Bibliographic record
Abstract
Octopuses are geniuses: they can unscrew jars from the inside, solve mazes and pull off Houdini-like feats of disguise and escape. Bizarrely, there aren't many compelling explanations for how octopuses are so smart; although their brains are large, the genes and proteins used to build them are similar to those of less intelligent invertebrates such as oysters. Fascinated by the mysterious ‘how’ of the cephalopod nervous system, an international team of researchers led by Grygoriy Zolotarov, affiliated with the Max Delbrück Center for Molecular Medicine, Germany, used bioinformatics to delve into the mind of the octopus. Their work suggests that the brilliance of the octopus lies not in what they have but in how they use it.All of the information required to build cells is stored within the nucleus in the DNA, and this is translated into messenger RNA (mRNA) for protein synthesis. Perhaps, reasoned the researchers, octopuses were modifying their mRNA in some way to use their genes in unorthodox ways, allowing them to construct their bizarre nervous system. But when they tested their hypothesis on the common octopus, Octopus vulgaris, it fell apart. After characterizing the amount and types of mRNA in 18 different tissues, including areas of the brain and a cluster of nerve cells in the gut, the researchers realized that the octopus's mRNA didn't have many unusual features that made the molecules stand out from the mRNA of other invertebrates – except for one thing. Many of the octopus mRNAs had unusually long tails compared with those of other invertebrates. Tails are an important part of mRNA, as proteins and other molecules use them as handles to grab onto the molecules. A longer tail could change how that process works and add an extra layer of control to the mRNA.The researchers turned their attention to one well-known type of tail-grabber: microRNA (miRNA). These are tiny, hairpin-shaped molecules that can mark mRNA for disposal or prevent the message from being translated in protein by cellular machines. By halting protein production, miRNAs control the kinds and amounts of proteins that cells make. Octopuses have an extraordinary repertoire of miRNAs with at least 164 miRNA genes belonging to 138 families at their disposal, compared with oysters, which have only 20, putting octopuses on a par with zebrafish and chickens. miRNA expansions of this degree are exceedingly rare, having only been observed in the intellectual rivals of octopuses: vertebrates.How much of this expanded miRNA repertoire is concerned with brain power? A lot. Thirty-four of the 43 miRNA families only found in octopuses (and not in close relatives such as squids) are concentrated in neural tissue. Many of these miRNAs were super active in embryos and hatchlings: exactly as expected if they were crucial for the development of a complex nervous system. And, when the team compared the common octopus's miRNAs with those of a far-flung relative, the California two-spot octopus (Octopus bimaculoides), they were quite similar, despite 50 million years of evolution between them, suggesting that they were useful enough to keep (or at least not worth throwing out). Overall, the new miRNAs showed up in the right places and at the right times to imply that they played key roles in nervous system development and evolution.In short, it seems that great minds think alike. While we can't know what chickens, zebrafish or octopuses are really thinking, it seems that they all owe some of their brainpower to vast repertories of miRNAs. The alien brain of the octopus is perhaps not so alien after all.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".