Ontogeny of bivalve immunity: assessing the potential of next‐generation sequencing techniques
Bibliographic record
Abstract
Abstract Living organisms are constantly evolving to secure their survival via adaptations at the molecular and cellular level. Most marine bivalves have microscopic planktonic larval stages until settlement to the benthic environment. These pelagic stages are generally more sensitive than their adult counterparts to environmental and pathogen threats. Adaptive capacities could improve survival of these early stages. Recent advancements in data mining and pipeline analysis should shed light on the currently unknown processes that occur during these first stages. Existing data on early stages are fragmented compared with the abundance of information available for adult. Exploring diversity through aquaculture and lessening the impact of common issues, for example, massive mortalities of larvae, especially within the current conditions of a changing climate, ultimately rests on our knowledge of the molecular processes responsible for phenotypic plasticity. Although it is somewhat difficult to assess immune mechanisms by tracking circulating immunocytes in larvae, studies on the development of immune processes are now feasible at the transcript level. Next‐generation techniques offer outstanding solutions for wide‐range transcriptome analysis. We present a short review of the early ontogeny of the immune system in marine bivalves, with particular focus on next‐generation sequencing applications. Like all reviews of this nature, there is a trade‐off between the depth of the coverage and the number of subjects discussed. We will thus restrict the scope to bivalve immunity and focus on the central concepts across a wide range of topics, that is, the ontogeny of immunity and advancements in molecular studies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".