Bibliographic record
Abstract
The cuticle is a lipid barrier that covers the air-exposed surfaces of plants. It consists of waxes and cutin, a cell wall–attached lipid polyester of oxygenated fatty acids and glycerol. Unlike waxes, cutin is insoluble in organic solvents, and its composition is typically studied by chemical depolymerization followed by monomer analysis by gas chromatography (GC). Here, we describe a method for the chemical depolymerization of cutin in maize leaves and subsequent compositional analysis of the constituent lipid monomers. The method has been adapted from protocols for cutin analysis developed for Arabidopsis , by both optimizing the amount of leaf tissue used and including a data analysis process specific to the monomers present in maize cutin. The approach uses base-catalyzed transmethylation, which produces fatty acid methyl esters, and silylation, which gives trimethylsilyl ether derivatives of hydroxyl groups for gas chromatographic analysis. For monomer identification, a few representative samples are first analyzed by GC–mass spectrometry (GC-MS). This is then followed by analysis of all replicates by gas chromatography coupled to a flame ionization detector (GC-FID) for monomer quantification, because the flame ionization detector provides a linear response over a wide mass range, is relatively simple to operate, and is more cost-effective to maintain compared to mass spectrometry detectors. Although the protocol bypasses time-consuming cuticle isolation steps by using whole-leaf samples, this means that a fraction of the compounds in the chromatographic profiles do not derive from cutin. Accordingly, we discuss some considerations for the interpretation of the resulting depolymerization products. Our protocol offers specific guidance on preparing maize leaf samples, ensuring reproducible results, and enabling the detection of subtle variations in cutin monomer composition among plant genotypes or developmental stages.
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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 | 0.001 | 0.001 |
| 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".