An RNA-Seq transcriptome analysis revealing novel insights into fluorine absorption and transportation in the tea plant
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The tea plant [Camellia sinensis (L.) O. Kuntze] is a species with a high concentration of fluorine in its leaves, especially in the mature leaves. The physiological mechanisms for fluorine absorption and accumulation have been well studied, but the related molecular mechanisms are poorly understood in the tea plant. In this study, transcriptome analysis by RNA-Seq following exposure to 16 mg/L of fluorine for 0, 3, 6, and 24 h was performed to identify the candidate genes involved in the transmembrane transportation of fluorine. More than 1.23 billion high-quality reads were generated, and 259.84 million unigenes were assembled de novo, with 518 216 of them being annotated in the seven databases used. Meanwhile, a large number of transporters, transcription factors, and heat-shock proteins with differential expression in response to high levels of fluorine (P ≤ 0.05) were identified. Comparative transcriptome analysis showed that the uptake of fluorine is related to photosynthesis, plant hormone signal transduction, and glutathione metabolism. Further systematic analysis of nitrate and potassium transporter genes revealed that many of these genes regulate fluorine transportation in roots and leaves. Gene expression and fluorine content analysis in different cultivars revealed CsNRT1/PTR 3.1 and CsPT 8 as the key genes regulating the transmembrane transportation of fluorine in the tea plant.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it