Institution
University of Tokyo
Education•Tokyo, Japan•
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.
Topics: Population, Gene, Catalysis, Magnetic field, Magnetization
Papers published on a yearly basis
Papers
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TL;DR: The ability of insulin to lower the plasma glucose level in the HCV transgenic mice was impaired, as observed in chronic hepatitis C patients, providing a direct experimental evidence for the contribution of HCV in the development of insulin resistance in human HCV infection, which finally leads to theDevelopment of type 2 diabetes.
763 citations
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TL;DR: In this article, a thin layer of basalt was sandwiched between compressed blocks of peridotite minerals and then was equilibrated with its host at melting temperatures.
Abstract: The solidus comprises three curves, corresponding to subsolidus mineral assemblages with cusps at about 11 and 26 kbar. A thin layer of basalt was sandwiched between compressed blocks of powdered peridotite minerals and then was equilibrated with its host at melting temperatures. The basalt melt, was completely homogenized with the partial melt in the peridotite matrix within 24 hours. The role of K 2 O in the melting was investigated. Hypothesis of shallow-depth origin for MORBS is supported.--Modified journal abstract.
763 citations
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TL;DR: It is shown that adiponectin stimulates food intake and decreases energy expenditure during fasting through its effects in the central nervous system via its receptor AdipoR1 to stimulate food intake.
762 citations
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University of New Mexico1, Los Alamos National Laboratory2, KAIST3, Francis Crick Institute4, Katholieke Universiteit Leuven5, Wellcome Trust Sanger Institute6, National Health Service7, Kyoto University8, University of Tokyo9, University of Cambridge10, Medical Research Council11, King's College London12
TL;DR: The results are consistent with the proposition that smoking increases cancer risk by increasing the somatic mutation load, although direct evidence for this mechanism is lacking in some smoking-related cancer types.
Abstract: Tobacco smoking increases the risk of at least 17 classes of human cancer. We analyzed somatic mutations and DNA methylation in 5243 cancers of types for which tobacco smoking confers an elevated risk. Smoking is associated with increased mutation burdens of multiple distinct mutational signatures, which contribute to different extents in different cancers. One of these signatures, mainly found in cancers derived from tissues directly exposed to tobacco smoke, is attributable to misreplication of DNA damage caused by tobacco carcinogens. Others likely reflect indirect activation of DNA editing by APOBEC cytidine deaminases and of an endogenous clocklike mutational process. Smoking is associated with limited differences in methylation. The results are consistent with the proposition that smoking increases cancer risk by increasing the somatic mutation load, although direct evidence for this mechanism is lacking in some smoking-related cancer types.
762 citations
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TL;DR: A class of weight-setting methods for lazy learning algorithms which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less pre-processing, perform better in the presence of interacting features, and generally require less training data to learn good settings.
Abstract: Many lazy learning algorithms are derivatives of the k-nearest neighbor (k-NN) classifier, which uses a distance function to generate predictions from stored instances. Several studies have shown that k-NN‘s performance is highly sensitive to the definition of its distance function. Many k-NN variants have been proposed to reduce this sensitivity by parameterizing the distance function with feature weights. However, these variants have not been categorized nor empirically compared. This paper reviews a class of weight-setting methods for lazy learning algorithms. We introduce a framework for distinguishing these methods and empirically compare them. We observed four trends from our experiments and conducted further studies to highlight them. Our results suggest that methods which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less pre-processing, perform better in the presence of interacting features, and generally require less training data to learn good settings. We also found that continuous weighting methods tend to outperform feature selection algorithms for tasks where some features are useful but less important than others.
762 citations
Authors
Showing all 135252 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ronald C. Kessler | 274 | 1332 | 328983 |
Donald P. Schneider | 242 | 1622 | 263641 |
George M. Whitesides | 240 | 1739 | 269833 |
Jing Wang | 184 | 4046 | 202769 |
Tadamitsu Kishimoto | 181 | 1067 | 130860 |
Yusuke Nakamura | 179 | 2076 | 160313 |
Dennis J. Selkoe | 177 | 607 | 145825 |
David L. Kaplan | 177 | 1944 | 146082 |
D. M. Strom | 176 | 3167 | 194314 |
Masayuki Yamamoto | 171 | 1576 | 123028 |
Krzysztof Matyjaszewski | 169 | 1431 | 128585 |
Yang Yang | 164 | 2704 | 144071 |
Qiang Zhang | 161 | 1137 | 100950 |
Kenji Kangawa | 153 | 1117 | 110059 |
Takashi Taniguchi | 152 | 2141 | 110658 |