Morphological, physiological, and transcriptional responses of the freshwater diatom Fragilaria crotonensis to elevated pH conditions
Bibliographic record
Abstract
Harmful algal blooms (HABs) caused by the toxin-producing cyanobacteriaMicrocystisspp., can increase water column pH. While the effect(s) of these basified conditions on the bloom formers are a high research priority, how these pH shifts affect other biota remains understudied. Recently, it was shown these high pH levels decrease growth and Si deposition rates in the freshwater diatomFragilaria crotonensisand natural Lake Erie (Canada-US) diatom populations. However, the physiological mechanisms and transcriptional responses of diatoms associated with these observations remain to be documented. Here, we examinedF. crotonensiswith a set of morphological, physiological, and transcriptomic tools to identify cellular responses to high pH. We suggest 2 potential mechanisms that may contribute to morphological and physiological pH effects observed inF. crotonensis. Moreover, we identified a significant upregulation of mobile genetic elements in theF. crotonensisgenome which appear to be an extreme transcriptional response to this abiotic stress to enhance cellular evolution rates–a process we have termed “genomic roulette.” We discuss the ecological and biogeochemical effects high pH conditions impose on fresh waters and suggest a means by which freshwater diatoms such asF. crotonensismay evade high pH stress to survive in a “basified” future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".