[{"scopus_import":"1","file_date_updated":"2023-10-04T11:40:51Z","ddc":["570"],"citation":{"ama":"Gupta D, Mlynarski WF, Sumser AL, Symonova O, Svaton J, Jösch MA. Panoramic visual statistics shape retina-wide organization of receptive fields. <i>Nature Neuroscience</i>. 2023;26:606-614. doi:<a href=\"https://doi.org/10.1038/s41593-023-01280-0\">10.1038/s41593-023-01280-0</a>","ieee":"D. Gupta, W. F. Mlynarski, A. L. Sumser, O. Symonova, J. Svaton, and M. A. Jösch, “Panoramic visual statistics shape retina-wide organization of receptive fields,” <i>Nature Neuroscience</i>, vol. 26. Springer Nature, pp. 606–614, 2023.","short":"D. Gupta, W.F. Mlynarski, A.L. Sumser, O. Symonova, J. Svaton, M.A. Jösch, Nature Neuroscience 26 (2023) 606–614.","mla":"Gupta, Divyansh, et al. “Panoramic Visual Statistics Shape Retina-Wide Organization of Receptive Fields.” <i>Nature Neuroscience</i>, vol. 26, Springer Nature, 2023, pp. 606–14, doi:<a href=\"https://doi.org/10.1038/s41593-023-01280-0\">10.1038/s41593-023-01280-0</a>.","ista":"Gupta D, Mlynarski WF, Sumser AL, Symonova O, Svaton J, Jösch MA. 2023. Panoramic visual statistics shape retina-wide organization of receptive fields. Nature Neuroscience. 26, 606–614.","apa":"Gupta, D., Mlynarski, W. F., Sumser, A. L., Symonova, O., Svaton, J., &#38; Jösch, M. A. (2023). Panoramic visual statistics shape retina-wide organization of receptive fields. <i>Nature Neuroscience</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41593-023-01280-0\">https://doi.org/10.1038/s41593-023-01280-0</a>","chicago":"Gupta, Divyansh, Wiktor F Mlynarski, Anton L Sumser, Olga Symonova, Jan Svaton, and Maximilian A Jösch. “Panoramic Visual Statistics Shape Retina-Wide Organization of Receptive Fields.” <i>Nature Neuroscience</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41593-023-01280-0\">https://doi.org/10.1038/s41593-023-01280-0</a>."},"intvolume":"        26","doi":"10.1038/s41593-023-01280-0","publication_status":"published","oa_version":"Published Version","date_published":"2023-04-01T00:00:00Z","status":"public","publication_identifier":{"issn":["1097-6256"],"eissn":["1546-1726"]},"oa":1,"month":"04","ec_funded":1,"abstract":[{"text":"Statistics of natural scenes are not uniform - their structure varies dramatically from ground to sky. It remains unknown whether these non-uniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. Here, by relying on the efficient coding hypothesis, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. We show experimentally that, in agreement with our predictions, receptive fields of retinal ganglion cells change their shape along the dorsoventral retinal axis, with a marked surround asymmetry at the visual horizon. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell-types.","lang":"eng"}],"date_updated":"2023-10-04T11:41:05Z","year":"2023","acknowledged_ssus":[{"_id":"ScienComp"},{"_id":"PreCl"},{"_id":"LifeSc"},{"_id":"Bio"}],"_id":"12349","article_type":"original","article_processing_charge":"Yes (in subscription journal)","quality_controlled":"1","volume":26,"external_id":{"isi":["000955258300002"],"pmid":["36959418"]},"related_material":{"record":[{"id":"12370","status":"public","relation":"research_data"}]},"has_accepted_license":"1","publication":"Nature Neuroscience","page":"606-614","date_created":"2023-01-23T14:14:19Z","type":"journal_article","department":[{"_id":"GradSch"},{"_id":"MaJö"}],"language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Springer Nature","title":"Panoramic visual statistics shape