Physiological and molecular responses to drought stress in teak (Tectona grandis L.f.)
Bibliographic record
Abstract
Drought stress is an increasingly common and worrying phenomenon because it causes a loss of production in both agriculture and forestry. Teak is a tropical tree which needs alternating rainy and dry seasons to produce high-quality wood. However, a robust understanding about the physiological characteristics and genes related to drought stress in this species is lacking. Consequently, after applying moderate and severe drought stress to teak seedlings, an infrared gas analyzer (IRGA) was used to measure different parameters in the leaves. Additionally, using the root transcriptome allowed finding and analyzing the expression of several drought-related genes. As a result, in both water deficit treatments a reduction in photosynthesis, transpiration, stomatal conductance and leaf relative water content was found. As well, an increase in free proline levels and intrinsic water use efficiency was found when compared to the control treatment. Furthermore, 977 transcripts from the root contigs showed functional annotation related to drought stress, and of these, TgTPS1, TgDREB1, TgAREB1 and TgPIP1 were selected. The expression analysis of those genes along with TgHSP1, TgHSP2, TgHSP3 and TgBI (other stress-related genes) showed that with moderate treatment, TgTPS1, TgDREB1, TgAREB1, TgPIP1, TgHSP3 and TgBI genes had higher expression than the control treatment, but with severe treatment only TgTPS1 and TgDREB1 showed higher expression than the control treatment. At the end, a schematic model for the physiological and molecular strategies under drought stress in teak from this study is provided. In conclusion, these physiological and biochemical adjustments in leaves and genetic changes in roots under severe and prolonged water shortage situations can be a limiting factor for teak plantlets' growth. Further studies of those genes under different biotic and abiotic stress treatments are needed.
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 | 0.000 | 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".