ATR–FTIR spectroscopy reveals involvement of lipids and proteins of intact pea pollen grains to heat stress tolerance
Bibliographic record
Abstract
With climate change, pea will be more frequently subjected to heat stress in semi-arid regions like Saskatchewan during flowering. The pollen germination percentage of two pea cultivars was reduced by heat stress (36°C) with an important decrease in cultivar 'CDC Golden' compared to 'CDC Sage.' Lipids, protein and other pollen coat compositions of whole intact pollen grains of both pea cultivars were investigated using mid infrared (mid-IR) attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy. Curve fitting of ATR absorbance spectra in the protein region enabled estimation and comparison of different protein secondary structures between the two cultivars. CDC Sage had relatively greater amounts of α-helical structures (48.6-43.6%; band at 1654 cm(-1)) and smaller amounts of β-sheets (41.3-46%) than CDC Golden. The CDC Golden had higher amounts of β-sheets (46.3-51.7%) compared to α-helical structures (35.3-36.2%). Further, heat stress resulted in prominent changes in the symmetrical and asymmetrical CH2 bands from lipid acyl chain, ester carbonyl band, and carbohydrate region. The intensity of asymmetric and symmetric CH2 vibration of heat stressed CDC Golden was reduced considerably in comparison to the control and the decrease was higher compared to CDC Sage. In addition, CDC Golden showed an increase in intensity at the oxidative band of 3015 cm(-1). These results reveal that the whole pollen grains of both pea cultivars responded differently to heat stress. The tolerance of CDC Sage to heat stress (expressed as pollen germination percentage) may be due to its protein richness with α-helical structures which would protect against the destructive effects of dehydration due to heat stress. The low pollen germination percentage of CDC Golden after heat stress may be also due to its sensitivity to lipid changes due to heat stress.
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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 | 0.000 |
| 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".