retina-wide organization of receptive fields","pmid":1,"acknowledgement":"We thank Hiroki Asari for sharing the dataset of naturalistic images, Anton Sumser for sharing visual stimulus code, Yoav Ben Simon for initial explorative work with the generation of AAVs, and Tomas Vega-Zuñiga for help with immunostainings. We also thank Gasper Tkacik and members of the Neuroethology group for their comments on the manuscript. This research was supported by the Scientific Service Units of IST Austria through resources provided by Scientific Computing, the Preclinical Facility, the Lab Support Facility, and the Imaging and Optics Facility. This work was supported by European Union Horizon 2020 Marie Skłodowska-Curie grant 665385 (DG), Austrian Science Fund (FWF) stand-alone grant P 34015 (WM), Human Frontiers Science Program LT000256/2018-L (AS), EMBO ALTF 1098-2017 (AS) and the European Research Council Starting Grant 756502 (MJ).","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"day":"01","isi":1,"author":[{"full_name":"Gupta, Divyansh","id":"2A485EBE-F248-11E8-B48F-1D18A9856A87","last_name":"Gupta","first_name":"Divyansh","orcid":"0000-0001-7400-6665"},{"full_name":"Mlynarski, Wiktor F","id":"358A453A-F248-11E8-B48F-1D18A9856A87","last_name":"Mlynarski","first_name":"Wiktor F"},{"full_name":"Sumser, Anton L","last_name":"Sumser","id":"3320A096-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4792-1881","first_name":"Anton L"},{"last_name":"Symonova","id":"3C0C7BC6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2012-9947","first_name":"Olga","full_name":"Symonova, Olga"},{"last_name":"Svaton","id":"f7f724c3-9d6f-11ed-9f44-e5c5f3a5bee2","orcid":"0000-0002-6198-2939","first_name":"Jan","full_name":"Svaton, Jan"},{"id":"2BD278E6-F248-11E8-B48F-1D18A9856A87","last_name":"Jösch","orcid":"0000-0002-3937-1330","first_name":"Maximilian A","full_name":"Jösch, Maximilian A"}],"project":[{"grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"International IST Doctoral Program"},{"grant_number":"P34015","_id":"626c45b5-2b32-11ec-9570-e509828c1ba6","name":"Efficient coding with biophysical realism"},{"_id":"2634E9D2-B435-11E9-9278-68D0E5697425","grant_number":"756502","name":"Circuits of Visual Attention","call_identifier":"H2020"},{"name":"Neuronal networks of salience and spatial detection in the murine superior colliculus","_id":"266D407A-B435-11E9-9278-68D0E5697425","grant_number":"LT000256"},{"name":"Connecting sensory with motor processing in the superior colliculus","grant_number":"ALTF 1098-2017","_id":"264FEA02-B435-11E9-9278-68D0E5697425"}],"file":[{"success":1,"file_size":6144866,"content_type":"application/pdf","relation":"main_file","date_created":"2023-10-04T11:40:51Z","file_name":"2023_NatureNeuroscience_Gupta.pdf","date_updated":"2023-10-04T11:40:51Z","access_level":"open_access","checksum":"a33d91e398e548f34003170e10988368","creator":"dernst","file_id":"14395"}]},{"title":"DynamicRoots: A software platform for the reconstruction and analysis of growing plant roots","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Public Library of