Faculty Opinions recommendation of TOR dynamically regulates plant cell-cell transport.
Bibliographic record
Abstract
The coordinated redistribution of sugars from mature "source" leaves to developing "sink" leaves requires tight regulation of sugar transport between cells via plasmodesmata (PD). Although fundamental to plant physiology, the mechanisms that control PD transport and thereby support development of new leaves have remained elusive. From a forward genetic screen for altered PD transport, we discovered that the conserved eukaryotic glucose-TOR (TARGET OF RAPAMYCIN) metabolic signaling network restricts PD transport in leaves. Genetic approaches and chemical or physiological treatments to either promote or disrupt TOR activity demonstrate that glucose-activated TOR decreases PD transport in leaves. We further found that TOR is significantly more active in mature leaves photosynthesizing excess sugars than in young, growing leaves, and that this increase in TOR activity correlates with decreased rates of PD transport. We conclude that leaf cells regulate PD trafficking in response to changing carbohydrate availability monitored by the TOR pathway. PMID: 32051250 Funding information This work was supported by: NIGMS NIH HHS, United States Grant ID: R01 GM045244 NIH HHS, United States Grant ID: DP5 OD023072
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.063 |
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".