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We gratefully acknowledge Eder Miguel for setting up webKnossos and Marek Šuplata for computational support and hardware control. We are grateful to Ryuichi Shigemoto and Bernd Bickel for generous support, and Michael Sixt and Scott Boyd (Stanford University) for discussions and critical reading of the manuscript. PSD95-HaloTag mice were kindly provided by Seth Grant (University of Edinburgh). We acknowledge expert support by IST Austria’s scientific computing, imaging and optics, preclinical, and lab support facilities, and by the Library and Miba machine shop.\r\nWe gratefully acknowledge funding by the following sources: \r\nAustrian Science Fund (FWF) grant I3600-B27 (JGD)\r\nAustrian Science Fund (FWF) grant DK W1232 (JGD, JMM)\r\nAustrian Science Fund (FWF) grant Z 312-B27, Wittgenstein award (PJ)\r\nAustrian Science Funds (FWF) projects I4685-B, I6565-B (SYNABS) and DOC 33-B27 (RH)\r\nGesellschaft für Forschungsförderung NÖ (NFB) grant LSC18-022 (JGD)\r\nEuropean Union’s Horizon 2020 research and innovation programme, European Research Council (ERC) grant 715508 – REVERSEAUTISM (GN)\r\nEuropean Union’s Horizon 2020 research and innovation programme, European Research Council (ERC) grant 692692 – GIANTSYN (PJ)\r\nMarie Skłodowska-Curie Actions Fellowship GA no. 665385 under the EU Horizon 2020 program (JMM, JL)\r\nMarie Skłodowska-Curie Actions Individual Fellowship 101026635 under the EU Horizon 2020 program (JFW)","ddc":["610"],"citation":{"ista":"Danzl JG. 2023. Research data for the publication ‘Imaging brain tissue architecture across millimeter to nanometer scales’, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:13126\">10.15479/AT:ISTA:13126</a>.","short":"J.G. Danzl, (2023).","mla":"Danzl, Johann G. <i>Research Data for the Publication “Imaging Brain Tissue Architecture across Millimeter to Nanometer Scales.”</i> Institute of Science and Technology Austria, 2023, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:13126\">10.15479/AT:ISTA:13126</a>.","ieee":"J. G. Danzl, “Research data for the publication ‘Imaging brain tissue architecture across millimeter to nanometer scales.’” Institute of Science and Technology Austria, 2023.","chicago":"Danzl, Johann G. “Research Data for the Publication ‘Imaging Brain Tissue Architecture across Millimeter to Nanometer Scales.’” Institute of Science and Technology Austria, 2023. <a href=\"https://doi.org/10.15479/AT:ISTA:13126\">https://doi.org/10.15479/AT:ISTA:13126</a>.","apa":"Danzl, J. G. (2023). Research data for the publication “Imaging brain tissue architecture across millimeter to nanometer scales.” Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:13126\">https://doi.org/10.15479/AT:ISTA:13126</a>","ama":"Danzl JG. Research data for the publication “Imaging brain tissue architecture across millimeter to nanometer scales.” 2023. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:13126\">10.15479/AT:ISTA:13126</a>"},"year":"2023","date_updated":"2024-02-21T12:18:19Z","day":"04","doi":"10.15479/AT:ISTA:13126","abstract":[{"lang":"eng","text":"Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here, we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease."}]},{"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1038/s41592-023-01936-6"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"link":[{"relation":"software","url":"https://github.com/danzllab/LIONESS"}],"record":[{"relation":"research_data","id":"12817","status":"public"},{"relation":"shorter_version","id":"14770","status":"public"}]},"status":"public","publication_identifier":{"issn":["1548-7091"],"eissn":["1548-7105"]},"oa":1,"type":"journal_article","date_published":"2023-08-01T00:00:00Z","language":[{"iso":"eng"}],"project":[{"grant_number":"I03600","name":"Optical control of synaptic function via adhesion molecules","call_identifier":"FWF","_id":"265CB4D0-B435-11E9-9278-68D0E5697425"},{"_id":"2548AE96-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"W1232-B24","name":"Molecular Drug Targets"},{"name":"The Wittgenstein Prize","grant_number":"Z00312","_id":"25C5A090-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"name":"High content imaging to decode human immune cell interactions in health and allergic disease","_id":"23889792-32DE-11EA-91FC-C7463DDC885E"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"665385","name":"International IST Doctoral