Science","day":"01","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"file":[{"file_size":1850825,"content_type":"application/pdf","relation":"main_file","date_created":"2018-12-12T10:15:30Z","date_updated":"2020-07-14T12:45:16Z","file_name":"IST-2016-454-v1+1_journal.pone.0127657.pdf","access_level":"open_access","checksum":"d20f26461ca575276ad3ed9ce4bfc787","file_id":"5150","creator":"system"}],"author":[{"first_name":"Olga","id":"3C0C7BC6-F248-11E8-B48F-1D18A9856A87","last_name":"Symonova","full_name":"Symonova, Olga"},{"full_name":"Topp, Christopher","first_name":"Christopher","last_name":"Topp"},{"id":"3FB178DA-F248-11E8-B48F-1D18A9856A87","last_name":"Edelsbrunner","first_name":"Herbert","orcid":"0000-0002-9823-6833","full_name":"Edelsbrunner, Herbert"}],"pubrep_id":"454","publist_id":"5318","related_material":{"record":[{"id":"9737","relation":"research_data","status":"public"}]},"quality_controlled":"1","volume":10,"publication":"PLoS One","date_created":"2018-12-11T11:54:02Z","has_accepted_license":"1","department":[{"_id":"MaJö"},{"_id":"HeEd"}],"language":[{"iso":"eng"}],"type":"journal_article","oa":1,"status":"public","month":"06","year":"2015","abstract":[{"lang":"eng","text":"We present a software platform for reconstructing and analyzing the growth of a plant root system from a time-series of 3D voxelized shapes. It aligns the shapes with each other, constructs a geometric graph representation together with the function that records the time of growth, and organizes the branches into a hierarchy that reflects the order of creation. The software includes the automatic computation of structural and dynamic traits for each root in the system enabling the quantification of growth on fine-scale. These are important advances in plant phenotyping with applications to the study of genetic and environmental influences on growth."}],"issue":"6","date_updated":"2023-02-23T14:06:33Z","_id":"1793","article_number":"e0127657","ddc":["000"],"file_date_updated":"2020-07-14T12:45:16Z","scopus_import":1,"citation":{"ieee":"O. Symonova, C. Topp, and H. Edelsbrunner, “DynamicRoots: A software platform for the reconstruction and analysis of growing plant roots,” <i>PLoS One</i>, vol. 10, no. 6. Public Library of Science, 2015.","ama":"Symonova O, Topp C, Edelsbrunner H. DynamicRoots: A software platform for the reconstruction and analysis of growing plant roots. <i>PLoS One</i>. 2015;10(6). doi:<a href=\"https://doi.org/10.1371/journal.pone.0127657\">10.1371/journal.pone.0127657</a>","short":"O. Symonova, C. Topp, H. Edelsbrunner, PLoS One 10 (2015).","ista":"Symonova O, Topp C, Edelsbrunner H. 2015. DynamicRoots: A software platform for the reconstruction and analysis of growing plant roots. PLoS One. 10(6), e0127657.","apa":"Symonova, O., Topp, C., &#38; Edelsbrunner, H. (2015). DynamicRoots: A software platform for the reconstruction and analysis of growing plant roots. <i>PLoS One</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0127657\">https://doi.org/10.1371/journal.pone.0127657</a>","mla":"Symonova, Olga, et al. “DynamicRoots: A Software Platform for the Reconstruction and Analysis of Growing Plant Roots.” <i>PLoS One</i>, vol. 10, no. 6, e0127657, Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pone.0127657\">10.1371/journal.pone.0127657</a>.","chicago":"Symonova, Olga, Christopher Topp, and Herbert Edelsbrunner. “DynamicRoots: A Software Platform for the Reconstruction and Analysis of Growing Plant Roots.” <i>PLoS One</i>. Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pone.0127657\">https://doi.org/10.1371/journal.pone.0127657</a>."