Institution
National Institute of Advanced Industrial Science and Technology
Government•Tsukuba, Ibaraki, Japan•
About: National Institute of Advanced Industrial Science and Technology is a government organization based out in Tsukuba, Ibaraki, Japan. It is known for research contribution in the topics: Catalysis & Thin film. The organization has 22114 authors who have published 65856 publications receiving 1669827 citations. The organization is also known as: Sangyō Gijutsu Sōgō Kenkyū-sho.
Topics: Catalysis, Thin film, Carbon nanotube, Laser, Hydrogen
Papers published on a yearly basis
Papers
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TL;DR: The results suggested the capacity for selective adsorption of MIPi to be not only based on the isoelectric point (pI) and protein molecular weight, but also the characteristics of protein recognition cavities imprinted on base silica.
231 citations
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01 Dec 2005
TL;DR: WoLF PSORT is a major update to the PSORTII program, based on new sequence, which presents a new program for predicting protein subcellular localization from amino acid sequence.
Abstract: We present a new program for predicting protein subcellular localization from amino acid sequence. WoLF PSORT is a major update to the PSORTII program, based on new sequence
231 citations
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01 Nov 2017
TL;DR: This paper proposes and evaluates the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS), and compares two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM).
Abstract: In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully operational raw water purification plant. For both methods, we first train detectors using a log generated by SWaT operating under normal conditions. Then, we evaluate the performance of both methods using a log generated by SWaT operating under 36 different attack scenarios. We find that our DNN generates fewer false positives than our one-class SVM while our SVM detects slightly more anomalies. Overall, our DNN has a slightly better F measure than our SVM. We discuss the characteristics of the DNN and one-class SVM used in this experiment, and compare the advantages and disadvantages of the two methods.
231 citations
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TL;DR: In this article, the design of multiferroics and the strategy to enhance the DC and optical magnetoelectric effect are argued in terms of the toroidal moment, helical spin structure, and critical-state phase control.
231 citations
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TL;DR: The study demonstrates that upregulation of mortalin contributes significantly to tumorigenesis, and thus is a good candidate target for cancer therapy.
Abstract: Mortalin, also known as mthsp70/GRP75/PBP74, interacts with the tumor suppressor protein p53 and inactivates its transcriptional activation and apoptotic functions. Here, we examined the level of mortalin expression in a large variety of tumor tissues, tumor-derived and in vitro immortalized human cells. It was elevated in many human tumors, and in all of the tumor-derived and in vitro immortalized cells. In human embryonic fibroblasts immortalized with an expression plasmid for hTERT, the telomerase catalytic subunit, with or without human papillomavirus E6 and E7 genes, we found that subclones with spontaneously increased mortalin expression levels became anchorage-independent and acquired the ability to form tumors in nude mice. Furthermore, overexpression of mortalin was sufficient to increase the malignancy of breast carcinoma cells. The study demonstrates that upregulation of mortalin contributes significantly to tumorigenesis, and thus is a good candidate target for cancer therapy.
231 citations
Authors
Showing all 22289 results
Name | H-index | Papers | Citations |
---|---|---|---|
Takeo Kanade | 147 | 799 | 103237 |
Ferenc A. Jolesz | 143 | 631 | 66198 |
Michele Parrinello | 133 | 637 | 94674 |
Kazunari Domen | 130 | 908 | 77964 |
Hideo Hosono | 128 | 1549 | 100279 |
Hideyuki Okano | 128 | 1169 | 67148 |
Kurunthachalam Kannan | 126 | 820 | 59886 |
Shaobin Wang | 126 | 872 | 52463 |
Ajit Varki | 124 | 542 | 58772 |
Tao Zhang | 123 | 2772 | 83866 |
Ramamoorthy Ramesh | 122 | 649 | 67418 |
Kazuhito Hashimoto | 120 | 781 | 61195 |
Katsuhiko Mikoshiba | 120 | 866 | 62394 |
Qiang Xu | 117 | 585 | 50151 |
Yoshinori Tokura | 117 | 858 | 70258 |