PCR–RFLP analysis of the diversity of phytate-degrading bacteria in the Tibetan Plateau
Bibliographic record
Abstract
Phytases play a very important role in increasing phytate digestion and reducing phosphorus pollution in the environment, and phytate-degrading bacteria have a ubiquitous distribution in the environment. Due to its extremely harsh environment, the Tibetan Plateau breeds possibly abundant, extreme microorganisms. In this research, 67 phytate-degrading bacteria were isolated from different habitats in the Tibetan Plateau. Among all isolates, 40.3% were screened from farmland, 25.3% from wetland, 4.5% from saline-alkaline soil, 7.5% from hot springs, and 22.4% from lawns, which showed that the distribution of the phytate-degrading bacteria varied with habitats. By the PCR-RFLP method, 16 different species were identified and named, 4 of which are reported for the first time as phytate-degrading bacteria, that is, Uncultured Enterococcus sp. GYPB01, Bacillaceae bacterium strain GYPB05, Endophytic bacterium strain GYPB16, and Shigella dysenteria strain GYPB22. Through the assay of phytase activity of 16 strains, Klebsiella sp. strain GYPB15 displayed the highest capability of phytase production. Through analysis of the optimum pH, the optimum temperature, and the thermal stability of enzyme from 16 strains, some especial phytate-degrading bacteria were obtained. Our findings clearly indicate a good relation between the composition of the soils from the different environments in the Tibetan Plateau and populations of cultivable phytate-degrading bacteria. Moreover, extreme harsh soils are logically the best soils in which to find some strains of phytate-degrading bacteria for exploiting in the fields of biotechnology and industry.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".