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Record W4288692450 · doi:10.1523/eneuro.0221-22.2022

Hybrid Offspring of C57BL/6J Mice Exhibit Improved Properties for Neurobehavioral Research

2022· article· en· W4288692450 on OpenAlexfundno aff
Hadas E. Sloin, Lior Bikovski, Amir Levi, Ortal Amber-Vitos, Tomer Katz, Lidor Spivak, Shirly Someck, Roni Gattegno, Shir Sivroni, Lucas Sjulson, Eran Stark

Bibliographic record

VenueeNeuro · 2022
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchAzrieli FoundationIsrael Science FoundationUnited States-Israel Binational Science FoundationRosetrees TrustInternational Development Research Centre
KeywordsNeocortexOffspringNeuroscienceHippocampusC57BL/6Genetically modified mouseTransgeneBiologyOptogeneticsPsychologyEndocrinologyGeneticsGene

Abstract

fetched live from OpenAlex

C57BL/6 is the most commonly used mouse strain in neurobehavioral research, serving as a background for multiple transgenic lines. However, C57BL/6 exhibit behavioral and sensorimotor disadvantages that worsen with age. We bred FVB/NJ females and C57BL/6J males to generate first-generation hybrid offspring (FVB/NJ x C57BL/6J)F1. The hybrid mice exhibit reduced anxiety-like behavior, improved learning, and enhanced long-term spatial memory. In contrast to both progenitors, hybrids maintain sensorimotor performance upon aging and exhibit improved long-term memory. The hybrids are larger than C57BL/6J, exhibiting enhanced running behavior on a linear track during freely-moving electrophysiological recordings. Hybrids exhibit typical rate and phase coding of space by CA1 pyramidal cells. Hybrids generated by crossing FVB/NJ females with transgenic males of a C57BL/6 background support optogenetic neuronal control in neocortex and hippocampus. The hybrid mice provide an improved model for neurobehavioral studies combining complex behavior, electrophysiology, and genetic tools readily available in C57BL/6 mice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.304
GPT teacher head0.373
Teacher spread0.069 · 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 teacher head, not a consensus.

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

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

Citations26
Published2022
Admission routes1
Has abstractyes

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