Aplysia Cell Adhesion Molecule and a Novel Protein Kinase C Activity in the Postsynaptic Neuron Are Required for Presynaptic Growth and Initial Formation of Specific Synapses
Bibliographic record
Abstract
To explore the role of both Aplysia cell adhesion molecule (ApCAM) and activity of specific protein kinase C (PKC) isoforms in the initial formation of sensory neuron synapses with specific postsynaptic targets (L7 but not L11), we examined presynaptic growth, initial synapse formation, and the expression of the presynaptic neuropeptide sensorin following cell-specific reduction of ApCAM or of a novel PKC activity. Synapse formation between sensory neurons and L7 begins by 3 h after plating and is accompanied by a rapid accumulation of a novel PKC to sites of synaptic interaction. Reducing ApCAM expression specifically from the surface of L7 blocks presynaptic growth and initial synapse formation, target-induced increase of sensorin in sensory neuron cell bodies and the rapid accumulation of the novel PKC to sites of interaction. Selective blockade of the novel PKC activity in L7, but not in sensory neurons, with injection of a dominant negative construct that interferes with the novel PKC activity, produces the same actions as downregulating ApCAM; blockade of presynaptic growth and initial synapse formation, and the target-induced increase of sensorin in sensory neuron cell bodies. The results indicate that signals initiated by postsynaptic cell adhesion molecule ApCAM coupled with the activation of a novel PKC in the appropriate postsynaptic neuron produce the retrograde signals required for presynaptic growth associated with initial synapse formation, and the target-induced expression of a presynaptic neuropeptide critical for synapse maturation.
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 | 0.000 | 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".