Quantitative surface-enhanced Raman spectroscopy of single bases in oligodeoxynucleotides
Bibliographic record
Abstract
. Initially, synthetic oligodeoxynucleotides with systematic structural variations were used to investigate the effect of adding single nucleobases to the 3' terminus of 10-mer and 20-mer sequences. It was found that the resulting SERS difference spectra could be used to identify the added nucleobases since they closely matched reference spectra of the same nucleobase. Investigation of the variation in intensity of an adenine probe which was moved along a test sequence showed there was a small end effect where nucleobases near the 3' terminus gave slightly larger signals but the effect was minor (30%). More significantly, in a sample set comprising 25-mer sequences where A, T or G nucleobases were substituted either near the centres of the sequences or the 5' or 3' ends, the SERS difference spectra only matched the expected form in approximately half the cases tested. This variation appeared to be due to changes in secondary structure induced by altering the sequences since uncoiling the sequences in a thermal pre-treatment step gave difference spectra which in all cases matched the expected form. Multivariate analysis of the set of substitution data showed that 99% of the variance could be accounted for in a model with just three factors whose loadings matched the spectra of the A, T, and G nucleobases and which contained no positional information. This suggests that aside from the differences in secondary structure which can be eliminated by thermal pre-treatment, the SERS spectra of the 25-mers studied here are simply the sum of their component parts. Although this means that SERS provides very little information on the primary sequence it should be excellent for the detection of post-transcription modifications to DNA which can occur at multiple positions along a given sequence.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".