Construction and analysis of an SSH cDNA library of early heat-induced genes of<i>Vigna aconitifolia</i>variety RMO-40
Bibliographic record
Abstract
Moth bean ( Vigna aconitifolia (Jacq.) Marechal) is an important grain legume crop grown in rain fed areas of hot desert regions of Thar, India, under scorching sun rays with very little supplementation of water. An SSH cDNA library was generated from leaf tissues of V. aconitifolia var. RMO-40 exposed to an elevated temperature of 42 °C for 5 min to identify early-induced genes. A total of 488 unigenes (114 contigs and 374 singletons) were derived by cluster assembly and sequence alignment of 738 ESTs; out of 206 ESTs (28%) of unknown proteins, 160 ESTs (14%) were found to be novel to moth bean. Only 578 ESTs (78%) showed significant BLASTX similarity (<1 × 10(-6)) in the NCBI non-redundant database. Gene ontology functional classification terms were retrieved for 479 (65%) sequences, and 339 sequences were annotated with 165 EC codes and mapped to 68 different KEGG pathways. Four hundred and fifty-two ESTs were further annotated with InterProScan (IPS), and no IPS was assigned to 153 ESTs. In addition, the expression level of 27 ESTs in response to heat stress was evaluated through semiquantitative RT-PCR assay. Approximately 20 different signaling genes and 16 different transcription factors have been shown to be associated with heat stress in moth bean for the first time.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
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".