Structural and functional studies of strigolactone receptors of <i>Striga hermonthica</i>, its adaptation and evolution
Bibliographic record
Abstract
Striga spp. is a genus of obligate root-parasitic plants, with the ability to deplete host roots from water, carbohydrates, and other essential nutrients. There are around 30-35 species within the genus, and at least 80% of them are found in Africa. The most destructive species found in Africa is Striga hermonthica, with the ability to parasite major food crops, such as rice, millet, maize, and sorghum, producing high yield losses ranging from 30% to 100% of the crops (Berner, Kling, & Singh, 1995). Nowadays, the most common method to eliminate the parasitic plants is hand weeding, which is time-consuming, labour-intensive, and not very effective. It is thought that a complete understanding of the germination mechanism of Striga is necessary to develop targeted strategies to attack this weed. It has been proven that the germination of parasitic plants such as Striga and Orobanche is triggered by chemical factors called strigolactones (Toh et al., 2015). Although the strigolactone receptor system has been studied, it is not completely characterized yet. It is known that the system is composed of 11 different receptors and each of them presents variable sensitivities to strigolactones. Several attempts to crystallize these receptors have been made, but only two structures have been reported so far, ShHTL3 (Xu et al., 2016) and ShHTL5 (Toh et al., 2015). It is essential to reveal the structural differences between the ShHTL receptors to understand its particular role in the strigolactone detection and establish the molecular basis for this detection.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".