Combined imaging and molecular techniques for evaluating microbial function and composition: A review
Bibliographic record
Abstract
In most cases, microbial function will determine the direction and onset of specific metabolic pathways as defined by their favorable thermodynamic outcome. Thus, the degree of chemical alteration in contact with minerals and bacteria can be directly proportional to the biological activity. This activity can influence redox conditions both from a localized perspective and global scale. Under these conditions, microscale mechanisms become important, impacting both molecular diffusion and the distribution of substrates and products. Visualizing microorganisms in their natural environments is no simple task and requires analytical tools that can measure cell function and chemical speciation at the sub‐micrometer level. In the last decade, the scientific community has observed a rapid increase in development of advanced imaging methods (eg, high‐resolution secondary mass spectrometry [NanoSIMS]) and synchrotron‐based approaches such as scanning transmission X‐ray microscopy (STXM). Coupled to culture‐independent techniques (eg, next generation sequencing technologies), these combined approaches excel at exploring microbial/mineral and sediment dynamics leading to valuable insight into both structure and function of single cells and diverse microbial communities in engineered and natural environments. This review focuses on recent advances in high‐resolution imaging and molecular‐based tools used to characterize microbial communities and function within natural systems. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".