Chemical Imaging of Microstructures of Plant Tissues within Cellular Dimension Using Synchrotron Infrared Microspectroscopy
Bibliographic record
Abstract
Synchrotron radiation-based Fourier transform infrared microspectroscopy (SR-FTIR) is an advanced bioanalytical technique capable of exploring the chemistry within microstructures of plant and animal tissues with a high signal to noise ratio at high ultraspatial resolutions (3-10 microm) without destruction of the intrinsic structures of a tissue. This technique is able to provide information relating to the quantity, composition, structure, and distribution of chemical constituents and functional groups in a tissue. The objective of this study was to illustrate how the SR-FTIR technique can be used to image inherent structures of plant tissues on a cellular level (pixel size, approximately 10 microm x 10 microm). The results showed that with the extremely bright synchrotron light, spectra with high signal to noise ratios were obtained from areas as small as 10 microm x 10 microm in the plant tissue, which allowed us to "see" plant tissue in a chemical sense on a cellular level. The ultraspatial resolved imaging of plant tissues by stepping in pixel-sized increments was obtained. Chemical distributions of plant tissues such as lignin, cellulose, protein, lipid, and total carbohydrate could be mapped. These images revealed the chemical information of plant intrinsic structure. In conclusion, SR-FTIR can provide chemical and functional characteristics of plant tissue at high ultraspatial resolutions. The SR-FTIR microspectroscopic images can generate spatially localized functional group and chemical information within cellular dimensions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".