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the 12th letter of the Roman alphabet (同)l

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lira(イタリアの貨幣単位リラ)
lethal dose(薬の)最小致死量

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出典(authority):フリー百科事典『ウィキペディア（Wikipedia）』「2014/06/04 03:10:15」(JST)

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1. 腎移植におけるHLAとABOの感作および脱感作 hla and abo sensitization and desensitization in renal transplantation

English Journal

Geographical tracing of Xihu Longjing tea using high performance liquid chromatography.

Wang L, Wei K, Cheng H, He W, Li X, Gong W.Author information National Center for Tea Improvement, Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.AbstractXihu Longjing tea (XHLJ) is one of the most famous green tea in China. Due to its high price, some inauthentic XHLJ from other tea producing areas appear on the market and hurt the interests of customers and producers. High-performance liquid chromatography (HPLC) combined with the principal component analysis (PCA) and linear discriminant analysis (LDA) methods were applied to the geographical tracing of XHLJ from two other types of Longjing teas and a non-Longjing flatten-shaped tea (non-LJ). The chromatograms of the tea samples from four different regions were highly similar. It was difficult to classify 4 types of teas directly by PCA. However, high total accuracy rates of 94.8% and 87.6% for the training and test set were achieved for distinguishing XHLJ from the other three types of tea by stepwise discriminant analysis. The identification accuracy of XHLJ from non-LJ was the highest, suggesting geographical distance might play an important role in this process. In summary, a combination of chromatographic chemical fingerprints with LDA provides a simple and rapid approach for the identification of XHLJ from other teas.
Food chemistry.Food Chem.2014 Mar 1;146:98-103. doi: 10.1016/j.foodchem.2013.09.043. Epub 2013 Sep 19.
Xihu Longjing tea (XHLJ) is one of the most famous green tea in China. Due to its high price, some inauthentic XHLJ from other tea producing areas appear on the market and hurt the interests of customers and producers. High-performance liquid chromatography (HPLC) combined with the principal compone
PMID 24176319

Identification and functional analysis of the peptidoglycan recognition protein LD gene in the mosquito, Armigeres subalbatus.

Wang S, Beerntsen BT.Author information Department of Veterinary Pathobiology, University of Missouri, Columbia, MO 65211, United States. Electronic address: Wangso@missouri.edu.AbstractPeptidoglycan recognition proteins are important recognition proteins in many organisms ranging from echinoderms to humans. In an attempt to characterize all the PGRPs in the mosquito Armigeres subalbatus, two PGRP-LD isoforms, AsPGRP-LDa and AsPGRP-LDb, which are orthologs of the PGRP-LDs in several other insect species, were identified from this mosquito using homologous cloning. To date the functions of this PGRP gene have not yet been described in detail in other organisms with a known PGRP-LD gene. In the current study, we analyzed the sequences of these AsPGRP-LDs, their evolutionary relationships with their orthologs, their transcriptional expression in various developmental stages and different tissue samples, and their transcriptional responses to different bacterial stimuli. We then knocked down the expression of both AsPGRP-LDs by injection of double-stranded RNAs, and assessed the impact of AsPGRP-LD RNAi on mosquito survival after bacterial challenges and on the transcriptional expression of a number of antimicrobial peptides.
Developmental and comparative immunology.Dev Comp Immunol.2014 Feb;42(2):148-58. doi: 10.1016/j.dci.2013.08.025. Epub 2013 Sep 7.
Peptidoglycan recognition proteins are important recognition proteins in many organisms ranging from echinoderms to humans. In an attempt to characterize all the PGRPs in the mosquito Armigeres subalbatus, two PGRP-LD isoforms, AsPGRP-LDa and AsPGRP-LDb, which are orthologs of the PGRP-LDs in severa
PMID 24016699

Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface.

Zhang Y, Zhou G, Jin J, Zhao Q, Wang X, Cichocki A.Author information Key Laboratory for Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, China.AbstractTwo main issues for event-related potential (ERP) classification in brain-computer interface (BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may deteriorate classification performance, especially with insufficient training samples resulted from limited calibration time. This study introduces an aggregation of sparse linear discriminant analyses (ASLDA) to overcome these problems. In the ASLDA, multiple sparse discriminant vectors are learned from differently l1-regularized least-squares regressions by exploiting the equivalence between LDA and least-squares regression, and are subsequently aggregated to form an ensemble classifier, which could not only implement automatic feature selection for dimensionality reduction to alleviate curse-of-dimensionality, but also decrease the variance to improve generalization capacity for new test samples. Extensive investigation and comparison are carried out among the ASLDA, the ordinary LDA and other competing ERP classification algorithms, based on different three ERP datasets. Experimental results indicate that the ASLDA yields better overall performance for single-trial ERP classification when insufficient training samples are available. This suggests the proposed ASLDA is promising for ERP classification in small sample size scenario to improve the practicability of BCI.
International journal of neural systems.Int J Neural Syst.2014 Feb;24(1):1450003. doi: 10.1142/S0129065714500038. Epub 2013 Dec 2.
Two main issues for event-related potential (ERP) classification in brain-computer interface (BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may deteriorate classification performance, especially with insufficient training samples resulted from limited calibration tim
PMID 24344691

Japanese Journal

Electronic structure of photoresponsive Ag6M2O7 (M = Si, Ge)

Journal of the Ceramic Society of Japan 124(1), 116-121, 2016
NAID 130005117389

LDAを用いたセルオートマトンによる交通流の分析 (情報論的学習理論と機械学習情報論的学習理論ワークショップ(IBIS2015))

電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 115(323), 33-37, 2015-11-26
NAID 40020676319

Comparison of the electronic band structures of LiCaAlF

Jpn. J. Appl. Phys. 54(12), 122602, 2015-11-13
NAID 150000111527

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