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Record W4236278089 · doi:10.3410/f.13414965.14789063

Faculty Opinions recommendation of Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain.

2011· dataset· en· W4236278089 on OpenAlexaff
Shimon Amir, Michael Verwey

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2011
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsConnectomicsFluorescenceHippocampal formationDentate gyrusConnectomeNeuroscienceMicroscopyOptical imagingConfocal microscopyScale (ratio)Fluorescence-lifetime imaging microscopyBiologyChemistryPathologyOpticsCell biologyMedicinePhysicsFunctional connectivity

Abstract

fetched live from OpenAlex

Optical methods for viewing neuronal populations and projections in the intact mammalian brain are needed, but light scattering prevents imaging deep into brain structures. We imaged fixed brain tissue using Scale, an aqueous reagent that renders biological samples optically transparent but completely preserves fluorescent signals in the clarified structures. In Scale-treated mouse brain, neurons labeled with genetically encoded fluorescent proteins were visualized at an unprecedented depth in millimeter-scale networks and at subcellular resolution. The improved depth and scale of imaging permitted comprehensive three-dimensional reconstructions of cortical, callosal and hippocampal projections whose extent was limited only by the working distance of the objective lenses. In the intact neurogenic niche of the dentate gyrus, Scale allowed the quantitation of distances of neural stem cells to blood vessels. Our findings suggest that the Scale method will be useful for light microscopy-based connectomics of cellular networks in brain and other tissues. PMID: 21878933

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.037
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0370.034

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.

Opus teacher head0.024
GPT teacher head0.339
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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