Primary biliary cirrhosis: symptoms, laboratory data and diagnosis

The most frequent and important symptoms in primary biliary cirrhosis (PBC) are pruritus and jaundice. In the majority, jaundice appears within one half to two years of onset of pruritus. Xanthoma, bone change and sicca syndrome are often observed. The recent increase in number of asymptomatic PBC is the result of progagation of medical examination. In the biochemical tests, elevation of the biliary enzymes, such as ALP and gamma-GTP, is characteristic, as well as, a high level of serum IgM. Serum bilirubin values are gradually raised along with the clinical course of the disease. Positive antimitochondrial antibody (AMA) is the most valuable immunological finding for the diagnosis of PBC. Anti-pyruvate dehydrogenase complex E2 component E2 has the same significance as AMA. Diagnostic criteria si shown in this paper.     

Taxonomy for protective ability of rust layer using its composition formed on weathering steel bridge

For a quantitative evaluation of the protectiveness of a rust layer formed on a weathering steel bridge, the relationship between the corrosion rate of the bridge and the composition of the rust layers formed on the girders was first investigated. These corrosion rates were clearly classified by the protective ability index (PAI) of α/γ^∗ and (β + s)/γ^∗, where α, γ^∗, β and s are the mass ratio of crystalline α-FeOOH, the total of γ-FeOOH, β-FeOOH and the spinel-type iron oxide (mainly Fe₃O₄), β-FeOOH and spinel-type iron oxide, analyzed by XRD, respectively. The inequality of the former index α/γ^∗ > 1 expressed the protectiveness criterion of the rust layer, while that of the latter index, (β + s)/γ^∗< 0.5 or > 0.5, classified the corrosion rate of the non-protective rust layer. The PAI is useful for a quantitative evaluation of the protectiveness of a rust layer formed on a weathering steel bridge and is an important item for the corrosion assessment of the bridge.     

Input information maximization for improving self-organizing maps

In this paper, we propose a new type of information-theoretic method called “input information maximization” to improve the performance of self-organizing maps. We consider outputs from input neurons by focusing on winning neurons. The outputs are based on the difference between input neurons and the corresponding winning neurons. Then, we compute the uncertainty of input neurons by normalizing the outputs. Input information is defined as a decrease in the uncertainty of input neurons from a maximum and observed value. When input information increases, fewer input neurons tend to be activated. In the maximum state, only one neuron is on, and all others are off. We applied the method to two data sets, namely, the Senate and voting attitude data sets. In both, experimental results confirmed that when input information increased, quantization and topographic errors decreased. In addition, clearer class structure could be extracted by increasing input information. In comparison to our previous methods to detect the importance of input neurons, the present method turned out to be good at producing faithful representations with much more simplified computational procedures.     

Electrical and structural properties of ortho-chloranil doped zinc phthalocyanine films

Electrical conductivity of evaporated zinc phthalocyanine (ZnPc) films, doped with orthochloranil (o-CA) molecules, is investigated. Benzoico-CA solution is introduced in the film and benzene is removed by vapourization afterwards. The film conductivity at first increased and then decreased with incremental doping. The maximum conductivity of the sample appeared at doping level × (=o-CA/ZnPc) 0.2 and is about 10³ times as large as that of the as-evaporated one. About a ten-fold further increase in the conductivity is obtained by the heat-treatment of 150 °C for 12 h. The conductivity change by doping is explained by taking into account thato-CA molecules behave not only as acceptors in the film but destructively for the film structure. The phase change of ZnPc by benzoic solution is also discussed.     

Information-Theoretic Competitive Learning with Inverse Euclidean Distance Output Units

In this paper, we propose a new information theoretic competitive learning method. We first construct a learning method in single-layered networks, and then we extend it to supervised multi-layered networks. Competitive unit outputs are computed by the inverse of Euclidean distance between input patterns and connection weights. As distance is smaller, competitive unit outputs are stronger. In realizing competition, neither the winner-take-all algorithm nor the lateral inhibition is used. Instead, the new method is based upon mutual information maximization between input patterns and competitive units. In maximizing mutual information, the entropy of competitive units is increased as much as possible. This means that all competitive units must equally be used in our framework. Thus, no under-utilized neurons or dead neurons are generated. When using multi-layered networks, we can improve noise-tolerance performance by unifying information maximization and minimization. We applied our method with single-layered networks to a simple artificial data problem and an actual road classification problem. In both cases, experimental results confirmed that the new method can produce the final solutions almost independently of initial conditions, and classification performance is significantly improved. Then, we used multi-layered networks, and applied them to a character recognition problem and a political data analysis. In these problem, we could show that noise-tolerance performance was improved by decreasing information content on input patterns to certain points.