Exploitation of the coil-globule plasmid DNA transition induced by small changes in temperature, pH salt, and poly(ethylene glycol) compositions for directed partitioning in aqueous two-phase systems

In this study, the interplay of two linked equilibria is examined, one concerning an aqueous two-phase system (ATPS) composed of poly(ethylene glycol) (PEG) and salt employed to partition plasmid DNA (pDNA), and the other a potential structural transition of pDNA depending on PEG and salt concentration and other system parameters. The boundary conditions for pDNA partitioning are set by PEG and salt concentrations, PEG molecular weight, pH, and temperature. While investigating these parameters, it was found that a small increase/decrease of the respective values led to a drastic and significant change in pDNA behavior. This behavior could be attributed to a coil-globule transition of the pDNA triggered by the respective phase conditions. The combination of this structural change, aggregation effects linked to the transition process, and the electrostatic potential difference found in PEG-salt systems thus offers a sensitive way to separate nucleic acid forms on the basis of their unique property to undergo coil-globule transitions under distinct system properties

The Lov\'asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks

The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance - which lends appropriate relevance to small objects, and appropriate counting of false negatives, in comparison to per-pixel losses. We present a method for direct optimization of the mean intersection-over-union loss in neural networks, in the context of semantic image segmentation, based on the convex Lov\'asz extension of submodular losses. The loss is shown to perform better with respect to the Jaccard index measure than the traditionally used cross-entropy loss. We show quantitative and qualitative differences between optimizing the Jaccard index per image versus optimizing the Jaccard index taken over an entire dataset. We evaluate the impact of our method in a semantic segmentation pipeline and show substantially improved intersection-over-union segmentation scores on the Pascal VOC and Cityscapes datasets using state-of-the-art deep learning segmentation architectures.Comment: Accepted as a conference paper at CVPR 201