3D Visualization of Maize Stem by MRI Technology
Bibliographic record
Abstract
Abstract Recent development in confocal and multi-photon microscopy allows 3D imaging of plant tissue in high resolution. However, other than physical sectioning, macroscopical study of plant organs in 3D remains a difficult task. Among various available technologies for macroscopical imaging (e.g., Xray macro-tomography, optical coherent tomography and MRI), MRI is an ideal choice for its contrasting modality in volumetric imaging of soft tissues. A 3T Biospect MRI system (Brucker, Germany)(FIG 1) equipped with a 6cm inner diameter micro-quadrature coil (FIG 2) for RF transmission and reception of MRI signals was used in this study. Spin echo based RARE sequence was used to obtain T2 weighted images with TR/TE = 3160.5/58.5ms and field-of-view of 1.67cm × 1.67cm (256 × 256 pixels) at a slice thickness of 0.8mm. This corresponds to a voxel size of 65 × 65 × 800μm. Data was obtained within 1/2 hour with number-of-excitations (nex) set at 16. Figure 4 (a-x) shows a series of MRI sections through a stem node (the node below the main ear insertion) from field-grown maize (Zea mays, van Odyssey sweet corn). The stem was fixed in 1:3 EtOH/acetic acid, washed thoroughly in water prior to imaging. Air bubbles trapped in the tissue were removed by vacuuming, to avoid imaging artifact due to low magnetic susceptibility of air. Figure 5 (a-g) shows reconstructed longitudinal sections. Three-dimensional reconstruction (FIG. 3) was performed by using Vaytek VoxBlast™ and AutoQuant’s AutoVisulize 3D™ software. in combination with image segmentation and tracing tools, the MRI technology will greatly enhance our capability in the understanding of vascular architecture and its development in plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".