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Record W7081569762 · doi:10.48448/x48y-t551

Importance of Stress Granules in Stress Tolerance

2025· other· en· W7081569762 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsOrganismStress granuleMechanism (biology)Model organismFunction (biology)YeastStress (linguistics)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.424
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.254
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it