Evaluation of three procedures for recovery of GUS enzyme and colony forming units of a nonpathogenic strain of<i>Fusarium oxysporum</i>, 70T01, from inoculated tomato roots
Bibliographic record
Abstract
Insertion of β-D-glucuronidase (GUS) reporter gene has been found to be useful for detection, quantitation, and monitoring of plant-associated fungi in their environment. GUS was extracted from tomato roots inoculated with a nonpathogenic strain of Fusarium oxysporum, 70T01, that had been genetically modified to express both GUS activity and hygromycin B resistance. To facilitate studies of fungus-plant interactions using the GUS enzyme, we tested several methods for their efficiency of preparation of fungal-encoded GUS from infected plant tissues, namely FastPrep homogenization, grinding of tissues frozen in liquid nitrogen, and extraction from lyophilized material. Of the three procedures, the FastPrep method yielded the highest GUS activity per unit of inoculated root and provided twice the sensitivity of the other methods. This procedure was also the easiest, quickest, and the most reliable. Up to 12 samples could be analyzed in less than 2 h, and as little as 50 mg of fresh tissue was sufficient. Of the factors examined that could affect extraction efficiency, only the length of homogenization and the presence of protein stabilizers (sucrose, bovine serum albumin, and protease inhibitors) in the GUS buffer improved enzyme activity in the extracts. The FastPrep method was also highly effective in enumerating fungal colony forming unit (CFU) populations in the root tissues, provided that the timing and speed of homogenization was controlled. Plating of infected root samples homogenized using the FastPrep equipment and a mortar and pestle yielded about 50 times more CFUs per unit root than the colony counts obtained from whole roots, dried and powdered roots, or lyophilized roots.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".