The effect of lipid oxidation on water permeability of phosphatidylcholine membranes was investigated by means of both scattering stopped flow experiments and atomistic molecular dynamics simulations. Formation of water pores followed by a significant enhancement of water permeability was observed. The molecules of oxidized phospholipids facilitate pore formation and subsequently stabilize water in the membrane interior. A wide range of oxidation ratios, from 15 to 100 mol%, was considered. The degree of oxidation was found to strongly influence the time needed for the opening of a pore. In simulations, the oxidation ratio of 75 mol% was found to be a threshold for spontaneous pore formation in the tens of nanosecond timescale, whereas 15 mol% of oxidation led to significant water permeation in the timescale of seconds. Once a pore was formed, the water permeability was found to be virtually independent of the oxidation ratio.
In traditional generative modeling, good data representation is very often a base for a good machine learning model. It can be linked to good representations encoding more explanatory factors that are hidden in the original data. With the invention of Generative Adversarial Networks (GANs), a subclass of generative models that are able to learn representations in an unsupervised and semi-supervised fashion, we are now able to adversarially learn good mappings from a simple prior distribution to a target data distribution. This paper presents an overview of recent developments in GANs with a focus on learning latent space representations.
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