Molecular Biology in the Improvement of Biological Nitrogen Fixation by Rhizobia and Extending the Scope to Cereals
Bibliographic record
Abstract
The contribution of biological nitrogen fixation to the total N requirement of food and feed crops diminished in importance with the advent of synthetic N fertilizers, which fueled the "green revolution". Despite being environmentally unfriendly, the synthetic versions gained prominence primarily due to their low cost, and the fact that most important staple crops never evolved symbiotic associations with bacteria. In the recent past, advances in our knowledge of symbiosis and nitrogen fixation and the development and application of recombinant DNA technology have created opportunities that could help increase the share of symbiotically-driven nitrogen in global consumption. With the availability of molecular biology tools, rapid improvements in symbiotic characteristics of rhizobial strains became possible. Further, the technology allowed probing the possibility of establishing a symbiotic dialogue between rhizobia and cereals. Because the evolutionary process did not forge a symbiotic relationship with the latter, the potential of molecular manipulations has been tested to incorporate a functional mechanism of nitrogen reduction independent of microbes. In this review, we discuss various strategies applied to improve rhizobial strains for higher nitrogen fixation efficiency, more competitiveness and enhanced fitness under unfavorable environments. The challenges and progress made towards nitrogen self-sufficiency of cereals are also reviewed. An approach to integrate the genetically modified elite rhizobia strains in crop production systems is highlighted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".