The “resurrection method” for modification of specific proteins in higher plants

AbstractWe describe a new method designated “the resurrection method” by which a modified protein is expressed in higher plants in place of the original protein. The modified gene constructed by introducing synonymous codon substitutions throughout the original gene to prevent the sequence-specific degradation of its mRNA during RNA silencing is expressed while the expression of the original gene is suppressed. Here, we report the successful alteration of the biochemical properties of green fluorescent protein expressed in transgenic Nicotiana benthamiana, suggesting that this method could be useful for gene control in living plants

ロシア近世経済史研究の新動向

論

A Comprehensive Analysis of Proportional Intensity-based Software Reliability Models with Covariates (New Developments on Mathematical Decision Making Under Uncertainty)

The black-box approach based on stochastic software reliability models is a simple methodology with only software fault data in order to describe the temporal behavior of fault-detection processes, but fails to incorporate some significant development metrics data observed in the development process. In this paper we develop proportional intensity-based software reliability models with time-dependent metrics, and propose a statistical framework to assess the software reliability with the timedependent covariate as well as the software fault data. The resulting models are similar to the usual proportional hazard model, but possess somewhat different covariate structure from the existing one. We compare these metricsbased software reliability models with eleven well-known non-homogeneous Poisson process models, which are the special cases of our models, and evaluate quantitatively the goodness-of-fit and prediction. As an important result, the accuracy on reliability assessment strongly depends on the kind of software metrics used for analysis and can be improved by incorporating the time-dependent metrics data in modeling

Streaming Active Learning for Regression Problems Using Regression via Classification

One of the challenges in deploying a machine learning model is that the model's performance degrades as the operating environment changes. To maintain the performance, streaming active learning is used, in which the model is retrained by adding a newly annotated sample to the training dataset if the prediction of the sample is not certain enough. Although many streaming active learning methods have been proposed for classification, few efforts have been made for regression problems, which are often handled in the industrial field. In this paper, we propose to use the regression-via-classification framework for streaming active learning for regression. Regression-via-classification transforms regression problems into classification problems so that streaming active learning methods proposed for classification problems can be applied directly to regression problems. Experimental validation on four real data sets shows that the proposed method can perform regression with higher accuracy at the same annotation cost