Post-transcriptional control of bacterial nitrogen metabolism by regulatory noncoding RNAs
Bibliographic record
Abstract
Nitrogen metabolism is the most basic process of material and energy metabolism in living organisms, and processes involving the uptake and use of different nitrogen sources are usually tightly regulated at the transcriptional and post-transcriptional levels. Bacterial regulatory noncoding RNAs are novel post-transcriptional regulators that repress or activate the expression of target genes through complementarily pairing with target mRNAs; therefore, these noncoding RNAs play an important regulatory role in many physiological processes, such as bacterial substance metabolism and stress response. In recent years, a study found that noncoding RNAs play a vital role in the post-transcriptional regulation of nitrogen metabolism, which is currently a hot topic in the study of bacterial nitrogen metabolism regulation. In this review, we present an overview of recent advances that increase our understanding on the regulatory roles of bacterial noncoding RNAs and describe in detail how noncoding RNAs regulate biological nitrogen fixation and nitrogen metabolic engineering. Furthermore, our goal is to lay a theoretical foundation for better understanding the molecular mechanisms in bacteria that are involved in environmental adaptations and metabolically-engineered genetic modifications.
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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 | 0.001 |
| 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 | 0.002 |
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".