- 著者
- Shingo Ueno Tatsunori Hirai Shusuke Sato Manish Biyani Hiromi Kuramochi Ryo Iizuka Takanori Akagi Takashi Funatsu Takanori Ichiki
- 出版者
- The Society of Photopolymer Science and Technology (SPST)
- 雑誌
- Journal of Photopolymer Science and Technology (ISSN:09149244)
- 巻号頁・発行日
- vol.28, no.5, pp.719-725, 2015-06-22 (Released:2015-10-20)
- 参考文献数
- 12
- 被引用文献数
- 1 4
The improved catalytic activity of enzymes is required in various fields. Enzymes have conventionally been improved by the screening of bacteria possessing mutant enzymes. However, the screening conditions are limited since screening requires the growth of bacteria. Here, we report the development of a protein microarray for the analysis of enzymatic activity. A his-tagged enzyme is synthesized in situ and immobilized on the microarray, which is composed of microreactors with a diameter and depth of 4 μm and a density of 1.0 x 106 reactors/cm2. β-glucosidase, synthesized in situ using a cell-free synthesis system, was immobilized on the microreactor array chip and its catalytic activity was observed. This enzyme-immobilized microarray is expected to enable the rapid and quantitative screening of enzymes.
- 著者
- Tatsunori Hirai Hironori Doi Shigeo Morishima
- 雑誌
- 情報処理学会論文誌 (ISSN:18827764)
- 巻号頁・発行日
- vol.59, no.3, 2018-03-15
This paper presents a topic modeling method to retrieve similar music fragments and its application, Music-Mixer, which is a computer-aided DJ system that supports DJ performance by automatically mixing songs in a seamless manner. MusicMixer mixes songs based on audio similarity calculated via beat analysis and latent topic analysis of the chromatic signal in the audio. The topic represents latent semantics on how chromatic sounds are generated. Given a list of songs, a DJ selects a song with beats and sounds similar to a specific point of the currently playing song to seamlessly transition between songs. By calculating similarities between all existing song sections that can be naturally mixed, MusicMixer retrieves the best mixing point from a myriad of possibilities and enables seamless song transitions. Although it is comparatively easy to calculate beat similarity from audio signals, considering the semantics of songs from the viewpoint of a human DJ has proven difficult. Therefore, we propose a method to represent audio signals to construct topic models that acquire latent semantics of audio. The results of a subjective experiment demonstrate the effectiveness of the proposed latent semantic analysis method. MusicMixer achieves automatic song mixing using the audio signal processing approach; thus, users can perform DJ mixing simply by selecting a song from a list of songs suggested by the system.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.26(2018) (online)DOI http://dx.doi.org/10.2197/ipsjjip.26.276------------------------------