Ice and anti‐nucleating activities of an ice‐binding protein from the annual grass, <i>Brachypodium distachyon</i>
Bibliographic record
Abstract
Plants exposed to sub-zero temperatures face unique challenges that threaten their survival. The growth of ice crystals in the extracellular space can cause cellular dehydration, plasma membrane rupture and eventual cell death. Additionally, some pathogenic bacteria cause tissue damage by initiating ice crystal growth at high sub-zero temperatures through the use of ice-nucleating proteins (INPs), presumably to access nutrients from lysed cells. An annual species of brome grass, Brachypodium distachyon (Bd), produces an ice-binding protein (IBP) that shapes ice with a modest depression of the freezing point (~0.1 °C at 1 mg/mL), but high ice-recrystallization inhibition (IRI) activity, allowing ice crystals to remain small at near melting temperatures. This IBP, known as BdIRI, is unlike other characterized IBPs with a single ice-binding face, as mutational analysis indicates that BdIRI adsorbs to ice on two faces. BdIRI also dramatically attenuates the nucleation of ice by bacterial INPs (up to -2.26 °C). This 'anti-nucleating' activity is significantly higher than previously documented for any IBP.
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.
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 | 0.000 | 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 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".