calcium, occurred as early as 90 min, and were maximal at 5 h. Cell fractionation revealed that a significant proportion of adapt33 RNA is associated with active translation. adapt33 is a novel sequence, as determined by cloning, sequencing, and GenBank analysis. adapt33 represents a new oxidant-inducible RNA and marker of cellular oxidative stress and a potential aid in the study, detection, and possible therapy of oxidant-related disorders.     

Brain-like Computing Based on Distributed Representations and Neurodynamics

A key to overcoming the limitations of classical artificial intelligence and to deal well with enormous amounts of information might be brain-like computing in which distributed representations of information are processed by dynamical systems without using symbols. We present a method for such computing. We constructed an inference system using a nonmonotone neural network, which is a kind of recurrent neural network with continuous-time dynamics. This system deduces a conclusion according to state transitions of the network in which knowledge is embedded as trajectory attractors. It has the powerful ability of analogical reasoning without special treatment for exceptional knowledge. We also propose a method of linking different neurodynamical systems and show that two mutually interacting systems can process complex spatiotemporal patterns.     

Universality Issues in Reversible Computing Systems and Cellular Automata (Extended Abstract)

In this survey, we deal with the problem how a universal computer can be constructed in a reversible environment. We discuss this problem based on the frameworks of reversible Turing machines, reversible logic circuits, and reversible cellular automata. We can see that in spite of the constraint of reversibility, there are several very simple reversible systems that have universal computing ability.     

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.     

Optimally designing caches to reduce P2P traffic

Traffic caused by P2P services dominates a large part of traffic on the Internet and imposes significant loads on the Internet, so reducing P2P traffic within networks is an important issue for ISPs. In particular, a huge amount of traffic is transferred within backbone networks; therefore reducing P2P traffic is important for transit ISPs to improve the efficiency of network resource usage and reduce network capital cost. To reduce P2P traffic, it is effective for ISPs to implement cache devices at some router ports and reduce the hop length of P2P flows by delivering the required content from caches. However, the design problem of cache locations and capacities has not been well investigated, although the effect of caches strongly depends on the cache locations and capacities. We propose an optimum design method of cache capacity and location for minimizing the total amount of P2P traffic based on dynamic programming, assuming that transit ISPs provide caches at transit links to access ISP networks. We apply the proposed design method to 31 actual ISP backbone networks and investigate the main factors determining cache efficiency. We also analyze the property of network structure in which deploying caches are effective in reducing P2P traffic for transit ISPs. We show that transit ISPs can reduce the P2P traffic within their networks by about 50-85% by optimally designing caches at the transit links to the lower networks.     

Translational lemmas for DLOGTIME-uniform circuits,alternating TMs,and PRAMs

We present translational lemmas for the three standard models of parallel computation, and apply them to obtain tight hierarchy results. It is shown that, for arbitrarily small rational constant , (i) there is a language which can be accepted by a -uniform circuit family of depth and size but not by any -uniform circuit family of depth and size , (ii) there is a language which can be accepted by a -time -space ATM with l worktapes but not by any -time -space ATM with the same l worktapes if the number of tape symbols is fixed, and (iii) there is a language which can be accepted by a -time PRAM with processors but not by any -time PRAM with processors. Here, c > 0, d ≥ 1, r ₁ > 1, and r ₂ ≥ 1 are arbitrary rational constants, and l ≥ 2 is an arbitrary integer. Preliminary versions of different parts of this paper appeared in Proc. MCU 2004 (LNCS 3354) and Proc. FCT 2005 (LNCS 3623).     

