Bibliographic record
Abstract
Lanthanides represent a group of very important but challenging analytes for biosensor development. These 15 elements are very similar in their chemical properties. So far, limited success has been realized using the rational ligand design approach. My laboratory has successfully accomplished the task of carrying out combinatorial selection to isolate lanthanide-dependent RNA-cleaving DNAzymes. We report two new DNAzymes, each discovered in a different selection condition and both are highly specific to lanthanides. When both DNAzymes are used together, it is possible to identify the last few heavy lanthanides. Upon introducing a phosphorothioate modification, one of the abovementioned DNAzymes becomes highly active with many toxic heavy metals. With the selection of more DNAzymes with different activity patterns cross the lanthanide series, a sensor array might be produced for identifying each ion. This article is a minireview of the current developments on this topic and some of the historical aspects. It reflects the main content of the Fred Beamish Award presentation delivered at the 2014 Canadian Society for Chemistry Conference in Vancouver. Future directions in this area are also discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".