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Record W4380992278 · doi:10.1016/j.elspec.2023.147355

Illuminating the brain: Revealing brain biochemistry with synchrotron X-ray spectromicroscopy

2023· article· en· W4380992278 on OpenAlexfundno aff
James Everett, Jake Brooks, Frederik Lermyte, Vindy Tjendana Tjhin, Ian Hands-Portman, Emily J. Hill, Joanna F. Collingwood, Neil D. Telling

Bibliographic record

VenueJournal of Electron Spectroscopy and Related Phenomena · 2023
Typearticle
Languageen
FieldMedicine
TopicGlycogen Storage Diseases and Myoclonus
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilWestern Economic Diversification CanadaCanadian Institutes of Health ResearchUniversity of WarwickVetenskapsrådetNational Research Council CanadaSvenska Forskningsrådet FormasOffice of ScienceKeele UniversityVINNOVADirectorate for Biological SciencesU.S. Department of EnergyDiamond Light SourceBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaCanadian Light Source
KeywordsSynchrotronMicroscopyHuman brainBrain researchNeuroscienceNanotechnologyX-rayChemistryMedicineMaterials scienceBiologyPathologyPhysicsOptics

Abstract

fetched live from OpenAlex

The synchrotron x-ray spectromicroscopy technique Scanning Transmission X-ray Microscopy (STXM) offers a powerful means to examine the underlying biochemistry of biological systems, owing to its combined chemical sensitivity and nanoscale spatial resolution. Here we introduce and demonstrate methodology for the use of STXM to examine the biochemistry of the human brain. We then discuss how this approach can help us better understand the biochemical changes that occur during the development of degenerative brain disorders, potentially facilitating the development of new therapies for disease diagnosis and treatment.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.256
Teacher spread0.251 · 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
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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Electron Spectroscopy and Related PhenomenaSame topicGlycogen Storage Diseases and MyoclonusFrench-language works237,207