Selection of DNA Aptamers for Root Exudate <scp>l</scp>-Serine Using Multiple Selection Strategies
Bibliographic record
Abstract
Agricultural biosensing can aid decisions about crop health and maintenance, because crops release root exudates that can inform about their status. l -Serine has been found to be indicative of nitrogen uptake in wheat and canola. The development of a biosensor for l -serine could allow farmers to monitor crop nutrient demands more precisely. The development of robust l -serine-binding DNA aptamers is described. Because small molecules can be challenging targets for Systematic Evolution of Ligands by EXponential enrichment (SELEX), three separate DNA libraries were used for SELEX experiments. A l -homocysteine aptamer was randomized to create a starting library for a l -serine selection (randomized SELEX). The final selection rounds of the l -homocysteine selection were also used as a starting library for l -serine (redirected SELEX). Finally, an original DNA library was used (original SELEX). All three SELEX experiments produced l -serine-binding aptamers with micromolar affinity, with Red.1 aptamer having a K d of 7.9 ± 3.6 μM. Truncation improved the binding affinity to 5.2 ± 2.7 μM, and from this sequence, a Spiegelmer with improved nuclease resistance was created with a K d of 2.0 ± 0.8 μM. This l -serine-binding Spiegelmer has the affinity and stability to be incorporated into aptamer-based biosensors for agricultural applications.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".