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Record W2322556561 · doi:10.1083/jcb.201507112

The position of lysosomes within the cell determines their luminal pH

2016· article· en· W2322556561 on OpenAlexafffund
Danielle Johnson, Philip P. Ostrowski, Valentin Jaumouillé, Sergio Grinstein

Bibliographic record

VenueThe Journal of Cell Biology · 2016
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsHospital for Sick Children
FundersNational Institutes of HealthCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsPosition (finance)Cell biologyChemistryCellBiophysicsBiologyBusinessBiochemistry

Abstract

fetched live from OpenAlex

We examined the luminal pH of individual lysosomes using quantitative ratiometric fluorescence microscopy and report an unappreciated heterogeneity: peripheral lysosomes are less acidic than juxtanuclear ones despite their comparable buffering capacity. An increased passive (leak) permeability to protons, together with reduced vacuolar H(+)-adenosine triphosphatase (V-ATPase) activity, accounts for the reduced acidifying ability of peripheral lysosomes. The altered composition of peripheral lysosomes is due, at least in part, to more limited access to material exported by the biosynthetic pathway. The balance between Rab7 and Arl8b determines the subcellular localization of lysosomes; more peripheral lysosomes have reduced Rab7 density. This in turn results in decreased recruitment of Rab-interacting lysosomal protein (RILP), an effector that regulates the recruitment and stability of the V1G1 component of the lysosomal V-ATPase. Deliberate margination of lysosomes is associated with reduced acidification and impaired proteolytic activity. The heterogeneity in lysosomal pH may be an indication of a broader functional versatility.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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

Citations607
Published2016
Admission routes2
Has abstractyes

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