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Record W4393873397 · doi:10.5281/zenodo.3382318

Preliminary investigation of caspase6 cleavage of HTT and HTT-HAP40 2019/07/15

2019· dataset· en· W4393873397 on OpenAlexaff
Rachel Harding, C.H. Arrowsmith, A.M. Edwards

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typedataset
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCleavage (geology)GeologyPaleontology

Abstract

fetched live from OpenAlex

Project: Structural and functional analysis of huntingtin protein Experiment: Preliminary investigation of caspase6 cleavage of HTT and HTT-HAP40 Date: 2019/07/15 Background: HTT is cleaved by numerous enzymes to generate protein fragments, many of which have implications for disease pathogenesis (reviewed by Saudou et al (2016) Neuron). However, very few of these proteases have been assessed for their cleavage of purified HTT protein or HTT-HAP40 protein samples. Caspase-6 cleavage of HTT has been reported to generate a aa. 1-586 fragment. However, it is not clear how HAP40 binding of HTT might affect cleavage by caspase-6 or release of this fragment from the complex structure. The caspase-6 cleavage site is in the intrinsically disordered region, distal from the globular structure. How cleavage might release a 1-586 fragment from the remainder of the structure remains incompletely understood. Rationale: In this experiments I am aiming to optimise conditions for caspase-6 cleavage to test how proteolytic cleavage of HTT affects “pathogenic” fragment formation for apo vs HAP40-bound HTT.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.042
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0420.029

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 head0.050
GPT teacher head0.287
Teacher spread0.238 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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