Tokyo Institute of Technology

About: Tokyo Institute of Technology is a education organization based out in Tokyo, Tôkyô, Japan. It is known for research contribution in the topics: Catalysis & Thin film. The organization has 46775 authors who have published 101656 publications receiving 2357893 citations. The organization is also known as: Tokyo Tech & Tokodai.

Resolution of distinct rotational substeps by submillisecond kinetic analysis of F1-ATPase.

Abstract: The enzyme F1-ATPase has been shown to be a rotary motor in which the central gamma-subunit rotates inside the cylinder made of alpha3beta3 subunits. At low ATP concentrations, the motor rotates in discrete 120 degrees steps, consistent with sequential ATP hydrolysis on the three beta-subunits. The mechanism of stepping is unknown. Here we show by high-speed imaging that the 120 degrees step consists of roughly 90 degrees and 30 degrees substeps, each taking only a fraction of a millisecond. ATP binding drives the 90 degrees substep, and the 30 degrees substep is probably driven by release of a hydrolysis product. The two substeps are separated by two reactions of about 1 ms, which together occupy most of the ATP hydrolysis cycle. This scheme probably applies to rotation at full speed ( approximately 130 revolutions per second at saturating ATP) down to occasional stepping at nanomolar ATP concentrations, and supports the binding-change model for ATP synthesis by reverse rotation of F1-ATPase.

Genome sequence of an industrial microorganism Streptomyces avermitilis: Deducing the ability of producing secondary metabolites

Abstract: Streptomyces avermitilis is a soil bacterium that carries out not only a complex morphological differentiation but also the production of secondary metabolites, one of which, avermectin, is commercially important in human and veterinary medicine. The major interest in this genus Streptomyces is the diversity of its production of secondary metabolites as an industrial microorganism. A major factor in its prominence as a producer of the variety of secondary metabolites is its possession of several metabolic pathways for biosynthesis. Here we report sequence analysis of S. avermitilis, covering 99% of its genome. At least 8.7 million base pairs exist in the linear chromosome; this is the largest bacterial genome sequence, and it provides insights into the intrinsic diversity of the production of the secondary metabolites of Streptomyces. Twenty-five kinds of secondary metabolite gene clusters were found in the genome of S. avermitilis. Four of them are concerned with the biosyntheses of melanin pigments, in which two clusters encode tyrosinase and its cofactor, another two encode an ochronotic pigment derived from homogentiginic acid, and another polyketide-derived melanin. The gene clusters for carotenoid and siderophore biosyntheses are composed of seven and five genes, respectively. There are eight kinds of gene clusters for type-I polyketide compound biosyntheses, and two clusters are involved in the biosyntheses of type-II polyketide-derived compounds. Furthermore, a polyketide synthase that resembles phloroglucinol synthase was detected. Eight clusters are involved in the biosyntheses of peptide compounds that are synthesized by nonribosomal peptide synthetases. These secondary metabolite clusters are widely located in the genome but half of them are near both ends of the genome. The total length of these clusters occupies about 6.4% of the genome.

Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation

Abstract: A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likelihood estimation are no longer consistent—weighted variants according to the ratio of test and training input densities are consistent. Therefore, accurately estimating the density ratio, called the importance, is one of the key issues in covariate shift adaptation. A naive approach to this task is to first estimate training and test input densities separately and then estimate the importance by taking the ratio of the estimated densities. However, this naive approach tends to perform poorly since density estimation is a hard task particularly in high dimensional cases. In this paper, we propose a direct importance estimation method that does not involve density estimation. Our method is equipped with a natural cross validation procedure and hence tuning parameters such as the kernel width can be objectively optimized. Simulations illustrate the usefulness of our approach.