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Mendeley readers
Attention Score in Context
| Title |
Maize transformation technology development for commercial event generation
|
|---|---|
| Published in |
Frontiers in Plant Science, August 2014
|
| DOI | 10.3389/fpls.2014.00379 |
| Pubmed ID | |
| Authors |
Qiudeng Que, Sivamani Elumalai, Xianggan Li, Heng Zhong, Samson Nalapalli, Michael Schweiner, Xiaoyin Fei, Michael Nuccio, Timothy Kelliher, Weining Gu, Zhongying Chen, Mary-Dell M. Chilton |
| Abstract |
Maize is an important food and feed crop in many countries. It is also one of the most important target crops for the application of biotechnology. Currently, there are more biotech traits available on the market in maize than in any other crop. Generation of transgenic events is a crucial step in the development of biotech traits. For commercial applications, a high throughput transformation system producing a large number of high quality events in an elite genetic background is highly desirable. There has been tremendous progress in Agrobacterium-mediated maize transformation since the publication of the Ishida et al. (1996) paper and the technology has been widely adopted for transgenic event production by many labs around the world. We will review general efforts in establishing efficient maize transformation technologies useful for transgenic event production in trait research and development. The review will also discuss transformation systems used for generating commercial maize trait events currently on the market. As the number of traits is increasing steadily and two or more modes of action are used to control key pests, new tools are needed to efficiently transform vectors containing multiple trait genes. We will review general guidelines for assembling binary vectors for commercial transformation. Approaches to increase transformation efficiency and gene expression of large gene stack vectors will be discussed. Finally, recent studies of targeted genome modification and transgene insertion using different site-directed nuclease technologies will be reviewed. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 157 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Belgium | 1 | <1% |
| Vietnam | 1 | <1% |
| Unknown | 155 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 36 | 23% |
| Student > Ph. D. Student | 28 | 18% |
| Student > Master | 19 | 12% |
| Student > Bachelor | 16 | 10% |
| Student > Doctoral Student | 15 | 10% |
| Other | 17 | 11% |
| Unknown | 26 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 81 | 52% |
| Biochemistry, Genetics and Molecular Biology | 31 | 20% |
| Medicine and Dentistry | 3 | 2% |
| Computer Science | 3 | 2% |
| Environmental Science | 2 | 1% |
| Other | 4 | 3% |
| Unknown | 33 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 September 2022.
All research outputs
#4,769,840
of 23,322,966 outputs
Outputs from Frontiers in Plant Science
#2,556
of 21,161 outputs
Outputs of similar age
#46,525
of 231,477 outputs
Outputs of similar age from Frontiers in Plant Science
#17
of 168 outputs
Altmetric has tracked 23,322,966 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 21,161 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 231,477 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 168 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.