7,841 research outputs found

    Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data

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    Impact of Cholesterol on Voids in Phospholipid Membranes

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    Free volume pockets or voids are important to many biological processes in cell membranes. Free volume fluctuations are a prerequisite for diffusion of lipids and other macromolecules in lipid bilayers. Permeation of small solutes across a membrane, as well as diffusion of solutes in the membrane interior are further examples of phenomena where voids and their properties play a central role. Cholesterol has been suggested to change the structure and function of membranes by altering their free volume properties. We study the effect of cholesterol on the properties of voids in dipalmitoylphosphatidylcholine (DPPC) bilayers by means of atomistic molecular dynamics simulations. We find that an increasing cholesterol concentration reduces the total amount of free volume in a bilayer. The effect of cholesterol on individual voids is most prominent in the region where the steroid ring structures of cholesterol molecules are located. Here a growing cholesterol content reduces the number of voids, completely removing voids of the size of a cholesterol molecule. The voids also become more elongated. The broad orientational distribution of voids observed in pure DPPC is, with a 30% molar concentration of cholesterol, replaced by a distribution where orientation along the bilayer normal is favored. Our results suggest that instead of being uniformly distributed to the whole bilayer, these effects are localized to the close vicinity of cholesterol molecules

    DenMune: Density peak based clustering using mutual nearest neighbors

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    Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm, DenMune is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neighborhoods of size K, where K is the only parameter required from the user, besides obeying the mutual nearest neighbor consistency principle. The algorithm is stable for a wide range of values of K. Moreover, it is able to automatically detect and remove noise from the clustering process as well as detecting the target clusters. It produces robust results on various low and high-dimensional datasets relative to several known state-of-the-art clustering algorithms.Comment: pyMune is a Python package that implements this clustering algorithm proposed in this paper, DenMune. It is opensource and reproducible, doi:10.1016/j.simpa.2023.10056
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