You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Chemical"].

Please return the output as a JSON object of the format: {"Chemical": ["Arsenic", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Tracking the mechanism of fibril assembly by simulated two-dimensional ultraviolet spectroscopy. Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the accumulation of plaque deposits in the human brain. The main component of these plaques consists of highly ordered structures called amyloid fibrils, formed by the amyloid β-peptide (Aβ). The mechanism connecting Aβ and AD is yet undetermined. In a previous study, a coarse-grained united-residue model and molecular dynamics simulations were used to model the growth mechanism of Aβ amyloid fibrils. On the basis of these simulations, a dock/lock mechanism was proposed, in which Aβ fibrils grow by adding monomers at either end of an amyloid fibril template. To examine the structures in the early time-scale formation and growth of amyloid fibrils, simulated two-dimensional ultraviolet spectroscopy is used. These early structures are monitored in the far ultraviolet regime (λ = 190-250 nm) in which the computed signals originate from the backbone nπ* and ππ* transitions. These signals show distinct cross-peak patterns that can be used, in combination with molecular dynamics, to monitor local dynamics and conformational changes in the secondary structure of Aβ-peptides. The protein geometry-correlated chiral xxxy signal and the non-chiral combined signal xyxy-xyyx were found to be sensitive to, and in agreement with, a dock/lock pathway.
{"Chemical": []}