AnatomyArray: A high-throughput platform for anatomical phenotyping in plants
Bibliographic record
Abstract
The anatomy or the arrangement of cells often determines the organization and function of plant tissues. However, current methods in large-scale imaging and accurate quantification of anatomical traits face major limitations. To address these challenges, we introduce the AnatomyArray system, an integrated platform for multiplexed tissue sectioning and anatomical phenotyping in plants. This system includes a highly adaptable device for high-throughput paraffin sectioning and multichannel slide imaging of various plant tissues, along with AnatomyNet, a deep learning tool for analyzing tissue-scale patterns of cell arrangement and morphology. AnatomyNet delivers accurate, automated quantification of anatomical traits at both the tissue and cellular levels, outperforming existing tools in image analysis. Using the AnatomyArray system, we dissected the genetic basis of root anatomy in a diverse wheat (Triticum aestivum L.) population through anatomics-based genome-wide association studies. Among the candidate genes identified, SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 14 (TaSPL14) was associated with stele and pericycle size in roots. Analysis of Taspl14 mutants confirmed that TaSPL14 plays a critical role in regulating root growth and tissue size by influencing phytohormone pathways. The AnatomyArray platform enables high-throughput characterization of cellular-level features and provides insights into the mechanisms shaping anatomical structure in plants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".