},"intvolume":"        10","doi":"10.1371/journal.pone.0127657","publication_status":"published","date_published":"2015-06-01T00:00:00Z","oa_version":"Published Version"},{"department":[{"_id":"MaJö"},{"_id":"HeEd"}],"date_published":"2015-06-01T00:00:00Z","oa_version":"Published Version","type":"research_data_reference","date_created":"2021-07-28T06:20:13Z","doi":"10.1371/journal.pone.0127657.s001","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"1793"}]},"citation":{"ista":"Symonova O, Topp C, Edelsbrunner H. 2015. Root traits computed by DynamicRoots for the maize root shown in fig 2, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pone.0127657.s001\">10.1371/journal.pone.0127657.s001</a>.","apa":"Symonova, O., Topp, C., &#38; Edelsbrunner, H. (2015). Root traits computed by DynamicRoots for the maize root shown in fig 2. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0127657.s001\">https://doi.org/10.1371/journal.pone.0127657.s001</a>","mla":"Symonova, Olga, et al. <i>Root Traits Computed by DynamicRoots for the Maize Root Shown in Fig 2</i>. Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pone.0127657.s001\">10.1371/journal.pone.0127657.s001</a>.","chicago":"Symonova, Olga, Christopher Topp, and Herbert Edelsbrunner. “Root Traits Computed by DynamicRoots for the Maize Root Shown in Fig 2.” Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pone.0127657.s001\">https://doi.org/10.1371/journal.pone.0127657.s001</a>.","ieee":"O. Symonova, C. Topp, and H. Edelsbrunner, “Root traits computed by DynamicRoots for the maize root shown in fig 2.” Public Library of Science, 2015.","ama":"Symonova O, Topp C, Edelsbrunner H. Root traits computed by DynamicRoots for the maize root shown in fig 2. 2015. doi:<a href=\"https://doi.org/10.1371/journal.pone.0127657.s001\">10.1371/journal.pone.0127657.s001</a>","short":"O. Symonova, C. Topp, H. Edelsbrunner, (2015)."},"article_processing_charge":"No","_id":"9737","author":[{"full_name":"Symonova, Olga","first_name":"Olga","last_name":"Symonova","id":"3C0C7BC6-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Topp, Christopher","first_name":"Christopher","last_name":"Topp"},{"full_name":"Edelsbrunner, Herbert","last_name":"Edelsbrunner","id":"3FB178DA-F248-11E8-B48F-1D18A9856A87","first_name":"Herbert","orcid":"0000-0002-9823-6833"}],"day":"01","year":"2015","date_updated":"2023-02-23T10:14:42Z","month":"06","title":"Root traits computed by DynamicRoots for the maize root shown in fig 2","status":"public","publisher":"Public Library of Science","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf"},{"date_updated":"2021-01-12T06:59:58Z","abstract":[{"lang":"eng","text":"Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala x Azucena. We phenotyped &gt;1,400 3D root models and &gt;57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r2 = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops."}],"issue":"18","year":"2013","_id":"2822","status":"public","oa":1,"month":"04","publication_status":"published","doi":"10.1073/pnas.1304354110","oa_version":"Submitted Version","date_published":"2013-04-30T00:00:00Z","scopus_import":1,"main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4378147/"}],"citation":{"chicago":"Topp, Christopher, Anjali Iyer Pascuzzi, Jill Anderson, Cheng Lee, Paul Zurek, Olga Symonova, Ying Zheng, et al. “3D Phenotyping and Quantitative Trait Locus Mapping Identify Core Regions of the Rice Genome Controlling Root Architecture.” <i>PNAS</i>. National Academy of Sciences, 2013. <a href=\"https://doi.org/10.1073/pnas.1304354110\">https://doi.org/10.1073/pnas.1304354110</a>.","mla":"Topp, Christopher, et al. “3D Phenotyping and Quantitative Trait Locus Mapping Identify Core Regions of the Rice Genome Controlling Root Architecture.” <i>PNAS</i>, vol. 110, no. 18, National Academy of Sciences, 2013, pp. E1695–704, doi:<a href=\"https://doi.org/10.1073/pnas.1304354110\">10.1073/pnas.1304354110</a>.","ista":"Topp C, Iyer Pascuzzi A, Anderson J, Lee C, Zurek P, Symonova O, Zheng Y, Bucksch A, Mileyko Y, Galkovskyi T, Moore B, Harer J, Edelsbrunner H, Mitchell Olds T, Weitz J, Benfey P. 2013. 