Considerations in the use of fluorescence in situ hybridization (FISH) and confocal laser scanning microscopy to characterize rumen methanogens and define their spatial distributions
Bibliographic record
Abstract
In this study, methanogen-specific coenzyme F420 autofluorescence and confocal laser scanning microscopy were used to identify rumen methanogens and define their spatial distribution in free-living, biofilm-, and protozoa-associated microenvironments. Fluorescence in situ hybridization (FISH) with temperature-controlled hybridization was used in an attempt to describe methanogen diversity. A heat pretreatment (65 °C, 1 h) was found to be a noninvasive method to increase probe access to methanogen RNA targets. Despite efforts to optimize FISH, 16S rRNA methanogen-specific probes, including Arch915, bound to some cells that lacked F420, possibly identifying uncharacterized Methanomassiliicoccales or reflecting nonspecific binding to other members of the rumen bacterial community. A probe targeting RNA from the methanogenesis-specific methyl coenzyme M reductase (mcr) gene was shown to detect cultured Methanosarcina cells with signal intensities comparable to those of 16S rRNA probes. However, the probe failed to hybridize with the majority of F420-emitting rumen methanogens, possibly because of differences in cell wall permeability among methanogen species. Methanogens were shown to integrate into microbial biofilms and to exist as ecto- and endosymbionts with rumen protozoa. Characterizing rumen methanogens and defining their spatial distribution may provide insight into mitigation strategies for ruminal methanogenesis.
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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 | 0.046 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| 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".