Impact of Pre-Extraction Methods on Apple Blossom Microbiome Analysis
Bibliographic record
Abstract
This study examines the effect of pre-extraction methods, namely, sonication, grinding, and lyophilization, and the use of peptide nucleic acid (PNA) blockers on the DNA recovery, diversity, and taxonomic resolution of bacterial and fungal communities in apple blossoms. Sonication was the most successful in recovering bacterial 16S and fungal ITS reads across all the collection points and plots. Lyophilization and grinding led to a significant reduction in fungal read counts, while PNA enhanced the recovery of bacterial 16S reads. Sonication improved the efficiency of DNA extraction and yielded greater diversity in the recovered microbial community. Sonicated samples showed greater sensitivity to temporal shifts in microbial community composition. Communities in sonicated samples contained a larger number of bacterial genera, such as Bacillus, Staphylococcus, and Erwinia, and fungal genera, including Didymellaceae and Cladosporium. In contrast, lyophilization and grinding led to a reduction in detected taxa. The indicator species analysis determined that 35 bacterial and 21 fungal genera were closely related to sonication, whereas no other pre-extraction method had any associated genera. Our findings suggest that sonication is the most appropriate pre-extraction method for analyzing blossom-associated microbiomes, and that the use of PNA blockers can improve the recovery of bacteria and minimize contamination by host DNA.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".