Distinct roles of light-activated channels TRP and TRPL in photoreceptors of <i>Periplaneta americana</i>
Bibliographic record
Abstract
Electrophysiological studies in Drosophila melanogaster and Periplaneta americana have found that the receptor current in their microvillar photoreceptors is generated by two light-activated cationic channels, TRP (transient receptor potential) and TRPL (TRP-like), each having distinct properties. However, the relative contribution of the two channel types to sensory information coding by photoreceptors remains unclear. We recently showed that, in contrast to the diurnal Drosophila in which TRP is the principal phototransduction channel, photoreceptors of the nocturnal P. americana strongly depend on TRPL. Here, we perform a functional analysis, using patch-clamp and intracellular recordings, of P. americana photoreceptors after RNA interference to knock down TRP (TRPkd) and TRPL (TRPLkd). Several functional properties were changed in both knockdown phenotypes: cell membrane capacitance was reduced 1.7-fold, light sensitivity was greatly reduced, and amplitudes of sustained light-induced currents and voltage responses decreased more than twofold over the entire range of light intensities. The information rate (IR) was tested using a Gaussian white-noise modulated light stimulus and was lower in TRPkd photoreceptors (28 ± 21 bits/s) than in controls (52 ± 13 bits/s) because of high levels of bump noise. In contrast, although signal amplitudes were smaller than in controls, the mean IR of TRPLkd photoreceptors was unchanged at 54 ± 29 bits/s1 because of proportionally lower noise. We conclude that TRPL channels provide high-gain/high-noise transduction, suitable for vision in dim light, whereas transduction by TRP channels is relatively low-gain/low-noise and allows better information transfer in bright light.
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 | 0.001 |
| 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".