Selectivity and stability via dendritic nonlinearity

Inspired by recent studies regarding dendritic computation, we constructed a recurrent neural network model incorporating dendritic lateral inhibition. Our model consists of an input layer and a neuron layer that includes excitatory cells and an inhibitory cell; this inhibitory cell is activated by the pooled activities of all the excitatory cells, and it in turn inhibits each dendritic branch of the excitatory cells that receive excitations from the input layer. Dendritic nonlinear operation consisting of branch-specifically rectified inhibition and saturation is described by imposing nonlinear transfer functions before summation over the branches. In this model with sufficiently strong recurrent excitation, on transiently presenting a stimulus that has a high correlation with feed- forward connections of one of the excitatory cells, the corresponding cell becomes highly active, and the activity is sustained after the stimulus is turned off, whereas all the other excitatory cells continue to have low activities. But on transiently presenting a stimulus that does not have high correlations with feedforward connections of any of the excitatory cells, all the excitatory cells continue to have low activities. Interestingly, such stimulus-selective sustained response is preserved for a wide range of stimulus intensity. We derive an analytical formulation of the model in the limit where individual excitatory cells have an infinite number of dendritic branches and prove the existence of an equilibrium point corresponding to such a balanced low-level activity state as observed in the simulations, whose stability depends solely on the signal-to-noise ratio of the stimulus. We propose this model as a model of stimulus selectivity equipped with self-sustainability and intensity-invariance simultaneously, which was difficult in the conventional competitive neural networks with a similar degree of complexity in their network architecture. We discuss the biological relevance of the model in a general framework of computational neuroscience.     

Study of Superconducting Fault Current Limiter Using Vacuum Interrupter Driven by Electromagnetic Repulsion Force for Commutating Switch

Using a high-temperature superconductor, we constructed and tested a model superconducting fault current limiter (SFCL). The superconductor and the vacuum interrupter as the commutation switch were connected in parallel using a bypass coil. When the fault current flows in this equipment, the superconductor is quenched and the current is transferred to the parallel coil because of the voltage drop in the superconductor. This large current in the parallel coil actuates the magnetic repulsion mechanism of the vacuum interrupter. Due to the opening of the vacuum interrupter, the current in the superconductor is broken. By using this equipment, the current flow time in the superconductor can be easily minimized. On the other hand, the fault current is also easily limited by large reactance of the parallel coil     

Using selective precipitant for uranyl ions — Fundamental studies for evaluating the precipitant performance

We have developed a simple reprocessing process for spent FBR fuels using N-cyclohexyl-2-pyrrolidone (NCP) which has selective precipitation ability for UO₂²⁺ ions. It was confirmed that NCP has sufficient precipitation ability for UO₂²⁺ ions, decontamination capability (separation of UO₂²⁺ from simulated fission products), and resistance to γ-ray radiation in nitric acid solutions. These findings indicate that NCP is applicable to our reprocessing process. We have also evaluated performances of other precipitants such as N-n-propyl-2-pyrrolidone (NProP), N-n-butyl-2-pyrrolidone (NBP), and N-n-butyl-2-pyridone (NBPyr). It was found that higher decontamination factors (DFs) are obtained by using NProP and NBP. This can be interpreted that the hydrophobicity of NProP and NBP is lower than that of NCP. Furthermore, we have obtained an experimental result that the resistance of NBPyr to γ-ray radiation is superior to that of NCP.     

Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions. Applications of artificial neural network

Artificial neural networks (ANNs) for predicting critical heat flux (CHF) under low pressure and oscillation conditions have been trained successfully for either natural circulation or forced circulation (FC) in the present study. The input parameters of the ANN are pressure, mean mass flow rate, relative amplitude, inlet subcooling, oscillation period and the ratio of the heated length to the diameter of the tube, L/D. The output is a nondimensionalized factor F, which expresses the relative CHF under oscillation conditions. Based on the trained ANN, the influences of principal parameters on F for FC were analyzed. The parametric trends of the CHF under oscillation obtained by the trained ANN are as follows: the effects of pressure below 500 kPa are complex due to the influence of other parameters. F will increase with increasing mean mass flow rate under any conditions, and will decrease generally with an increase in relative amplitude. F will decrease initially and then increase with increasing inlet subcooling. The influence curves of mean mass flow rate on F will be almost the same when the period is shorter than 5.0 s or longer than 15 s. The influence of L/D will be negligible if L/D>200. It is found that the minimum number of neurons in the hidden layer is a product of the number of neurons in the input layer and in the output layer.