Decoding Gene Networks Modules That Explain the Recovery of Hymenoglossum cruentum Cav. After Extreme Desiccation
Bibliographic record
Abstract
Hymenoglossum cruentum (Hymenophyllaceae) is a poikilohydric, homoiochlorophyllous desiccation-tolerant (DT) epiphyte fern. It can undergo fast and frequent dehydration-rehydration cycles. This fern is highly abundant at high-humidity/low-light microenvironments within the canopy, although rapid changes in humidity and light intensity are frequent. The objective of this study is to study the transcriptome of H. cruentum to better understand the genetic dynamics behind its desiccation tolerance mechanism. H. cruentum plants were subjected to a seven days long desiccation-rehydration process and then used to identify the expression of key genes associated to its capacity to dehydrate and rehydrate. The relative water content (RWC) and maximum quantum efficiency (Fv/Fm) of H. cruentum fronds decayed to 6% and 0.04, respectively, at the end of the desiccation stage. After re-watering, the fern showed a rapid recovery of RWC and Fv/Fm (ca. 73% and 0.8, respectively). Based on clustering and network analysis on transcriptome data, our results reveal key genes orchestrating a preventive state at full hydrated; strong balance between avoiding cell death and defense when dehydrated, and detoxifying pathways and stabilization of photosystems during rehydration. Here we provide novel insights into the genetic dynamics behind the desiccation tolerance mechanism of H. cruentum.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".