A shared calcium profile needs error factor 1.233 for five reported ribbon pairs from one zebrafish cell
Can one positive scale per synaptic ribbon describe calcium near and farther from its base? We test a common spatial profile with published calcium summaries from five ribbons in one zebrafish rod bipolar cell. The source used 10 mM EGTA and a low-affinity ribbon-bound indicator. Distal/proximal ratios range from 0.364 to 0.843. An exact formula and an independent linear program give a smallest uniform entry error factor of 1.233. Both values in each pair may change within this factor. Fixed positive gains for ribbons and locations do not change the result. Removing ribbon 2 lowers the factor to 1.132. The workbook gives identical stored repeat values within each ribbon and location. It cannot establish independent repeat noise, so we cancelled the planned repeat-based test. The result is a deterministic bound for reported values from one cell. It does not estimate biological noise, transmitter release, or a population effect. The package preserves the source workbook, label guides, data provenance, all results, figures, the analysis plan, and a reusable solver for positive matrices with missing entries.
ribbon synapse · calcium · structure and function · retina · zebrafish · positive matrix · error bound · data provenance
References
4- usesdoi:10.7554/eLife.105875.4
We use Figure 8E/F ribbon labels, the calcium measurement definitions, and the two source label guides. The original paper established variation among ribbons.
- usesdoi:10.5061/dryad.qfttdz0vh
We use dataset version 3, API version 404901, source file 4436514. The full workbook SHA-256 matches the source record. The data are CC0 1.0.
- doi:10.3103/S106345411802005X
This is prior work on positive rank-one matrix approximation with a uniform error on the log scale. We do not claim a new factorization method.
- doi:10.21638/11701/spbu10.2020.402
This is prior work on positive matrix approximation with missing entries and bounded factors. Our code independently implements a sparse linear program.
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