Inferring the Geometry of Fourth-Period Metallic Elements in Arabidopsis thaliana Seeds using Synchrotron-Based Multi-Angle X-ray Fluorescence Mapping
Bibliographic record
Abstract
BACKGROUND: Improving our knowledge of plant metal metabolism is facilitated by the use of analytical techniques to map the distribution of elements in tissues. One such technique is X-ray fluorescence (XRF), which has been used previously to map metal distribution in both two and three dimensions. One of the difficulties of mapping metal distribution in two dimensions is that it can be difficult to normalize for tissue thickness. When mapping metal distribution in three dimensions, the time required to collect the data can become a major constraint. In this article a compromise is suggested between two- and three-dimensional mapping using multi-angle XRF imaging. METHODS: A synchrotron-based XRF microprobe was used to map the distribution of K, Ca, Mn, Fe, Ni, Cu and Zn in whole Arabidopsis thaliana seeds. Relative concentrations of each element were determined by measuring fluorescence emitted from a 10 microm excitation beam at 13 keV. XRF spectra were collected from an array of points with 25 or 30 microm steps. Maps were recorded at 0 and 90 degrees , or at 0, 60 and 120 degrees for each seed. Using these data, circular or ellipsoidal cross-sections were modelled, and from these an apparent pathlength for the excitation beam was calculated to normalize the data. Elemental distribution was mapped in seeds from ecotype Columbia-4 plants, as well as the metal accumulation mutants manganese accumulator 1 (man1) and nicotianamine synthetase (nasx). CONCLUSIONS: Multi-angle XRF imaging will be useful for mapping elemental distribution in plant tissues. It offers a compromise between two- and three-dimensional XRF mapping, as far as collection times, image resolution and ease of visualization. It is also complementary to other metal-mapping techniques. Mn, Fe and Cu had tissue-specific accumulation patterns. Metal accumulation patterns were different between seeds of the Col-4, man1 and nasx genotypes.
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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".