Polyclonal-Based ELISA for the Identification of Cyclohexanedione Analogs that Inhibit Maize Acetyl Coenzyme-A Carboxylase
Bibliographic record
Abstract
Cyclohexanedione herbicides inhibit monocotyledonous acetyl coenzyme-A carboxylase (ACCase; E.C. 6.4.1.2.), which catalyzes the first committed step in fatty acid biosynthesis. Although the target site has been identified, little is known about the mechanisms involved in herbicide binding. An immunological study was undertaken to create a model to better characterize the herbicide-enzyme interaction. Cyclohexanedione-specific antiserum was raised in New Zealand white rabbits by immunizing them with a cyclohexanedione analog-bovine serum albumin conjugate. Two indirect enzyme-linked immunosorbent assays (ELISA) were developed using 2 different cyclohexanedione analogs conjugated to ovalbumin as coating conjugates. Nineteen cyclohexanedione analogs, 13 active ACCase inhibitors, and 6 inactive analogs were tested for their ability to compete with both coating conjugates for antiserum binding. All active ACCase inhibitors were observed to compete with both coating conjugates, whereas all inactive analogs failed to compete with at least one coating conjugate. On the basis of these results, the immunological model could be used to distinguish all active ACCase inhibitors from inactive analogs using the 2 ELISAs sequentially.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".