Temperature Response of Photosynthesis in Transgenic Rice Transformed with ‘Sense’ or ‘Antisense’ rbcS
Bibliographic record
Abstract
The responses of chlorophyll fluorescence, gas exchange rate and Rubisco activation state to temperature were examined in transgenic rice plants with 130 and 35% of the wild-type (WT) Rubisco content by transformation with rbcS cDNA in sense and antisense orientations, respectively. Although the optimal temperatures of PSII quantum efficiency and CO(2) assimilation were found to be between 25 and 32 degrees C, the maximal activation state of Rubisco was found to be between 16 and 20 degrees C in all genotypes. The Rubisco flux control coefficient was also the highest between 16 and 20 degrees C in the WT and antisense lines [>0.88 at an intercellular CO(2) pressure (Ci) of 28 Pa]. Gross photosynthesis at Ci = 28 Pa per Rubisco content in the WT between 12 and 20 degrees C was close to that of the antisense lines where high Rubisco control is present. Thus, Rubisco activity most strongly limited photosynthesis at cool temperatures. These results indicated that a selective enhancement of Rubisco content can enhance photosynthesis at cool temperatures, but in the sense line with enhanced Rubisco content Pi regeneration limitation occurred. Above 20 degrees C, the Rubisco flux control coefficient declined. This decline was associated with a decline in Rubisco activation. The activation state of Rubisco measured at each temperature decreased with increasing Rubisco content, and the slope of activation to Rubisco content was independent of temperature. We discuss the possibility that the decline in Rubisco activation at intermediate and high temperatures is part of a regulated response to a limitation in other photosynthetic processes.
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 | 0.000 |
| 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".