[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122831-en":3,"doc-seo-122831-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122831,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning topological defects in confluent tissues - Abstract","Active nematics provides a framework to characterise biological systems, and its topological defects are linked to many homeostatic and morphogenetic processes. Reliable detection and classification are therefore crucial for interpreting experimental observations. Existing methods work well for elongated, rod-shaped constituents but fail for epithelial cell layers where cell orientation is poorly defined. A convolutional neural network is introduced to detect and classify nematic defects in confluent cell layers, including non-rod-shaped cells.","arXiv :2303 .08166v2 [ cond-mat .soft] 20 Mar 2023  \nMachine learning topological defects in confluent tissues Andrew Killeen 1* , Thibault Bertrand2†, Chiu Fan Lee 1‡,  \n1 Department of Bioengineering, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.  \n2 Department of Mathematics, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.  \n* [a.killeen18@imperial.ac.uk](a.killeen18@imperial.ac.uk)[ ](a.killeen18@imperial.ac.uk)† [t.bertrand@imperial.ac.uk](t.bertrand@imperial.ac.uk)[ ](t.bertrand@imperial.ac.uk)‡ [c.lee@imperial.ac.uk](c.lee@imperial.ac.uk)  \nAbstract  \nActive nematics is an emerging paradigm for characterising biological systems. One aspect of particularly intense focus is the role active nematic defects play in these systems, as they have been found to mediate a growing number of biological processes. Accurately detecting and classifying these defects in biological systems is, therefore, of vital importance to improving our understanding of such processes. While robust methods for defect detection exist for systems of elongated constituents, other systems, such as epithelial layers, are not well suited to such methods. Here, we address this problem by developing a convolutional neural network to detect and classify nematic defects in confluent cell layers. Crucially, our method is readily implementable on experimental images of cell layers and is specifically designed to be suitable for cells that are not rod-shaped. We demonstrate that our machine learning model outperforms current defect detection techniques and that this manifests itself in our method requiring less data to accurately capture defect properties. This could drastically improve the accuracy of experimental data interpretation whilst also reducing costs, advancing the study of nematic defects in biological systems.  \nAuthor summary  \nDefects in the local alignment of cells have been found to play a functional role in homeostatic and morphogenetic processes in many different biological systems. Detecting these defects is, therefore, very important for improving our understanding of these processes. However, current defect detection techniques are not well suited to cell layers in which cells are not elongated in shape, and so the direction of cell orientation can be poorly defined, even though defects mediate important homeostatic processes in these layers. Here, we address this problem and develop a machine learning method to detect and classify defects which is specifically designed for systems for which existing methods are not appropriate. We show that our method outperforms current techniques, detecting defects in confluent cell layers more accurately. We then demonstrate that this improved performance means that properties of these defects, often the target of experimental studies, can be characterised more accurately with less data. We anticipate this could drastically improve experiments investigating defects in these systems, improving our knowledge of important biological processes.  \nMarch 21, 2023 1/15  \nIntroduction  \nTissue dynamics underpins a wide variety of biological processes such as wound healing [1], cancer metastasis [2] and morphogenesis [3] . Many of these processes concern confluent tissues, such as epithelial and endothelial cell layers, making suitable descriptions of the dynamics of these systems a prerequisite for our understanding of these processes. Unlike constituents in a passive material, cells within a confluent tissue can generate forces and exert stresses on their neighbours and underlying substrate. As such, active matter physics provides a natural framework for describing confluent tissues and has provided numerous insights into these systems [4] . Active matter is an emergent field of physics concerned with describing many-body systems far from equilibrium, where the system is driven from equilibrium by energy expended by individual constituents [5] .  \nA fruitful conn","cbCaidm5qv0MXlGM","https://ap.wps.com/l/cbCaidm5qv0MXlGM","pdf",1188027,1,17,"English","en",105,"# Abstract\n# Author summary\n# Introduction\n## Active matter and confluent tissues\n## Active nematic theory\n## Topological defects in nematic fields","[{\"question\":\"Why are detecting and classifying topological defects in active nematics important for biological studies?\",\"answer\":\"Topological defects mediate multiple homeostatic and morphogenetic processes, so accurate detection improves understanding and interpretation of biological mechanisms.\"},{\"question\":\"What limitation affects current defect detection methods for confluent tissue layers?\",\"answer\":\"Most robust methods assume elongated, rod-shaped constituents, making them poorly suited to epithelial layers where cell orientation direction is difficult to define.\"},{\"question\":\"How does the proposed method address non-rod-shaped cell layers?\",\"answer\":\"It uses a convolutional neural network designed to detect and classify nematic defects in confluent cell layers, specifically targeting systems where cells are not rod-shaped.\"}]","Machine learning topological defects in confluent tissues - 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