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. PNAS. 110(18), E1695–E1704.","apa":"Topp, C., Iyer Pascuzzi, A., Anderson, J., Lee, C., Zurek, P., Symonova, O., … Benfey, P. (2013). 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1304354110\">https://doi.org/10.1073/pnas.1304354110</a>","ama":"Topp C, Iyer Pascuzzi A, Anderson J, et al. 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. <i>PNAS</i>. 2013;110(18):E1695-E1704. doi:<a href=\"https://doi.org/10.1073/pnas.1304354110\">10.1073/pnas.1304354110</a>","ieee":"C. Topp <i>et al.</i>, “3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture,” <i>PNAS</i>, vol. 110, no. 18. National Academy of Sciences, pp. E1695–E1704, 2013.","short":"C. Topp, A. Iyer Pascuzzi, J. Anderson, C. Lee, P. Zurek, O. Symonova, Y. Zheng, A. Bucksch, Y. Mileyko, T. Galkovskyi, B. Moore, J. Harer, H. Edelsbrunner, T. Mitchell Olds, J. Weitz, P. Benfey, PNAS 110 (2013) E1695–E1704."},"intvolume":"       110","pmid":1,"day":"30","author":[{"last_name":"Topp","first_name":"Christopher","full_name":"Topp, Christopher"},{"last_name":"Iyer Pascuzzi","first_name":"Anjali","full_name":"Iyer Pascuzzi, Anjali"},{"full_name":"Anderson, Jill","last_name":"Anderson","first_name":"Jill"},{"first_name":"Cheng","last_name":"Lee","full_name":"Lee, Cheng"},{"full_name":"Zurek, Paul","last_name":"Zurek","first_name":"Paul"},{"first_name":"Olga","last_name":"Symonova","id":"3C0C7BC6-F248-11E8-B48F-1D18A9856A87","full_name":"Symonova, Olga"},{"full_name":"Zheng, Ying","last_name":"Zheng","first_name":"Ying"},{"last_name":"Bucksch","first_name":"Alexander","full_name":"Bucksch, Alexander"},{"last_name":"Mileyko","first_name":"Yuriy","full_name":"Mileyko, Yuriy"},{"first_name":"Taras","last_name":"Galkovskyi","full_name":"Galkovskyi, Taras"},{"full_name":"Moore, Brad","first_name":"Brad","last_name":"Moore"},{"first_name":"John","last_name":"Harer","full_name":"Harer, John"},{"orcid":"0000-0002-9823-6833","first_name":"Herbert","id":"3FB178DA-F248-11E8-B48F-1D18A9856A87","last_name":"Edelsbrunner","full_name":"Edelsbrunner, Herbert"},{"first_name":"Thomas","last_name":"Mitchell Olds","full_name":"Mitchell Olds, Thomas"},{"full_name":"Weitz, Joshua","last_name":"Weitz","first_name":"Joshua"},{"full_name":"Benfey, Philip","first_name":"Philip","last_name":"Benfey"}],"publisher":"National Academy of Sciences","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture","date_created":"2018-12-11T11:59:47Z","page":"E1695 - E1704","publication":"PNAS","type":"journal_article","language":[{"iso":"eng"}],"department":[{"_id":"MaJö"},{"_id":"HeEd"}],"publist_id":"3979","external_id":{"pmid":["25673779"]},"volume":110,"quality_controlled":"1"},{"publisher":"IEEE","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","title":"The adaptive topology of a digital image","author":[{"full_name":"Edelsbrunner, Herbert","orcid":"0000-0002-9823-6833","first_name":"Herbert","last_name":"Edelsbrunner","id":"3FB178DA-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Symonova, Olga","last_name":"Symonova","id":"3C0C7BC6-F248-11E8-B48F-1D18A9856A87","first_name":"Olga"}],"file":[{"date_created":"2018-12-12T10:09:41Z","date_updated":"2020-07-14T12:45:52Z","file_name":"IST-2016-545-v1+1_2012-P-10-AdaptiveTopology.pdf","access_level":"open_access","creator":"system","file_id":"4765","checksum":"444869a4e8abf07834f88b6e5cb5e9c3","file_size":760548,"content_type":"application/pdf","relation":"main_file"}],"day":"06","quality_controlled":"1","publist_id":"3844","pubrep_id":"545","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"HeEd"},{"_id":"MaJö"}],"has_accepted_license":"1","page":"41 - 48","date_created":"2018-12-11T12:00:15Z","month":"08","status":"public","oa":1,"_id":"2903","date_updated":"2021-01-12T07:00:35Z","abstract":[{"text":"In order to enjoy a digital version of the Jordan Curve Theorem, it is common to use the closed topology for the foreground and the open topology for the background of a 2-dimensional binary image. In this paper, we introduce a single topology that enjoys this theorem for all thresholds decomposing a real-valued image into foreground and background. This topology is easy to construct and it generalizes to n-dimensional images.","lang":"eng"}],"year":"2012","citation":{"ama":"Edelsbrunner H, Symonova O. The adaptive topology of a digital image. In: IEEE; 2012:41-48. doi:<a href=\"https://doi.org/10.1109/ISVD.2012.11\">10.1109/ISVD.2012.11</a>","ieee":"H. Edelsbrunner and O. Symonova, “The adaptive topology of a digital image,” presented at the ISVD: International Symposium on Voronoi Diagrams in Science and Engineering, New Brunswick, NJ, USA , 2012, pp. 41–48.","short":"H. Edelsbrunner, O. Symonova, in:, IEEE, 2012, pp. 41–48.","chicago":"Edelsbrunner, Herbert, and Olga Symonova. “The Adaptive Topology of a Digital Image,” 41–48. IEEE, 2012. <a href=\"https://doi.org/10.1109/ISVD.2012.11\">https://doi.org/10.1109/ISVD.2012.11</a>.","apa":"Edelsbrunner, H., &#38; Symonova, O. (2012). The adaptive topology of a digital image (pp. 41–48). Presented at the ISVD: International Symposium on Voronoi Diagrams in Science and Engineering, New Brunswick, NJ, USA : IEEE. <a href=\"https://doi.org/10.1109/ISVD.2012.11\">https://doi.org/10.1109/ISVD.2012.11</a>","mla":"Edelsbrunner, Herbert, and Olga Symonova. <i>The Adaptive Topology of a Digital Image</i>. IEEE, 2012, pp. 41–48, doi:<a href=\"https://doi.org/10.1109/ISVD.2012.11\">10.1109/ISVD.2012.11</a>.","ista":"Edelsbrunner H, Symonova O. 2012. The adaptive topology of a digital image. ISVD: International Symposium on Voronoi Diagrams in Science and Engineering, 41–48."},"scopus_import":1,"file_date_updated":"2020-07-14T12:45:52Z","ddc":["000"],"oa_version":"Submitted Version","date_published":"2012-08-06T00:00:00Z","conference":{"name":"ISVD: International Symposium on Voronoi Diagrams in Science and Engineering","start_date":"2012-06-27","location":"New Brunswick, NJ, USA ","end_date":"2012-06-29"},"publication_status":"published","doi":"10.1109/ISVD.2012.11"},{"publisher":"BioMed Central","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","title":"GiA Roots: Software for the high throughput analysis of plant root system architecture","day":"26","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"extern":"1","author":[{"first_name":"Taras","last_name":"Galkovskyi","full_name":"Galkovskyi, Taras"},{"last_name":"Mileyko","first_name":"Yuriy","full_name":"Mileyko, Yuriy"},{"full_name":"Bucksch, Alexander","first_name":"Alexander","last_name":"Bucksch"},{"last_name":"Moore","first_name":"Brad","full_name":"Moore, Brad"},{"full_name":"Symonova, Olga","first_name":"Olga","id":"3C0C7BC6-F248-11E8-B48F-1D18A9856A87","last_name":"Symonova"},{"first_name":"Charles","last_name":"Price","full_name":"Price, Charles"},{"full_name":"Topp, Chrostopher","first_name":"Chrostopher","last_name":"Topp"},{"last_name":"Iyer