<scp>T</scp>ip<scp>C</scp>hip: a modular, <scp>MEMS</scp>‐based platform for experimentation and phenotyping of tip‐growing cells
Bibliographic record
Abstract
Large-scale phenotyping of tip-growing cells such as pollen tubes has hitherto been limited to very crude parameters such as germination percentage and velocity of growth. To enable efficient and high-throughput execution of more sophisticated assays, an experimental platform, the TipChip, was developed based on microfluidic and microelectromechanical systems (MEMS) technology. The device allows positioning of pollen grains or fungal spores at the entrances of serially arranged microchannels equipped with microscopic experimental set-ups. The tip-growing cells (pollen tubes, filamentous yeast or fungal hyphae) may be exposed to chemical gradients, microstructural features, integrated biosensors or directional triggers within the modular microchannels. The device is compatible with Nomarski optics and fluorescence microscopy. Using this platform, we were able to answer several outstanding questions on pollen tube growth. We established that, unlike root hairs and fungal hyphae, pollen tubes do not have a directional memory. Furthermore, pollen tubes were found to be able to elongate in air, raising the question of how and where water is taken up by the cell. The platform opens new avenues for more efficient experimentation and large-scale phenotyping of tip-growing cells under precisely controlled, reproducible conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".