Quantitative Membrane Loading of Polymer Vesicles

We utilize a series of structurally homologous, multi-porphyrin-based, fluorophores (PBFs) in order to explore the capacity of polymer vesicles (polymersomes) to stably incorporate large hydrophobic molecules, non-covalently within their thick lamellar membranes. Through aqueous hydration of dry, uniform thin-films of amphiphilic polymer and PBF species deposited on Teflon, self-assembled polymersomes are readily generated incorporating the hydrophobic fluorophores in prescribed molar ratios within their membranes. The size-dependent spectral properties of the PBFs allow for ready optical verification (via steady-state absorption and emission spectroscopy) of the extent of vesicle membrane loading and enable delineation of intermembranous molecular interactions. The resultant effects of PBF membrane-loading on polymersome thermodynamic and mechanical stability are further assessed by cryogenic transmission electron microscopy (cryo-TEM) and micropipet aspiration, respectively. We demonstrate that polymersomes can be loaded at up to 10 mol/wt% concentrations, with hydrophobic molecules that possess sizes comparable to those of large pharmaceutical conjugates (e.g. ranging 1.4–5.4 nm in length and Mw = 0.7–5.4 kg mol–1), without significantly compromising the robust thermodynamic and mechanical stabilities of these synthetic vesicle assemblies. Due to membrane incorporation, hydrophobic encapsulants are effectively prevented from self-aggregation, able to be highly concentrated in aqueous solution, and successfully shielded from deleterious environmental interactions. Together, these studies present a generalized paradigm for the generation of complex multi-functional materials that combine both hydrophilic and hydrophobic agents, in mesoscopic dimensions, through cooperative self-assembly

Mechanoenzymatic Breakdown of Chitinous Material to N-Acetylglucosamine: The Benefits of a Solvent-Free Environment

We report an innovative and efficient mechanoenzymatic method to hydrolyze chitin to N-acetylglucosamine monomer using chitinase under the recently developed RAging methodology, i.e. a combination of mechanochemical ball milling activation and accelerated aging repeated over many cycles, in the absence of bulk solvent. Our result show that the activity of chitinases increases several times by switching from traditional solvent-based conditions of enzymatic catalysis to solvent-free RAging, which operates on moist solid substrates. Importantly, RAging is also highly efficient for the production of N-actylglucosamine directly from shrimp and crab shell biomass without any other processing, except for a gentle wash with aqueous acetic acid.<br /

A deep learning framework for neuroscience

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress