IMP2RIS, an automated plant root PET radiotracer gas delivery system for in-soil visualization of symbiotic N2 fixation in nodulated roots of soybean plants via PET imaging
Bibliographic record
Abstract
The real-time and non-invasive visualization and quantification of symbiotic nitrogen fixation (SNF) in nodulated roots of soybean plants using Positron Emission Tomography (PET) imaging, coupled with the application of [ 13 N]N 2 gas as a PET radiotracer, has been explored in only a few studies. In these studies, [ 13 N]N 2 was delivered to nodulated soybean roots suspended in air within gas-tight acrylic boxes, followed by two-dimensional (2D) PET imaging to visualize the assimilated [ 13 N]N 2 in the air-suspended root nodules. In this paper, we introduce the In-Media Plant PET Root Imaging System (IMP 2 RIS), a novel gas delivery system designed and constructed in-house. Unlike the previous methods, IMP 2 RIS allows for non-intrusive delivery and exposure of [ 13 N]N 2 gas to the nodulated roots of soybean plants grown in a clay-rich, soil-like and visually opaque growth medium. This advancement enabled in-soil, three-dimensional (3D) visualization of SNF in soybean root nodules using Sofie, a preclinical PET scanner. Equipped with automated controls, IMP 2 RIS ensures ease of operation and operator safety during the [ 13 N]N 2 delivery process. We describe the components and functionalities of IMP 2 RIS, supported by experimental results showcasing its successful application in efficient delivery and exposure of [ 13 N]N 2 gas to nodulated roots of three soybean plant cultivars that vary in rates of N 2 fixation. The in-soil quantitative PET imaging of SNF, aided by IMP 2 RIS, holds promise for enhancing the integration of SNF as a functional phenotypic trait into breeding programs, aiming to enhance SNF efficiency by identifying breeding materials with high SNF capacities.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".