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.

Classification of stable three-dimensional Dirac semimetals with nontrivial topology

Abstract: A three-dimensional (3D) Dirac semimetal (SM) is the 3D analogue of graphene having linear energy dispersion around Fermi points Owing to the nontrivial topology of electronic wave functions, the 3D Dirac SM shows nontrivial physical properties and hosts various exotic quantum states such as Weyl SMs and topological insulators under proper external conditions There are several kinds of Dirac SMs proposed theoretically and partly confirmed experimentally, but its unified picture is still missing Here we propose a general framework to classify stable 3D Dirac SMs in systems having the time-reversal, inversion and uniaxial rotational symmetries We show that there are two distinct classes of 3D Dirac SMs In one class, the Dirac SM possesses a single Dirac point (DP) at a time-reversal invariant momentum on the rotation axis Whereas the other class of Dirac SMs have a pair of DPs created by band inversion, and carry a quantized topological invariant

Identification of genetic networks from a small number of gene expression patterns under the Boolean network model.

Abstract: Liang, Fuhrman and Somogyi (PSB98, 18-29, 1998) have described an algorithm for inferring genetic network architectures from state transition tables which correspond to time series of gene expression patterns, using the Boolean network model. Their results of computational experiments suggested that a small number of state transition (INPUT/OUTPUT) pairs are sufficient in order to infer the original Boolean network correctly. This paper gives a mathematical proof for their observation. Precisely, this paper devises a much simpler algorithm for the same problem and proves that, if the indegree of each node (i.e., the number of input nodes to each node) is bounded by a constant, only O(log n) state transition pairs (from 2n pairs) are necessary and sufficient to identify the original Boolean network of n nodes correctly with high probability. We made computational experiments in order to expose the constant factor involved in O(log n) notation. The computational results show that the Boolean network of size 100,000 can be identified by our algorithm from about 100 INPUT/OUTPUT pairs if the maximum indegree is bounded by 2. It is also a merit of our algorithm that the algorithm is conceptually so simple that it is extensible for more realistic network models.

Membrane-Type 1 Matrix Metalloproteinase Cleaves Cd44 and Promotes Cell Migration

Abstract: Migratory cells including invasive tumor cells frequently express CD44, a major receptor for hyaluronan and membrane-type 1 matrix metalloproteinase (MT1-MMP) that degrades extracellular matrix at the pericellular region. In this study, we demonstrate that MT1-MMP acts as a processing enzyme for CD44H, releasing it into the medium as a soluble 70-kD fragment. Furthermore, this processing event stimulates cell motility; however, expression of either CD44H or MT1-MMP alone did not stimulate cell motility. Coexpression of MT1-MMP and mutant CD44H lacking the MT1-MMP–processing site did not result in shedding and did not promote cell migration, suggesting that the processing of CD44H by MT1-MMP is critical in the migratory stimulation. Moreover, expression of the mutant CD44H inhibited the cell migration promoted by CD44H and MT1-MMP in a dominant-negative manner. The pancreatic tumor cell line, MIA PaCa-2, was found to shed the 70-kD CD44H fragment in a MT1-MMP–dependent manner. Expression of the mutant CD44H in the cells as well as MMP inhibitor treatment effectively inhibited the migration, suggesting that MIA PaCa-2 cells indeed use the CD44H and MT1-MMP as migratory devices. These findings revealed a novel interaction of the two molecules that have each been implicated in tumor cell migration and invasion.

Submillimetre-wavelength detection of dusty star-forming galaxies at high redshift

Abstract: Optical surveys of the global star-formation rate in high-redshift galaxies show a strong peak in activity at a redshift of z ≈ 1.5, which implies that most of the star formation1 has already been seen. High-redshift galaxies may, however, emit most of their energy at submillimetre wavelengths, if they contain substantial amounts of dust that absorbs the starlight and reradiates it as far-infrared light. Here we report a deep survey of a blank region of sky, performed at submillimetre wavelengths (450 and 850 μm). We detect luminous sources in the 850-μm band which, if they have similar spectra to low-redshift ultraluminous infrared galaxies and are primarily powered by star formation, must each be converting more than 100 solar masses of gas per year into stars: this is larger than themaximum star-formation rates inferred for most optically selected galaxies2. The total amount of star formation at high redshifts is essentially fixed by the level of background light, but where the peak activity occurs at submillimetre wavelengths is not yet well established. However, the background light inferred from the sources that we have detected is already comparable to that from the optically selected sources. Establishing the main epoch of star formation will therefore require a combination of optical and submillimetre studies.