3D biofilms: in search of the polysaccharides holding together lichen symbioses
Bibliographic record
Abstract
Stable, long-term interactions between fungi and algae or cyanobacteria, collectively known as lichens, have repeatedly evolved complex architectures with little resemblance to their component parts. Lacking any central scaffold, the shapes they assume are casts of secreted polymers that cement cells into place, determine the angle of phototropic exposure and regulate water relations. A growing body of evidence suggests that many lichen extracellular polymer matrices harbor unicellular, non-photosynthesizing organisms (UNPOs) not traditionally recognized as lichen symbionts. Understanding organismal input and uptake in this layer is key to interpreting the role UNPOs play in lichen biology. Here, we review both polysaccharide composition determined from whole, pulverized lichens and UNPOs reported from lichens to date. Most reported polysaccharides are thought to be structural cell wall components. The composition of the extracellular matrix is not definitively known. Several lines of evidence suggest some acidic polysaccharides have evaded detection in routine analysis of neutral sugars and may be involved in the extracellular matrix. UNPOs reported from lichens include diverse bacteria and yeasts for which secreted polysaccharides play important biological roles. We conclude by proposing testable hypotheses on the role that symbiont give-and-take in this layer could play in determining or modifying lichen symbiotic outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".