Author Correction: Optimizing the synthesis and purification of MS2 virus like particles
Bibliographic record
Abstract
The original version of this Article contained an error in Figure 4E, where thymine was used in place of uracil. The original Figure 4 and its accompanying legend appear below. Figure 4 VLPs produced in the NaNO3-Tris were more stable than VLPs produced in the NaNO3 solution. ( a ) Protein concentration evaluation of VLP suspension in two buffers. The amount of protein was reported as mg ml-1. ( b ) Number of VLPs in 1 ml VLP suspension according to the protein concentration. The values are from repeated measurements of samples through 12 months. ( c ) The scattered plot of VLP size in NaNO3 solution and NaNO3-Tris buffer (100 mM NaNO3 and pH: 8, for both of them). The size was analyzed 10 times in each sample and was shown as median with range. ( d ) Gel electrophoresis for RT-PCR fragments (99, 138, 177, and 216 bp); RT-PCR fragments of RNA extracted from VLPs in NaNO3-Tris (pH: 8)- left, and VLPs in NaNO3 (pH: 8)- right, after 3dyas, 3, 9, and 12 months. ( e ) The sequence of shRNA and four hairpins ( pac sites) was packed in MS2 VLPs. Primer locations were shown as Fw and Rev on the sequence. The stability of VLPs and their ability to protect the inner shRNA was checked by RNA extraction and RT-PCR. Full size image
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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.020 |
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".