Importance of Stress Granules in Stress Tolerance
Bibliographic record
Abstract
Plants are sessile organisms that had to develop molecular mechanisms to deal with changes in their environment. One of such mechanism is formation of stress-induced condensates called Stress Granules (SGs). SGs are liquid-liquid phase separation (LLPS) biomolecular condensates composed of proteins, mRNA and metabolites. The main function of the SGs is protective sequestration of their components. When analyzed the composition of SGs in plants we realized high similarity to SGs described in mammalian and yeast cells suggesting that there is conservation of SGs across different species. Therefore, research on SGs in plants might be beneficial for understanding the response of whole organism into stressful conditions. With the use of cell biology, biochemistry, molecular biology and omic approaches, our group is interested to uncover the mechanism of SGs formation/disassembly but also the true role of SGs in stress signaling and tolerance. Recent research in our lab shows that by manipulation of key SG proteins or their biophysical properties we can affect the overall SG dynamics leading to improved stress tolerance. During my talk, I will focus on RBP proteins that are close homologues of TIA from mammalian and are well known SGs markers in plants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".