Characterization of monoclonal antibodies specific to wheat glutenin subunits and their correlation with quality parameters
Bibliographic record
Abstract
Immunochemical methods are very useful in predicting the quality of wheat and differentiating alleles. In order to prepare appropriate monoclonal antibodies, HMW-GS 1Bx13 and 1By16 from spelt wheat were used as antigens to immunize BALB/C mice. Four monoclonal antibodies (mAbs) were obtained and designated 24231, 24245, 14588 and 14587, respectively. Results of Western blot showed that mAbs 24231 and 24245 prepared against 1Bx13 bound only to LMW-GS. The mAb 14588 prepared against 1By16 bound strongly to LMW-GS, but weakly to 1By and some 1Dy type HMW-GS. The mAb 14587 prepared against 1By16 bound only to 1Dx HMW-GS. The results of indirect ELISA and statistical analysis showed that correlations between mAb 24231 and development time and stability were significantly (P < 0.05) and highly significantly (P < 0.01) negative, respectively, whereas those of mAb 24245 with development time and extensibility were highly significantly (P < 0.01) and significantly (P < 0.05) negative, respectively. Significantly (P < 0.05) and highly significantly (P < 0.01) positive correlations were observed between mAb 14588 and stability and development time. Mean differential binding of mAb 14587 with 1Dx5t and 1Dx2t subunits from Aegilops tauschii was highly significant (P < 0.01), suggesting that ELISA could potentially be used as an effective screening tool during direct genetic transfer of desirable glutenin subunits from Aegilops tauschii to hexaploid wheat. Key words: Glutenin subunits, monoclonal antibodies, ELISA, wheat quality
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".