University of Tokyo

About: University of Tokyo is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Population & Gene. The organization has 134564 authors who have published 337567 publications receiving 10178620 citations. The organization is also known as: Todai & Universitas Tociensis.

Reproducibility in density functional theory calculations of solids

Abstract: The widespread popularity of density functional theory has given rise to an extensive range of dedicated codes for predicting molecular and crystalline properties. However, each code implements the formalism in a different way, raising questions about the reproducibility of such predictions. We report the results of a community-wide effort that compared 15 solid-state codes, using 40 different potentials or basis set types, to assess the quality of the Perdew-Burke-Ernzerhof equations of state for 71 elemental crystals. We conclude that predictions from recent codes and pseudopotentials agree very well, with pairwise differences that are comparable to those between different high-precision experiments. Older methods, however, have less precise agreement. Our benchmark provides a framework for users and developers to document the precision of new applications and methodological improvements.

Modulation of microRNA processing by p53

Abstract: MicroRNAs (miRNAs) have emerged as key post-transcriptional regulators of gene expression, involved in diverse physiological and pathological processes. Although miRNAs can function as both tumour suppressors and oncogenes in tumour development, a widespread downregulation of miRNAs is commonly observed in human cancers and promotes cellular transformation and tumorigenesis. This indicates an inherent significance of small RNAs in tumour suppression. However, the connection between tumour suppressor networks and miRNA biogenesis machineries has not been investigated in depth. Here we show that a central tumour suppressor, p53, enhances the post-transcriptional maturation of several miRNAs with growth-suppressive function, including miR-16-1, miR-143 and miR-145, in response to DNA damage. In HCT116 cells and human diploid fibroblasts, p53 interacts with the Drosha processing complex through the association with DEAD-box RNA helicase p68 (also known as DDX5) and facilitates the processing of primary miRNAs to precursor miRNAs. We also found that transcriptionally inactive p53 mutants interfere with a functional assembly between Drosha complex and p68, leading to attenuation of miRNA processing activity. These findings suggest that transcription-independent modulation of miRNA biogenesis is intrinsically embedded in a tumour suppressive program governed by p53. Our study reveals a previously unrecognized function of p53 in miRNA processing, which may underlie key aspects of cancer biology.

Criteria for Annotation of Plant MicroRNAs

Abstract: MicroRNAs (miRNAs) are ∼21 nucleotide noncoding RNAs produced by Dicer-catalyzed excision from stem-loop precursors. Many plant miRNAs play critical roles in development, nutrient homeostasis, abiotic stress responses, and pathogen responses via interactions with specific target mRNAs. miRNAs are not the only Dicer-derived small RNAs produced by plants: A substantial amount of the total small RNA abundance and an overwhelming amount of small RNA sequence diversity is contributed by distinct classes of 21- to 24-nucleotide short interfering RNAs. This fact, coupled with the rapidly increasing rate of plant small RNA discovery, demands an increased rigor in miRNA annotations. Herein, we update the specific criteria required for the annotation of plant miRNAs, including experimental and computational data, as well as refinements to standard nomenclature.

Featherweight Java: a minimal core calculus for Java and GJ

Abstract: Several recent studies have introduced lightweight versions of Java: reduced languages in which complex features like threads and reflection are dropped to enable rigorous arguments about key properties such as type safety. We carry this process a step further, omitting almost all features of the full language (including interfaces and even assignment) to obtain a small calculus, Featherweight Java, for which rigorous proofs are not only possible but easy. Featherweight Java bears a similar relation to Java as the lambda-calculus does to languages such as ML and Haskell. It offers a similar computational "feel," providing classes, methods, fields, inheritance, and dynamic typecasts with a semantics closely following Java's. A proof of type safety for Featherweight Java thus illustrates many of the interesting features of a safety proof for the full language, while remaining pleasingly compact. The minimal syntax, typing rules, and operational semantics of Featherweight Java make it a handy tool for studying the consequences of extensions and variations. As an illustration of its utility in this regard, we extend Featherweight Java with generic classes in the style of GJ (Bracha, Odersky, Stoutamire, and Wadler) and give a detailed proof of type safety. The extended system formalizes for the first time some of the key features of GJ.

The Galaxy Luminosity Function and Luminosity Density at Redshift z = 0.1*

Abstract: Using a catalog of 147,986 galaxy redshifts and fluxes from the Sloan Digital Sky Survey (SDSS), we measure the galaxy luminosity density at z = 0.1 in five optical bandpasses corresponding to the SDSS bandpasses shifted to match their rest-frame shape at z = 0.1. We denote the bands 0.1u, 0.1g, 0.1r, 0.1i, 0.1z with λeff = (3216, 4240, 5595, 6792, 8111 A), respectively. To estimate the luminosity function, we use a maximum likelihood method that allows for a general form for the shape of the luminosity function, fits for simple luminosity and number evolution, incorporates the flux uncertainties, and accounts for the flux limits of the survey. We find luminosity densities at z = 0.1 expressed in absolute AB magnitudes in a Mpc3 to be (-14.10 ± 0.15, -15.18 ± 0.03, -15.90 ± 0.03, -16.24 ± 0.03, -16.56 ± 0.02) in (0.1u, 0.1g, 0.1r, 0.1i, 0.1z), respectively, for a cosmological model with Ω0 = 0.3, ΩΛ = 0.7, and h = 1 and using SDSS Petrosian magnitudes. Similar results are obtained using Sersic model magnitudes, suggesting that flux from outside the Petrosian apertures is not a major correction. In the 0.1r band, the best-fit Schechter function to our results has * = (1.49 ± 0.04) × 10-2 h3 Mpc-3, M* - 5 log10 h = -20.44 ± 0.01, and α = -1.05 ± 0.01. In solar luminosities, the luminosity density in 0.1r is (1.84 ± 0.04) × 108 h L0.1r,☉ Mpc-3. Our results in the 0.1g band are consistent with other estimates of the luminosity density, from the Two-Degree Field Galaxy Redshift Survey and the Millennium Galaxy Catalog. They represent a substantial change (~0.5 mag) from earlier SDSS luminosity density results based on commissioning data, almost entirely because of the inclusion of evolution in the luminosity function model.