Author response: Synergistic integration of Netrin and ephrin axon guidance signals by spinal motor neurons
Bibliographic record
Abstract
The ability of animals to walk and perform skilled movements depends on particular groups of muscles contracting in a coordinated manner. Muscles are activated by nerve cells called motor neurons found in the spinal cord. The connections between the motor neurons and muscles are established in the developing embryo. Each motor neuron produces a long projection called an axon whose growth is guided towards the target muscle by signal proteins. The motor neurons are exposed to many such signal proteins at the same time and it is not clear how they integrate all this information so that their axons target the correct muscles. Poliak, Morales et al. used a variety of genetic and biochemical approaches to study the formation of motor neuron and muscle connections in the limbs of mice and chicks. The experiments show that a signal protein called Netrin-1 is produced in the limbs of developing embryos and attracts the axons of some types of motor neurons and repels others. This is due to the motor neurons producing different types of receptor proteins to detect Netrin-1. Further experiments show that individual axons can combine information from attractive or repulsive Netrin-1 signals together with repulsive signals from another family of proteins called ephrins in a 'synergistic' manner. That is, the combined effect of both cues is stronger than their individual effects added together. This synergy involves ligand-dependent interactions between the Netrin-1 and ephrin receptor proteins, and the activation of a common enzyme. Poliak, Morales et al.’s findings reveal a new role for Netrin-1 in guiding the development of motor neurons in the limb. Future work will focus on further understanding the mechanism of synergy between Netrin-1 and ephrins. Netrin-1 and ephrins are also involved in the formation of blood vessels and many other developmental processes, so understanding how they work together would have a wide-reaching impact on research into human health and disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.402 | 0.191 |
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".