PCA.xlsxDevelopment of multi-species qPCR assays for a stress transcriptional profiling (STP) Chip to assess the resilience of salmonids to changing environments
Bibliographic record
Abstract
Ecologically and socio-economically important salmonid fishes in Canada are threatened by diverse environmental stressors. Using custom-designed nanofluidic stress transcriptional profiling chip (STP-Chip), we tested our developed 112 multi-species Taqman qPCR assays using gill, liver and muscle tissue from eight species of salmonids across four genera. Of the selected 112 assays, 69 assays showed amplification in gill, 64 in liver, and 67 in muscle across all eight salmonid species. In general, the assays performed better in Salmo, Oncorhynchus, and Salvelinus species than Coregonusspecies. Not surprisingly, we observed tissue-specific variation among qPCR amplification patterns. Stress, circadian rhythm, apoptosis, growth-metabolism, and detoxification-relevant assays showed high success rates for amplification across all salmonid species for all three tissues. In contrast, neural plasticity, appetite regulation, osmoregulation, immune function, endocrine disruption, and hypoxia-relevant assays showed poorer amplification, potentially due to the tissues being tested. These qPCR assays can be used to design custom STP-Chips that can be used for quantifying stress in salmonid fishes, and monitoring physiological responses to accelerated climate change regionally and globally
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.376 | 0.088 |
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".