Ionic currents in the myoepithelium of <i>Aglantha digitale</i>
Bibliographic record
Abstract
When “fishing” for prey the jellyfish Aglantha digitale undergoes a series of “slow swims” driven by pacemaker neurones at the base of its bell‐shaped body wall. To avoid predators Aglantha generates an altogether stronger form of “escape” swimming. During an escape swim, the striated muscle sheet that lines the inside of the body wall gives a strong contraction and water is forced from the bell opening. Neuromuscular synapses are distributed widely within this myoepithelium. An overshooting Na + ‐dependent action potential in each motor axon sets off a large depolarising post‐synaptic potential (psp) with a 1 ms synaptic delay. A spike‐like component on its rising phase initiates contraction. During slow swimming, low amplitude impulses in the motor axons set off a more slowly rising psp and spike in the myoepithelium. Although the properties and functions of different excitable epithelia have been studied extensively, the basis of their epithelial impulses is not well understood. Using the loose patch clamp technique to study the muscle spike in Aglantha , we find that the voltage‐gated inward (Na + and Ca 2+ ) and outward (K + ) currents that form its ionic basis are inactivated within 10 ms. We suggest that differences in the strength of muscle contraction during swimming arise from differences in the rate of rise of the psp. The more slowly rising psp during slow swimming partially inactivates the voltage‐gated currents, producing a reduced Ca 2+ influx and a weaker contraction. This flexible response to synaptic input accommodates the two forms of swimming in Aglantha but may be absent in other medusae, which depend on myoid‐propagated spikes in their striated muscle sheets rather than the presence of distributed synapses.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".