Abscisic acid phytohormone estimation in tubers and shoots of <scp><i>Ipomoea batatas</i></scp> subjected to long drought stress using competitive immunological assay
Bibliographic record
Abstract
Sweet potato (Ipomoea batatas L.), typically cultivated in temperate climates under low inputs, is one of the most important crops worldwide. Abscisic acid (ABA) is an important plant stress‐induced phytohormone. Hitherto, few works analyzed the ABA function in sweet potato tissue growth. Very scarce information is available concerning the ABA role in sweet potato response to water scarcity conditions. Here, we show the ABA content variation in shoots and tubers of eight sweet potato accessions subjected to drought stress. ABA was also related to other resistance traits, such as chlorophyll content index (CCI), carbon isotopic discrimination (Δ13C), oxalic acid (OA) and water use efficiency (WUE), to assess stress response mechanisms to water deficit between their organs. The most resilient drought‐stressed sweet potato plants accumulated ABA‐shoot, and significantly decreased the ABA‐tuber content. ABA signaling was related to Δ13C and CCI decrease and WUE increment, as an attempt to cope with water stress by partially closing the stomata. The partial closure of stomata could be in part due to the presence of OA‐shoots, known to affect the intensity of the ABA‐shoot signal in stomatal closure. Higher CCI content and minimal Δ13C‐shoot differences indicated good carboxylation fractionation, with higher Δ13C‐tuber content as an indicator of efficient tuber 13C fixation and growth. Our work demonstrated that ABA could be used in conjunction with the other traits studied for the assessment of sweet potato whole‐plant responses to environmental stresses, and thus aid the selection of the best drought tolerant genotypes for breeding programs.
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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 | 0.001 |
| 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".