12,927 research outputs found

    Computational Screening of Tip and Stalk Cell Behavior Proposes a Role for Apelin Signaling in Sprout Progression

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    Angiogenesis involves the formation of new blood vessels by sprouting or splitting of existing blood vessels. During sprouting, a highly motile type of endothelial cell, called the tip cell, migrates from the blood vessels followed by stalk cells, an endothelial cell type that forms the body of the sprout. To get more insight into how tip cells contribute to angiogenesis, we extended an existing computational model of vascular network formation based on the cellular Potts model with tip and stalk differentiation, without making a priori assumptions about the differences between tip cells and stalk cells. To predict potential differences, we looked for parameter values that make tip cells (a) move to the sprout tip, and (b) change the morphology of the angiogenic networks. The screening predicted that if tip cells respond less effectively to an endothelial chemoattractant than stalk cells, they move to the tips of the sprouts, which impacts the morphology of the networks. A comparison of this model prediction with genes expressed differentially in tip and stalk cells revealed that the endothelial chemoattractant Apelin and its receptor APJ may match the model prediction. To test the model prediction we inhibited Apelin signaling in our model and in an \emph{in vitro} model of angiogenic sprouting, and found that in both cases inhibition of Apelin or of its receptor APJ reduces sprouting. Based on the prediction of the computational model, we propose that the differential expression of Apelin and APJ yields a "self-generated" gradient mechanisms that accelerates the extension of the sprout.Comment: 48 pages, 10 figures, 8 supplementary figures. Accepted for publication in PLoS ON

    In vivo imaging of the tonoplast intrinsic protein family in Arabidopsis roots

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    Background: Tonoplast intrinsic proteins (TIPs) are widely used as markers for vacuolar compartments in higher plants. Ten TIP isoforms are encoded by the Arabidopsis genome. For several isoforms, the tissue and cell specific pattern of expression are not known. Results: We generated fluorescent protein fusions to the genomic sequences of all members of the Arabidopsis TIP family whose expression is predicted to occur in root tissues (TIP1;1 and 1;2; TIP2;1, 2;2 and 2;3; TIP4;1) and expressed these fusions, both individually and in selected pairwise combinations, in transgenic Arabidopsis. Analysis by confocal microscopy revealed that TIP distribution varied between different cell layers within the root axis, with extensive co-expression of some TIPs and more restricted expression patterns for other isoforms. TIP isoforms whose expression overlapped appeared to localise to the tonoplast of the central vacuole, vacuolar bulbs and smaller, uncharacterised structures. Conclusion: We have produced a comprehensive atlas of TIP expression in Arabidopsis roots, which reveals novel expression patterns for not previously studied TIPs

    discrimination of different serbian pronunciations from shtokavian dialect

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    Abstract This paper proposes a new methodology for discrimination of different pronunciations in the Shtokavian dialect of the Serbian language. At the first, the written language (Unicode text) is converted into codes according to the energy status of each character in the text-line. Such a set of codes is seen as a grayscale image. Then, the local structures of the image are explored by local binary operators. It creates a vector set which differentiates various pronunciations of the Serbian language. The experiment is performed on fifty documents given in Serbian language. A comparison performed between the proposed method and the n -gram method shows its clear advantage

    High-throughput simulation studies of angiogenesis : reverse engineering the role of tip cells and pericytes in vascular development

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    Angiogenesis is the process by which new blood vessels develop by splitting of or by sprouting from existing vessels. In sprouting angiogenesis vessels branch out and connect with other sprouts to form a new network. This process involves both the endothelial cells, which make up the inner lining of a vessel, and the perivascular cells, which surround the vessel. The collective behavior of these cells results in the formation of sprouts and eventually vascular networks. The cells involved in angiogenesis differ in shape and behavior, which affects their collective behavior. Furthermore, the cells also affect one another via diffusive and membrane bound signaling molecules. In this thesis we aim to understand how interactions between multiple cell-types exhibiting subtle differences in behavior change the resulting collective angiogenic sprouting. To this end, we developed cell-based, computational models of angiogenesis, based on the cellular Potts model. The inputs of these models are the observed or hypothesized behavior of individual cells and the output is the resulting collective cell behavior: e.g., the formation of angiogenic sprouts or vascular networks. By assigning different behavior to a subset of the cells, these models can be used to study the interplay between cell types exhibiting different behavior.Centrum Wiskunde & Informatica Netherlands Consortium for Systems BiologyUBL - phd migration 201

    Neural Connectivity with Hidden Gaussian Graphical State-Model

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    The noninvasive procedures for neural connectivity are under questioning. Theoretical models sustain that the electromagnetic field registered at external sensors is elicited by currents at neural space. Nevertheless, what we observe at the sensor space is a superposition of projected fields, from the whole gray-matter. This is the reason for a major pitfall of noninvasive Electrophysiology methods: distorted reconstruction of neural activity and its connectivity or leakage. It has been proven that current methods produce incorrect connectomes. Somewhat related to the incorrect connectivity modelling, they disregard either Systems Theory and Bayesian Information Theory. We introduce a new formalism that attains for it, Hidden Gaussian Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS is equivalent to a frequency domain Linear State Space Model (LSSM) but with sparse connectivity prior. The mathematical contribution here is the theory for high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS can attenuate the leakage effect in the most critical case: the distortion EEG signal due to head volume conduction heterogeneities. Its application in EEG is illustrated with retrieved connectivity patterns from human Steady State Visual Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence for noninvasive procedures of neural connectivity: concurrent EEG and Electrocorticography (ECoG) recordings on monkey. Open source packages are freely available online, to reproduce the results presented in this paper and to analyze external MEEG databases
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