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  • 1
    ISSN: 1545-9985
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Medicine
    Notes: [Auszug] 'Superantigens' (SAgs) trigger the massive activation of T cells by simultaneous interactions with MHC and TCR receptors, leading to human diseases. Here we present the first crystal structure, at 2.5-Å resolution, of a complete ternary complex between a SAg and its two receptors, HLA-DR1/HA ...
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1748-7692
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology
    Notes: Seven hundred and twenty base pairs (bp) of the mitochondrial control region from 73 finless porpoises, Neophocaena phocaenoides, in Chinese waters were sequenced. Thirteen variable sites were determined and 17 haplotypes were defined. Of these, 5 and 7 were found only in the Yellow Sea population and the South China Sea population, respectively, whereas no specific haplo-type was found in the Yangtze River population. Phylogenetic analyses using NJ and ML algorithm did not divide the haplotypes into monophyletic clades representing recognized geographic populations of finless porpoises in Chinese waters, suggesting the existence of migration and gene flow among populations. Analysis of molecular variance showed the obvious population genetic structure (φst= 0.41, P 〈 0.05); however, the structure was mainly between either the Yangtze River population or the Yellow Sea population and the South China Sea population. The genetic diversity (nucleotide diversity and haplotypic diversity) of the Yellow Sea population was significantly higher than those of the Yangtze River population and the South China Sea population, suggesting the relatively later divergence of the latter two populations and supporting the Yellow Sea population as the original center of Neophocaena.
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 0192-8651
    Keywords: pseudospectral ; parallel ; Hartree-Fock ; gradient ; scalable ; Chemistry ; Theoretical, Physical and Computational Chemistry
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Computer Science
    Notes: We present an outline of the parallel implementation of our pseudospectral electronic structure program, Jaguar, including the algorithm and timings for the Hartree-Fock and analytic gradient portions of the program. We also present the parallel algorithm and timings for our Lanczos eigenvector refinement code and demonstrate that its performance is superior to the ScaLAPACK diagonalization routines. The overall efficiency of our code increases as the size of the calculation is increased, demonstrating actual as well as theoretical scalability. For our largest test system, alanine pentapeptide [818 basis functions in the cc-pVTZ(-f) basis set], our Fock matrix assembly procedure has an efficiency of nearly 90% on a 16-processor SP2 partition. The SCF portion for this case (including eigenvector refinement) has an overall efficiency of 87% on a partition of 8 processors and 74% on a partition of 16 processors. Finally, our parallel gradient calculations have a parallel efficiency of 84% on 8 processors for porphine (430 basis functions).   © 1998 John Wiley & Sons, Inc.   J Comput Chem 19: 1017-1029, 1998
    Additional Material: 7 Ill.
    Type of Medium: Electronic Resource
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  • 4
    Publication Date: 2018-05-25
    Description: Sensors, Vol. 18, Pages 1705: Spectral Kurtosis Entropy and Weighted SaE-ELM for Bogie Fault Diagnosis under Variable Conditions Sensors doi: 10.3390/s18061705 Authors: Zhipeng Wang Limin Jia Linlin Kou Yong Qin Bogies are crucial for the safe operation of rail transit systems and usually work under uncertain and variable operating conditions. However, the diagnosis of bogie faults under variable conditions has barely been discussed until now. Thus, it is valuable to develop effective methods to deal with variable conditions. Besides, considering that the normal data for training are much more than the faulty data in practice, there is another problem in that only a small amount of data is available that includes faults. Concerning these issues, this paper proposes two new algorithms: (1) A novel feature parameter named spectral kurtosis entropy (SKE) is proposed based on the protrugram. The SKE not only avoids the manual post-processing of the protrugram but also has strong robustness to the operating conditions and parameter configurations, which have been validated by a simulation experiment in this paper. In this paper, the SKE, in conjunction with variational mode decomposition (VMD), is employed for feature extraction under variable conditions. (2) A new learning algorithm named weighted self-adaptive evolutionary extreme learning machine (WSaE-ELM) is proposed. WSaE-ELM gives each sample an extra sample weight to rebalance the training data and optimizes these weights along with the parameters of hidden neurons by means of the self-adaptive differential evolution algorithm. Finally, the hybrid method based on VMD, SKE, and WSaE-ELM is verified by using the vibration signals gathered from real bogies with speed variations. It is demonstrated that the proposed method of bogie fault diagnosis outperforms the conventional methods by up to 4.42% and 6.22%, respectively, in percentages of accuracy under variable conditions.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by MDPI Publishing
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  • 5
    Publication Date: 2018-01-22
    Description: Entropy, Vol. 20, Pages 73: Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD Entropy doi: 10.3390/e20010073 Authors: Zhipeng Wang Limin Jia Yong Qin Rotating machineries often work under severe and variable operation conditions, which brings challenges to fault diagnosis. To deal with this challenge, this paper discusses the concept of adaptive diagnosis, which means to diagnose faults under variable operation conditions with self-adaptively and little prior knowledge or human intervention. To this end, a novel algorithm is proposed, information geometrical extreme learning machine with kernel (IG-KELM). From the perspective of information geometry, the structure and Riemannian metric of Kernel-ELM is specified. Based on the geometrical structure, an IG-based conformal transformation is created to improve the generalization ability and self-adaptability of KELM. The proposed IG-KELM, in conjunction with variation mode decomposition (VMD) and singular value decomposition (SVD) is utilized for adaptive diagnosis: (1) VMD, as a new self-adaptive signal processing algorithm is used to decompose the raw signals into several intrinsic mode functions (IMFs). (2) SVD is used to extract the intrinsic characteristics from the matrix constructed with IMFs. (3) IG-KELM is used to diagnose faults under variable conditions self-adaptively with no requirement of prior knowledge or human intervention. Finally, the proposed method was applied on fault diagnosis of a bearing and hydraulic pump. The results show that the proposed method outperforms the conventional method by up to 7.25% and 7.78% respectively, in percentages of accuracy.
    Electronic ISSN: 1099-4300
    Topics: Chemistry and Pharmacology , Physics
    Published by MDPI Publishing
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