A quantitative model for efficient construction of lentiviral vectors with a unique clone site
Bibliographic record
Abstract
Abstract Lentiviral vectors (LVs) were a powerful tool for transgene expression in vivo and in vitro. However, the construction of LVs is of low efficiency, due to the large sizes and lack of proper clone sites. Therefore, it is critical to develop efficient strategies for cloning LVs. Here, we reported a combinatorial strategy to efficiently construct LVs using EGFP, hPlk2 wild type (WT) and mutant genes as inserts. Firstly, site-directed mutagenesis (SDM) was performed to create BamH I site for the inserts; secondly, pWPI LV was dephosphorylated after BamH I digestion; finally, the amounts and ratios of the insert and vector DNA were optimized to increase monomeric ligation. Our results showed that the total percentage of positive clones was approximately 51.3%±15.2%. Using this model, almost all the vectors could be constructed through two or three minipreps, therefore, our study provided an efficient quantitative model for constructing large-size vectors.This paper has since been published in Scientific Reports: Zhang, G. & Tandon, A. Quantitative assessment on the cloning efficiencies of lentiviral transfer vectors with a unique clone site. Sci. Rep. 2, 415;DOI:10.1038/srep00415 (2012)
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".