| Project/Area Number |
22658075
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Single-year Grants |
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| Research Field |
Agricultural environmental engineering
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| Research Institution | Kobe University |
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Principal Investigator |
ITOH Hiromichi 神戸大学, 大学院・農学研究科, 准教授 (00258063)
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| Co-Investigator(Kenkyū-buntansha) |
SHIRAISHI Naomasa 神戸大学, 大学院・農学研究, 助教 (00304121)
UNO Yuichi 神戸大学, 大学院・農学研究科, 准教授 (90304120)
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| Project Period (FY) |
2010 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| Budget Amount *help |
¥3,450,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2010: ¥2,800,000 (Direct Cost: ¥2,800,000)
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| Keywords | 生物生産システム / 遺伝子組換え / カルス / ハイパースペクトルカメラ / 近赤外線分光法 / 硝酸還元酵素遺伝子 / ホウレンソウ / 遺伝子組み換え |
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| Research Abstract |
The purpose of this research is to develop a hyperspectral imaging system to detect gene recombinant plant cells. Since drug tolerance genes have the potential to negatively affect ecosystems, this research plans to use genes that express the nitrate reductase(NR) as marker genes. If the NR genes are introduced into a cell with a transgene, the recombined genes would show higher potential to reduce nitrate concentration than that of a wild type. Therefore, non-destructive methods for measurements of nitrate concentration in callus were developed to detect the gene recombinant calluses. As for accuracy of developed mathematical model to estimate the nitrate concentration of callus from the near-infrared spectrum, correlation coefficient between measured and estimated nitrate concentration became 0. 7363. Though we tried to introduce the NR gene to spinach cells by using the particle gun method, gene recombinant calluses could not be produced because of inappropriate conditions for the recombination. Then possibilities to discriminate calluses containing high nitrate concentration(more than 961. 0 mg/L) from low concentration calluses(less than 961. 0 mg/L) by processing the near-infrared spectra were examined. Accuracy rate became 85% that was obtained from the mathematical model to predict the nitrate concentration and 87. 4% that was derived from PLS-DA
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