Spectroscopy and SEM imaging reveal endosymbiont-dependent components changes in germinating kernel through direct and indirect coleorhiza-fungus interactions under stress
Bibliographic record
Abstract
Abstract In the present study, FTIR spectroscopy and hyperspectral imaging was introduced as a non-destructive, sensitive-reliable tool for assessing the tripartite kernel-fungal endophyte environment interaction. Composition of coleorhizae of Triticum durum was studied under ambient and drought stress conditions. The OH-stretch IR absorption spectrum suggests that the water-deficit was possibly improved or moderated by kernel’s endophytic partner. The OH-stretch frequency pattern coincides with other (growth and stress) related molecular changes. Analysis of lipid (3100–2800 cm−1) and protein (1700–1550 cm−1) regions seems to demonstrate that drought has a positive impact on lipids. The fungal endosymbiont direct contact with kernel during germination had highest effect on both lipid and protein (Amide I and II) groups, indicating an increased stress resistance in inoculated kernel. Compared to the indirect kernel-fungus interaction and to non-treated kernels (control), direct interaction produced highest effect on lipids. Among treatments, the fingerprint region (1800–800 cm−1) and SEM images indicated an important shift in glucose oligosaccharides, possibly linked to coleorhiza-polymer layer disappearance. Acquired differentiation in coleorhiza composition of T. durum, between ambient and drought conditions, suggests that FTIR spectroscopy could be a promising tool for studying endosymbiont-plant interactions within a changing environment.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".