Program"},{"grant_number":"715767","name":"MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and Modeling","_id":"24F9549A-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"call_identifier":"H2020","_id":"25444568-B435-11E9-9278-68D0E5697425","name":"Probing the Reversibility of Autism Spectrum Disorders by Employing in vivo and in vitro Models","grant_number":"715508"},{"name":"Biophysics and circuit function of a giant cortical glumatergic synapse","grant_number":"692692","_id":"25B7EB9E-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"_id":"fc2be41b-9c52-11eb-aca3-faa90aa144e9","call_identifier":"H2020","grant_number":"101026635","name":"Synaptic computations of the hippocampal CA3 circuitry"},{"_id":"2668BFA0-B435-11E9-9278-68D0E5697425","grant_number":"LT00057","name":"High-speed 3D-nanoscopy to study the role of adhesion during 3D cell migration"}],"oa_version":"Published Version","acknowledged_ssus":[{"_id":"ScienComp"},{"_id":"Bio"},{"_id":"PreCl"},{"_id":"E-Lib"},{"_id":"LifeSc"},{"_id":"M-Shop"}],"month":"08","publication":"Nature Methods","acknowledgement":"We thank J. Vorlaufer, N. Agudelo and A. Wartak for microscope maintenance and troubleshooting, C. Kreuzinger and A. Freeman for technical assistance, M. Šuplata for hardware control support and M. Cunha dos Santos for initial exploration of software. We\r\nthank P. Henderson for advice on deep-learning training and M. Sixt, S. Boyd and T. Weiss for discussions and critical reading of the manuscript. L. Lavis (Janelia Research Campus) generously provided the JF585-HaloTag ligand. We acknowledge expert support by IST\r\nAustria’s scientific computing, imaging and optics, preclinical, library and laboratory support facilities and by the Miba machine shop. We gratefully acknowledge funding by the following sources: Austrian Science Fund (F.W.F.) grant no. I3600-B27 (J.G.D.), grant no. DK W1232\r\n(J.G.D. and J.M.M.) and grant no. Z 312-B27, Wittgenstein award (P.J.); the Gesellschaft für Forschungsförderung NÖ grant no. LSC18-022 (J.G.D.); an ISTA Interdisciplinary project grant (J.G.D. and B.B.); the European Union’s Horizon 2020 research and innovation programme,\r\nMarie-Skłodowska Curie grant 665385 (J.M.M. and J.L.); the European Union’s Horizon 2020 research and innovation programme, European Research Council grant no. 715767, MATERIALIZABLE (B.B.); grant no. 715508, REVERSEAUTISM (G.N.); grant no. 695568, SYNNOVATE (S.G.N.G.); and grant no. 692692, GIANTSYN (P.J.); the Simons\r\nFoundation Autism Research Initiative grant no. 529085 (S.G.N.G.); the Wellcome Trust Technology Development grant no. 202932 (S.G.N.G.); the Marie Skłodowska-Curie Actions Individual Fellowship no. 101026635 under the EU Horizon 2020 program (J.F.W.);\r\nthe Human Frontier Science Program postdoctoral fellowship LT000557/2018 (W.J.); and the National Science Foundation grant no. IIS-1835231 (H.P.) and NCS-FO-2124179 (H.P.).","volume":20,"day":"01","doi":"10.1038/s41592-023-01936-6","abstract":[{"text":"Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure–function relationships of the brain’s complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.","lang":"eng"}],"citation":{"ista":"Velicky P, Miguel Villalba E, Michalska JM, Lyudchik J, Wei D, Lin Z, Watson J, Troidl J, Beyer J, Ben Simon Y, Sommer CM, Jahr W, Cenameri A, Broichhagen J, Grant SGN, Jonas PM, Novarino G, Pfister H, Bickel B, Danzl JG. 2023. Dense 4D nanoscale reconstruction of living brain tissue. Nature Methods. 20, 1256–1265.","short":"P. Velicky, E. Miguel Villalba, J.M. Michalska, J. Lyudchik, D. Wei, Z. Lin, J. Watson, J. Troidl, J. Beyer, Y. Ben Simon, C.M. Sommer, W. Jahr, A. Cenameri, J. Broichhagen, S.G.N. Grant, P.M. Jonas, G. Novarino, H. Pfister, B. Bickel, J.G. Danzl, Nature Methods 20 (2023) 1256–1265.","mla":"Velicky, Philipp, et al. “Dense 4D Nanoscale Reconstruction of Living Brain Tissue.” <i>Nature Methods</i>, vol. 20, Springer Nature, 2023, pp. 1256–65, doi:<a href=\"https://doi.org/10.1038/s41592-023-01936-6\">10.1038/s41592-023-01936-6</a>.","ieee":"P. Velicky <i>et al.