• Journal of Microbiology and Biotechnology
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• v.25 no.8
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• pp.1339-1348
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• 2015

Until now, considerable effort has been made to engineer novel nitrogen-fixing organisms through the transfer of nif genes from various diazotrophs to non-nitrogen fixers; however, regulatory coupling of the heterologous nif genes with the regulatory system of the new host is still not well understood. In this work, a 49 kb nitrogen fixation island from P. stutzeri A1501 was transferred into E. coli using a novel and efficient transformation strategy, and a series of recombinant nitrogen-fixing E. coli strains were obtained. We found that the nitrogenase activity of the recombinant E. coli strain EN-01, similar to the parent strain P. stutzeri A1501, was dependent on external ammonia concentration, oxygen tension, and temperature. We further found that there existed a regulatory coupling between the E. coli general nitrogen regulatory system and the heterologous P. stutzeri nif island in the recombinant E. coli strain. We also provided evidence that the E. coli general nitrogen regulator GlnG protein was involved in the activation of the nif-specific regulator NifA via a direct interaction with the NifA promoter. To the best of our knowledge, this work plays a groundbreaking role in increasing understanding of the regulatory coupling of the heterologous nitrogen fixation system with the regulatory system of the recipient host. Furthermore, it will shed light on the structure and functional integrity of the nif island and will be useful for the construction of novel and more robust nitrogen-fixing organisms through biosynthetic engineering.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.