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Iwasaki K.,Osaka University | Iwasaki K.,Japan Science and Technology Agency | Omura T.,Japan National Agricultural Research Center
Current Opinion in Structural Biology | Year: 2010

Visualizing the viral life cycle in the host challenges us to extend our understanding of the viral infection mechanism. Three-dimensional images obtained by advanced electron tomographic imaging techniques, if resolved to molecular resolution, are helpful for bridging the atomic structural information of proteins to cellular events. Characteristic large structures appear in virus-infected host cells through the life cycle of various viruses. These structures are likely to provide clues to understanding viral infection mechanisms, such as how viruses move in host cells, how they are assembled, how they egress and how they spread cell-to-cell. Here we review recent advances in the studies of the molecular architecture of virus machinery involved in the mechanism of virus infection using comprehensive electron tomographic imaging techniques. © 2010 Elsevier Ltd. Source

Hayashi T.,Japan National Institute of Agrobiological Science | Iwata H.,Japan National Agricultural Research Center
BMC Genetics | Year: 2010

Background: In genomic selection, a model for prediction of genome-wide breeding value (GBV) is constructed by estimating a large number of SNP effects that are included in a model. Two Bayesian methods based on MCMC algorithm, Bayesian shrinkage regression (BSR) method and stochastic search variable selection (SSVS) method, (which are called BayesA and BayesB, respectively, in some literatures), have been so far proposed for the estimation of SNP effects. However, much computational burden is imposed on the MCMC-based Bayesian methods. A method with both high computing efficiency and prediction accuracy is desired to be developed for practical use of genomic selection.Results: EM algorithm applicable for BSR is described. Subsequently, we propose a new EM-based Bayesian method, called wBSR (weighted BSR), which is a modification of BSR incorporating a weight for each SNP according to the strength of its association to a trait. Simulation experiments show that the computational time is much reduced with wBSR based on EM algorithm and the accuracy in predicting GBV is improved by wBSR in comparison with BSR based on MCMC algorithm. However, the accuracy of predicted GBV with wBSR is inferior to that with SSVS based on MCMC algorithm which is currently considered to be a method of choice for genomic selection.Conclusions: EM-based wBSR method proposed in this study is much advantageous over MCMC-based Bayesian methods in computational time and can predict GBV more accurately than MCMC-based BSR. Therefore, wBSR is considered a practical method for genomic selection with a large number of SNP markers. © 2010 Hayashi and Iwata; licensee BioMed Central Ltd. Source

Takahashi S.,Japan National Agricultural Research Center
Journal of Plant Nutrition and Soil Science | Year: 2013

Characterization of the forms of phosphorus (P) in organic soil amendments was conducted by sequential P fractionation. More than 60% of total P was inorganic P (Pi). The major Pi forms in the cattle-manure composts were NaHCO3- and HCl-extractable P fractions. HCl-extractable Pi was the predominant P form and a considerable proportion of the total P was present in the HCl-extractable organic P fraction in the poultry manure composts and combined organic fertilizers. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Source

Herbivore-induced plant volatiles (HIPVs) emitted from lima bean leaves infested with the two-spotted spider mites Tetranychus urticae strongly attract the predatory mites Neoseiulus californicus. Among these HIPVs, methyl salicylate and linalool can attract the predators. Three green-leaf volatiles (GLVs) of (Z)-3-hexen-1-ol, (Z)-3-hexenyl acetate and (E)-2-hexenal, found in the odor blends from T. urticae-infested leaves and physically damaged leaves, can also attract the predators. To search for a strong predator attractant, the olfactory responses of N. californicus to each synthetic compound or their combinations were investigated in a Y-tube olfactometer. When presented a choice between a mixture of the five compounds (i.e. the two HIPVs and the three GLVs) and T. urticae-infested leaves, N. californicus did not discriminate between these odor sources. The same trend was observed when either a mixture of the two HIPVs or methyl salicylate vs. T. urticae-infested leaves were compared. In contrast, the predators preferred T. urticae-infested leaves to linalool, each of the three GLVs, or a mixture of the three GLVs. These results indicated that methyl salicylate is a strong predator attractant, and its potential attractiveness almost equaled that of the blend of HIPVs from T. urticae-infested leaves. © 2009 Springer Science+Business Media B.V. Source

Jannink J.-L.,Cornell University | Jannink J.-L.,U.S. Department of Agriculture | Lorenz A.J.,U.S. Department of Agriculture | Iwata H.,Japan National Agricultural Research Center
Briefings in Functional Genomics and Proteomics | Year: 2010

We intuitively believe that the dramatic drop in the cost of DNA marker information we have experienced should have immediate benefits in accelerating the delivery of crop varieties with improved yield, quality and biotic and abiotic stress tolerance. But these traits are complex and affected by many genes, each with small effect. Traditional marker-assisted selection has been ineffective for such traits. The introduction of genomic selection (GS), however, has shifted that paradigm. Rather than seeking to identify individual loci significantly associated with a trait,GS uses all marker data as predictors of performance and consequently delivers more accurate predictions. Selection can be based on GS predictions, potentially leading to more rapid and lower cost gains from breeding. The objectives of this article are to review essential aspects of GS and summarize the important take-home messages from recent theoretical, simulation and empirical studies.We then look forward and consider research needs surrounding methodological questions and the implications of GS for long-term selection. © The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org. Source

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