Cloning Vectors Based on Cryptic Plasmids Isolated from Lactic Acid Bacteria:Their Characteristics and Potential Applications in Biotechnology
Bibliographic record
Abstract
Lactic acid bacteria (LAB) are Gram positive bacteria, widely distributed in nature, and industrially important as they are used in a variety of industrial food fermentations. The use of genetic engineering techniques is an effective means of enhancing the industrial applicability of LAB. However, when using genetic engineering technology, safety becomes an essential factor for the application of improved LAB to the food industry. Cloning and expression systems should be derived preferably from LAB cryptic plasmids that generally encode genes for which functions can be proposed, but no phenotypes can be observed. However, some plasmid-encoded functions have been discovered in cryptic plasmids originating from Lactobacillus, Streptococcus thermophilus, and Pediococcus spp. and can be used as selective marker systems in vector construction. This article presents information concerning LAB cryptic plasmids, and their structures, functions, and applications. A total of 134 cryptic plasmids collated are discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".