Use of differential fluorescence induction and optical trapping to isolate environmentally induced genes
Bibliographic record
Abstract
The techniques of differential fluorescence induction (DFI) and optical trapping (OT) have been combined to allow the identification of environmentally induced genes in single bacterial cells. Designated DFI-OT, this technique allows the in situ isolation of genes driving the expression of green fluorescent protein (Gfp) using temporal and spatial criteria. A series of plasmid-based promoter probe vectors (pOT) was developed for the construction of random genomic libraries that are linked to gfpUV or egfp. Bacteria that do not express Gfp on laboratory medium (i.e. non-fluorescent) were inoculated into the environment, and induced genes were detected with a combined fluorescence/optical trapping microscope. Using this selection strategy, rhizosphere-induced genes with homology to thiamine pyrophosphorylase (thiE) and cyclic glucan synthase (ndvB) were isolated. Other genes were expressed late in the stationary phase or as a consequence of surface-dependent growth, including fixND and metX, and a putative ABC transporter of putrescine. This strategy provides a unique ability to combine spatial, temporal and physical information to identify environmental regulation of bacterial gene expression.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".