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)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".