Stepwise loss of motilin and its specific receptor genes in rodents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Specific interactions among biomolecules drive virtually all cellular functions and underlie phenotypic complexity and diversity. Biomolecules are not isolated particles, but are elements of integrated interaction networks, and play their roles through specific interactions. Simultaneous emergence or loss of multiple interacting partners is unlikely. If one of the interacting partners is lost, then what are the evolutionary consequences for the retained partner? Taking advantages of the availability of the large number of mammalian genome sequences and knowledge of phylogenetic relationships of the species, we examined the evolutionary fate of the motilin (MLN) hormone gene, after the pseudogenization of its specific receptor, MLN receptor (MLNR), on the rodent lineage. We speculate that the MLNR gene became a pseudogene before the divergence of the squirrel and other rodents about 75 mya. The evolutionary consequences for the MLN gene were diverse. While an intact open reading frame for the MLN gene, which appears functional, was preserved in the kangaroo rat, the MLN gene became inactivated independently on the lineages leading to the guinea pig and the common ancestor of the mouse and rat. Gain and loss of specific interactions among biomolecules through the birth and death of genes for biomolecules point to a general evolutionary dynamic: gene birth and death are widespread phenomena in genome evolution, at the genetic level; thus, once mutations arise, a stepwise process of elaboration and optimization ensues, which gradually integrates and orders mutations into a coherent pattern.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it