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Record W2291225689 · doi:10.1111/jtsa.12177

Quantitative Risk Management: Concepts, Techniques and Tools, by Alexander J.McNeil, RüdigerFrey and PaulEmbrechts. Revised edition. Published by Princeton University Press, 2015. Total number of pages: 720. ISBN: 978‐0‐691‐16627‐8 (Hardback)

2016· article· en· W2291225689 on OpenAlexafffund
Johanna Nešlehová

Bibliographic record

VenueJournal of Time Series Analysis · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicForecasting Techniques and Applications
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéArthritis SocietyCanadian Institutes of Health ResearchAlzheimer SocietyMcGill UniversityFaculty of Medicine, McGill UniversityNatural Sciences and Engineering Research Council of CanadaPfizer
KeywordsLibrary scienceCitationMathematicsComputer science

Abstract

fetched live from OpenAlex

The amyloid precursor protein (APP) is an ubiquitously expressed cell surface protein and a key molecule in the etiology of Alzheimer disease. Amyloidogenic processing of APP through secretases leads to the generation of toxic amyloid (A) peptides, which are regarded as the molecular cause of the disease. We report here an alternative processing pathway of APP through the mammalian intramembrane rhomboid protease RHBDL4. RHBDL4 efficiently cleaves APP inside the cell, thus bypassing APP from amyloidogenic processing, leading to reduced A levels. RHBDL4 cleaves APP multiple times in the ectodomain, resulting in several N-and C-terminal fragments that are not further degraded by classical APP secretases. Knockdown of endogenous RHBDL4 results in decreased levels of C-terminal fragments derived from endogenous APP. Similarly, we found the APP family members APLP1 and APLP2 to be substrates of RHBDL4. We conclude that RHBDL4-mediated APP processing provides insight into APP and rhomboid physiology and qualifies for further investigations to elaborate its impact on Alzheimer disease pathology.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.316
Teacher spread0.291 · 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.

Study designNot applicable
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

Citations3
Published2016
Admission routes2
Has abstractyes

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