Microprobing the molecular spatial distribution and structural architecture of feed-type sorghum seed tissue (<i>Sorghum Bicolor L.</i>) using the synchrotron radiation infrared microspectroscopy technique
Bibliographic record
Abstract
Sorghum seed (Sorghum bicolor L.) has unique degradation and fermentation behaviours compared with other cereal grains such as wheat, barley and corn. This may be related to its cell and cell-wall architecture. The advanced synchrotron radiation infrared microspectroscopy (SR-IMS) technique enables the study of cell or living cell biochemistry within cellular dimensions. The objective of this study was to use the SR-IMS imaging technique to microprobe molecular spatial distribution and cell architecture of the sorghum seed tissue comprehensively. High-density mapping was carried out using SR-IMS on beamline U2B at the National Synchrotron Light Source (Brookhaven National Laboratory, NY, USA). Molecular images were systematically recorded from the outside to the inside of the seed tissue under various chemical functional groups and their ratios [peaks at ∼1725 (carbonyl C=O ester), 1650 (amide I), 1657 (protein secondary structure α-helix), 1628 (protein secondary structure β-sheet), 1550 (amide II), 1515 (aromatic compounds of lignin), 1428, 1371, 1245 (cellulosic compounds in plant seed tissue), 1025 (non-structural CHO, starch granules), 1246 (cellulosic material), 1160 (CHO), 1150 (CHO), 1080 (CHO), 930 (CHO), 860 (CHO), 3350 (OH and NH stretching), 2960 (CH(3) anti-symmetric), 2929 (CH(2) anti-symmetric), 2877 (CH(3) symmetric) and 2848 cm(-1) (CH(2) asymmetric)]. The relative protein secondary structure α-helix to β-sheet ratio image, protein amide I to starch granule ratio image, and anti-symmetric CH(3) to CH(2) ratio image were also investigated within the intact sorghum seed tissue. The results showed unique cell architecture, and the molecular spatial distribution and intensity in the sorghum seed tissue (which were analyzed through microprobe molecular imaging) were generated using SR-IMS. This imaging technique and methodology has high potential and could be used for scientists to develop specific cereal grain varieties with targeted food and feed quality, and can also be used to monitor the degree of grain maturity, grain damage, the fate of organic contaminants and the effect of chemical treatment on plant and grain seeds.
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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".