Quantitative Comparison and Clustering of Circular Dichroism Spectra Using a Symmetrized Weighted Spectral Difference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spectroscopy (UV-visible, circular dichroism, infrared, Raman, fluorescence, etc.) is of fundamental importance to determine the structures of macromolecules and monitor their stability, especially for drug products, based on proteins or nucleic acids. In their 2014 article, Dinh et al. proposed Weighted Spectral Difference (WSD) as a method to quantitatively compute the dissimilarity of a given spectrum to a reference one. Despite the various properties of this method, its lack of symmetry and dependence on the selection of a reference limits the range of possible applications. Here, we propose a reference-free, symmetrized version of WSD (SWSD) that allows the computation of a semi-distance between two spectra. SWSD can be applied to perform group comparisons, track spectral kinetics, or construct a SWSD matrix leading to the hierarchical clustering of spectra. This method was tested on circular dichroism spectra from a split-virus-based (influenza) vaccine and a recombinant spike protein (COVID-19 vaccine). This approach resulted, first, in a perfect clustering of influenza A and B viruses into two distinct clusters, and second, in the detection of the change of secondary structure of the spike protein during a heating experiment, identifying two main temperatures of denaturation (Tm) by SWSD kinetics, in agreement with results obtained by conventional DSC. In summary, we have shown that SWSD is a versatile and efficient tool for quantitative spectral comparison, tracking spectral kinetics and enabling relevant unsupervised classification.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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 it