An optimised transformation protocol for <i>Anthoceros agrestis</i> and three more hornwort species
Bibliographic record
Abstract
Abstract Land plants comprise two large monophyletic lineages, the vascular plants and the bryophytes, which diverged from their most recent common ancestor approximately 480 million years ago. Of the three lineages of bryophytes, only the mosses and the liverworts are systematically investigated, while the hornworts are understudied. Despite their importance for understanding fundamental questions of land plant evolution, they only recently became amenable to experimental investigation, with Anthoceros agrestis being developed as a hornwort model system. Availability of a high quality genome assembly and a recently developed genetic transformation technique makes A. agrestis an attractive model species for hornworts. Here we describe an updated and optimised transformation protocol for A. agrestis which can be successfully used to genetically modify one more strain of A. agrestis and three more hornwort species, Anthoceros punctatus, Leiosporoceros dussi and Phaeoceros carolinianus. The new transformation method is less laborious, faster and results in the generation of greatly increased numbers of transformants compared to the previous method. We have also developed a new selection marker for transformation. Finally, we report the development of a set of different cellular localisation signal peptides for hornworts providing new tools to better understand hornwort cell biology.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".