Two Cases of Acrodermatitis Continua of Hallopeau Associated with Generalized Arthritis

We report two cases of acrodermatitis continua of Hallopeau with generalized arthritis. Oral cyclosporin together with topical steroid were effective for the skin disorders, but not for the arthritis, whereas infliximab and salazosulfapyridine improved the general arthritis. Although arthritis is usually more refractory to conventional therapies, the arthritis in acrodermatitis continua of Hallopeau is likely to be improved by intravenous infliximab together with methotrexate as well as by oral salazosulfapyridine, similar to psoriatic arthritis

Application of a New Probabilistic Model for Mining Implicit Associated Cancer Genes from OMIM and Medline

An important issue in current medical science research is to find the genes that are strongly related to an inherited disease. A particular focus is placed on cancer-gene relations, since some types of cancers are inherited. As biomedical databases have grown speedily in recent years, an informatics approach to predict such relations from currently available databases should be developed. Our objective is to find implicit associated cancer-genes from biomedical databases including the literature database. Co-occurrence of biological entities has been shown to be a popular and efficient technique in biomedical text mining. We have applied a new probabilistic model, called mixture aspect model (MAM) [48], to combine different types of co-occurrences of genes and cancer derived from Medline and OMIM (Online Mendelian Inheritance in Man). We trained the probability parameters of MAM using a learning method based on an EM (Expectation and Maximization) algorithm. We examined the performance of MAM by predicting associated cancer gene pairs. Through cross-validation, prediction accuracy was shown to be improved by adding gene-gene co-occurrences from Medline to cancer-gene cooccurrences in OMIM. Further experiments showed that MAM found new cancer-gene relations which are unknown in the literature. Supplementary information can be found at http://www.bic.kyotou.ac.jp/pathway/zhusf/CancerInformatics/Supplemental2006.htm

A Modeling of Singing Voice Robust to Accompaniment Sounds and Its Application to Singer Identification and Vocal-Timbre-Similarity-Based Music Information Retrieval

This paper describes a method of modeling the characteristics of a singing voice from polyphonic musical audio signals including sounds of various musical instruments. Because singing voices play an important role in musical pieces with vocals, such representation is useful for music information retrieval systems. The main problem in modeling the characteristics of a singing voice is the negative influences caused by accompaniment sounds. To solve this problem, we developed two methods, accompaniment sound reduction and reliable frame selection . The former makes it possible to calculate feature vectors that represent a spectral envelope of a singing voice after reducing accompaniment sounds. It first extracts the harmonic components of the predominant melody from sound mixtures and then resynthesizes the melody by using a sinusoidal model driven by these components. The latter method then estimates the reliability of frame of the obtained melody (i.e., the influence of accompaniment sound) by using two Gaussian mixture models (GMMs) for vocal and nonvocal frames to select the reliable vocal portions of musical pieces. Finally, each song is represented by its GMM consisting of the reliable frames. This new representation of the singing voice is demonstrated to improve the performance of an automatic singer identification system and to achieve an MIR system based on vocal timbre similarity

Nonparametric Bayesian Dereverberation of Power Spectrograms Based on Infinite-Order Autoregressive Processes

This paper describes a monaural audio dereverberation method that operates in the power spectrogram domain. The method is robust to different kinds of source signals such as speech or music. Moreover, it requires little manual intervention, including the complexity of room acoustics. The method is based on a non-conjugate Bayesian model of the power spectrogram. It extends the idea of multi-channel linear prediction to the power spectrogram domain, and formulates a model of reverberation as a non-negative, infinite-order autoregressive process. To this end, the power spectrogram is interpreted as a histogram count data, which allows a nonparametric Bayesian model to be used as the prior for the autoregressive process, allowing the effective number of active components to grow, without bound, with the complexity of data. In order to determine the marginal posterior distribution, a convergent algorithm, inspired by the variational Bayes method, is formulated. It employs the minorization-maximization technique to arrive at an iterative, convergent algorithm that approximates the marginal posterior distribution. Both objective and subjective evaluations show advantage over other methods based on the power spectrum. We also apply the method to a music information retrieval task and demonstrate its effectiveness

Influence of Different Concentration of Tris Buffer Solution on Calcium Carbonate Precipitation in Bio-based Repair Materials

This research examined differences in the concentration of Tris buffer solution on calcium carbonate precipitation in bio-based repair materials. Four concentrations of Tris buffer solution were chosen based on previous research with initial pH 8.0 and 9.0. Initial experiments demonstrated that the concentration and pH of Tris buffer solution had an influence on the amount of precipitation of calcium carbonate. X-Ray Diffraction (XRD) analysis elucidated the morphological and structural differences of the calcium carbonate crystal, including calcite and vaterite. They are the prominent forms of CaCO3 detected based on results obtained by according to FT-IR analysis. The result further explains the effectiveness of Tris buffer concentration

Classification of Known and Unknown Environmental Sounds Based on Self-Organized Space Using a Recurrent Neural Network

Our goal is to develop a system to learn and classify environmental sounds for robots working in the real world. In the real world, two main restrictions pertain in learning. (i) Robots have to learn using only a small amount of data in a limited time because of hardware restrictions. (ii) The system has to adapt to unknown data since it is virtually impossible to collect samples of all environmental sounds. We used a neuro-dynamical model to build a prediction and classification system. This neuro-dynamical model can self-organize sound classes into parameters by learning samples. The sound classification space, constructed by these parameters, is structured for the sound generation dynamics and obtains clusters not only for known classes, but also unknown classes. The proposed system searches on the basis of the sound classification space for classifying. In the experiment, we evaluated the accuracy of classification for both known and unknown sound classes

Exciton localization and decomposition dynamics in cuprous halide nanocrystals

We report temporal changes of luminescence and absorption (differential transmission) of excitons in nanometer-size semiconductor crystals (nanocrystals) of CuCl embedded in NaCl or in glass, and CuBr nano- crystals embedded in glass. In CuCl nanocrystals in NaCl, an exciton relaxes nonradiatively to some localized state. In CuCl and CuBr nanocrystals in glass, the temporal changes of the differential transmission have a longer decay component in addition to the fast decay component which agrees with the luminescence decay. This result suggests exciton decomposition and the existence of an electron or a hole remainder in the nano- crystals. The decay time of the longer decay component increases by the accumulation of photoexcitation. This phenomenon indicates persistent trapping of carriers in the glass matrix, which is concerned with persistent spectral hole burning in nanocrystals