2,894 research outputs found

    Polycomb group protein complexes exchange rapidly in living Drosophila

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    Fluorescence recovery after photobleaching (FRAP) microscopy was used to determine the kinetic properties of Polycomb group (PcG) proteins in whole living Drosophila organisms (embryos) and tissues (wing imaginal discs and salivary glands). PcG genes are essential genes in higher eukaryotes responsible for the maintenance of the spatially distinct repression of developmentally important regulators such as the homeotic genes. Their absence, as well as overexpression, causes transformations in the axial organization of the body. Although protein complexes have been isolated in vitro, little is known about their stability or exact mechanism of repression in vivo. We determined the translational diffusion constants of PcG proteins, dissociation constants and residence times for complexes in vivo at different developmental stages. In polytene nuclei, the rate constants suggest heterogeneity of the complexes. Computer simulations with new models for spatially distributed protein complexes were performed in systems showing both diffusion and binding equilibria, and the results compared with our experimental data. We were able to determine forward and reverse rate constants for complex formation. Complexes exchanged within a period of 1-10 minutes, more than an order of magnitude faster than the cell cycle time, ruling out models of repression in which access of transcription activators to the chromatin is limited and demonstrating that long-term repression primarily reflects mass-action chemical equilibria

    Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation

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    In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations

    Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment

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    This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100×\times increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.Comment: 6 pages, 5 figures, submitted to IEEE HPEC 2020 proceeding

    Quantitative imaging of collective cell migration during Drosophila gastrulation: multiphoton microscopy and computational analysis

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    This protocol describes imaging and computational tools to collect and analyze live imaging data of embryonic cell migration. Our five-step protocol requires a few weeks to move through embryo preparation and four-dimensional (4D) live imaging using multiphoton microscopy, to 3D cell tracking using image processing, registration of tracking data and their quantitative analysis using computational tools. It uses commercially available equipment and requires expertise in microscopy and programming that is appropriate for a biology laboratory. Custom-made scripts are provided, as well as sample datasets to permit readers without experimental data to carry out the analysis. The protocol has offered new insights into the genetic control of cell migration during Drosophila gastrulation. With simple modifications, this systematic analysis could be applied to any developing system to define cell positions in accordance with the body plan, to decompose complex 3D movements and to quantify the collective nature of cell migration

    Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis

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    An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. Crucially however, these frameworks require large human-annotated datasets for training and the resulting models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming based computational strategy that generates transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets, a feature which confers tremendous flexibility, speed, and functionality to this approach. We also deployed Kartezio to solve semantic and instance segmentation problems in four real-world Use Cases, and showcase its utility in imaging contexts ranging from high-resolution microscopy to clinical pathology. By successfully implementing Kartezio on a portfolio of images ranging from subcellular structures to tumoral tissue, we demonstrated the flexibility, robustness and practical utility of this fully explicable evolutionary designer for semantic and instance segmentation.Comment: 36 pages, 6 main Figures. The Extended Data Movie is available at the following link: https://www.youtube.com/watch?v=r74gdzb6hdA. The source code is available on Github: https://github.com/KevinCortacero/Kartezi
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