Pascuzzi","first_name":"Anjali","full_name":"Iyer Pascuzzi, Anjali"},{"first_name":"Paul","last_name":"Zurek","full_name":"Zurek, Paul"},{"full_name":"Fang, Suqin","last_name":"Fang","first_name":"Suqin"},{"full_name":"Harer, John","first_name":"John","last_name":"Harer"},{"full_name":"Benfey, Philip","last_name":"Benfey","first_name":"Philip"},{"last_name":"Weitz","first_name":"Joshua","full_name":"Weitz, Joshua"}],"file":[{"access_level":"open_access","creator":"system","file_id":"4953","checksum":"0c629e36acd5f2878ff7dd088d67d494","date_created":"2018-12-12T10:12:35Z","file_name":"IST-2018-946-v1+1_2012_Symonova_GiA_Roots.pdf","date_updated":"2020-07-14T12:46:35Z","file_size":1691436,"content_type":"application/pdf","relation":"main_file"}],"publist_id":"7328","pubrep_id":"946","article_processing_charge":"No","volume":12,"quality_controlled":"1","has_accepted_license":"1","date_created":"2018-12-11T11:46:46Z","publication":"BMC Plant Biology","type":"journal_article","language":[{"iso":"eng"}],"status":"public","oa":1,"month":"07","date_updated":"2022-08-25T14:59:17Z","abstract":[{"text":"Background: Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.Results: We have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.Conclusions: We demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.","lang":"eng"}],"year":"2012","article_number":"116","_id":"492","file_date_updated":"2020-07-14T12:46:35Z","scopus_import":"1","ddc":["005","514","516"],"intvolume":"        12","citation":{"ama":"Galkovskyi T, Mileyko Y, Bucksch A, et al. GiA Roots: Software for the high throughput analysis of plant root system architecture. <i>BMC Plant Biology</i>. 2012;12. doi:<a href=\"https://doi.org/10.1186/1471-2229-12-116\">10.1186/1471-2229-12-116</a>","ieee":"T. Galkovskyi <i>et al.</i>, “GiA Roots: Software for the high throughput analysis of plant root system architecture,” <i>BMC Plant Biology</i>, vol. 12. BioMed Central, 2012.","short":"T. Galkovskyi, Y. Mileyko, A. Bucksch, B. Moore, O. Symonova, C. Price, C. Topp, A. Iyer Pascuzzi, P. Zurek, S. Fang, J. Harer, P. Benfey, J. Weitz, BMC Plant Biology 12 (2012).","chicago":"Galkovskyi, Taras, Yuriy Mileyko, Alexander Bucksch, Brad Moore, Olga Symonova, Charles Price, Chrostopher Topp, et al. “GiA Roots: Software for the High Throughput Analysis of Plant Root System Architecture.” <i>BMC Plant Biology</i>. BioMed Central, 2012. <a href=\"https://doi.org/10.1186/1471-2229-12-116\">https://doi.org/10.1186/1471-2229-12-116</a>.","mla":"Galkovskyi, Taras, et al. “GiA Roots: Software for the High Throughput Analysis of Plant Root System Architecture.” <i>BMC Plant Biology</i>, vol. 12, 116, BioMed Central, 2012, doi:<a href=\"https://doi.org/10.1186/1471-2229-12-116\">10.1186/1471-2229-12-116</a>.","apa":"Galkovskyi, T., Mileyko, Y., Bucksch, A., Moore, B., Symonova, O., Price, C., … Weitz, J. (2012). GiA Roots: Software for the high throughput analysis of plant root system architecture. <i>BMC Plant Biology</i>. BioMed Central. <a href=\"https://doi.org/10.1186/1471-2229-12-116\">https://doi.org/10.1186/1471-2229-12-116</a>","ista":"Galkovskyi T, Mileyko Y, Bucksch A, Moore B, Symonova O, Price C, Topp C, Iyer Pascuzzi A, Zurek P, Fang S, Harer J, Benfey P, Weitz J. 2012. GiA Roots: Software for the high throughput analysis of plant root system architecture. BMC Plant Biology. 12, 116."},"publication_status":"published","doi":"10.1186/1471-2229-12-116","oa_version":"Published Version","date_published":"2012-07-26T00:00:00Z"}]