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Biological membranes are highly complex architectures.
Deep learning deals with deep and complex architectures (Bengio 2009; Schmidhuber 2015).
These techniques can also reproduce highly complex architectures in a relatively short time without using a mould.
In principle, self-assembly of AuNPs via well-designed interfaces may be useful for fabrications of other complex architectures.
This implies that the cytotoxic effects of complex architectures are most likely predominated by the nanoscale building blocks.
Bioprinting technology aims to generate accurately controlled organized assemblies and resemble the complex architectures of native tissues.
These copolymer chains with regularly-distributed reactive functions can be integrated into more complex architectures.
Native tissues present complex architectures at the micro- and nanoscale that dictate their biological function.
This paper describes a graph-based approach to model complex architectures.
Self-assembly formation is a crucial process for building those complex architectures.
These are also known the brushes with much more complex architectures, such as star-like brushes.
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