</i>, “Dense 4D nanoscale reconstruction of living brain tissue,” <i>Nature Methods</i>, vol. 20. Springer Nature, pp. 1256–1265, 2023.","chicago":"Velicky, Philipp, Eder Miguel Villalba, Julia M Michalska, Julia Lyudchik, Donglai Wei, Zudi Lin, Jake Watson, et al. “Dense 4D Nanoscale Reconstruction of Living Brain Tissue.” <i>Nature Methods</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41592-023-01936-6\">https://doi.org/10.1038/s41592-023-01936-6</a>.","apa":"Velicky, P., Miguel Villalba, E., Michalska, J. M., Lyudchik, J., Wei, D., Lin, Z., … Danzl, J. G. (2023). Dense 4D nanoscale reconstruction of living brain tissue. <i>Nature Methods</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41592-023-01936-6\">https://doi.org/10.1038/s41592-023-01936-6</a>","ama":"Velicky P, Miguel Villalba E, Michalska JM, et al. Dense 4D nanoscale reconstruction of living brain tissue. <i>Nature Methods</i>. 2023;20:1256-1265. doi:<a href=\"https://doi.org/10.1038/s41592-023-01936-6\">10.1038/s41592-023-01936-6</a>"},"year":"2023","date_updated":"2024-01-10T08:37:48Z","external_id":{"pmid":["37429995"],"isi":["001025621500001"]},"isi":1,"publisher":"Springer Nature","article_type":"original","quality_controlled":"1","ec_funded":1,"page":"1256-1265","date_created":"2023-07-23T22:01:13Z","department":[{"_id":"PeJo"},{"_id":"GaNo"},{"_id":"BeBi"},{"_id":"JoDa"},{"_id":"Bio"}],"article_processing_charge":"Yes","publication_status":"published","intvolume":"        20","title":"Dense 4D nanoscale reconstruction of living brain tissue","scopus_import":"1","pmid":1,"_id":"13267","author":[{"first_name":"Philipp","last_name":"Velicky","orcid":"0000-0002-2340-7431","full_name":"Velicky, Philipp","id":"39BDC62C-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Eder","last_name":"Miguel Villalba","orcid":"0000-0001-5665-0430","full_name":"Miguel Villalba, Eder","id":"3FB91342-F248-11E8-B48F-1D18A9856A87"},{"id":"443DB6DE-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3862-1235","full_name":"Michalska, Julia M","first_name":"Julia M","last_name":"Michalska"},{"full_name":"Lyudchik, Julia","first_name":"Julia","last_name":"Lyudchik","id":"46E28B80-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Wei","first_name":"Donglai","full_name":"Wei, Donglai"},{"first_name":"Zudi","last_name":"Lin","full_name":"Lin, Zudi"},{"full_name":"Watson, Jake","orcid":"0000-0002-8698-3823","last_name":"Watson","first_name":"Jake","id":"63836096-4690-11EA-BD4E-32803DDC885E"},{"first_name":"Jakob","last_name":"Troidl","full_name":"Troidl, Jakob"},{"full_name":"Beyer, Johanna","last_name":"Beyer","first_name":"Johanna"},{"id":"43DF3136-F248-11E8-B48F-1D18A9856A87","last_name":"Ben Simon","first_name":"Yoav","full_name":"Ben Simon, Yoav"},{"orcid":"0000-0003-1216-9105","full_name":"Sommer, Christoph M","first_name":"Christoph M","last_name":"Sommer","id":"4DF26D8C-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Wiebke","last_name":"Jahr","full_name":"Jahr, Wiebke","id":"425C1CE8-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Alban","last_name":"Cenameri","full_name":"Cenameri, Alban","id":"9ac8f577-2357-11eb-997a-e566c5550886"},{"last_name":"Broichhagen","first_name":"Johannes","full_name":"Broichhagen, Johannes"},{"first_name":"Seth G.N.","last_name":"Grant","full_name":"Grant, Seth G.N."},{"orcid":"0000-0001-5001-4804","full_name":"Jonas, Peter M","first_name":"Peter M","last_name":"Jonas","id":"353C1B58-F248-11E8-B48F-1D18A9856A87"},{"id":"3E57A680-F248-11E8-B48F-1D18A9856A87","first_name":"Gaia","last_name":"Novarino","orcid":"0000-0002-7673-7178","full_name":"Novarino, Gaia"},{"full_name":"Pfister, Hanspeter","first_name":"Hanspeter","last_name":"Pfister"},{"last_name":"Bickel","first_name":"Bernd","full_name":"Bickel, Bernd","orcid":"0000-0001-6511-9385","id":"49876194-F248-11E8-B48F-1D18A9856A87"},{"id":"42EFD3B6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8559-3973","full_name":"Danzl, Johann G","first_name":"Johann G","last_name":"Danzl"}]},{"ec_funded":1,"quality_controlled":"1","article_type":"original","publisher":"Springer Nature","author":[{"orcid":"0000-0003-3862-1235","full_name":"Michalska, Julia M","first_name":"Julia M","last_name":"Michalska","id":"443DB6DE-F248-11E8-B48F-1D18A9856A87"},{"id":"46E28B80-F248-11E8-B48F-1D18A9856A87","full_name":"Lyudchik, Julia","first_name":"Julia","last_name":"Lyudchik"},{"id":"39BDC62C-F248-11E8-B48F-1D18A9856A87","last_name":"Velicky","first_name":"Philipp","full_name":"Velicky, Philipp","orcid":"0000-0002-2340-7431"},{"last_name":"Korinkova","first_name":"Hana","full_name":"Korinkova, Hana","id":"ee3cb6ca-ec98-11ea-ae11-ff703e2254ed"},{"id":"63836096-4690-11EA-BD4E-32803DDC885E","first_name":"Jake","last_name":"Watson","orcid":"0000-0002-8698-3823","full_name":"Watson, Jake"},{"id":"9ac8f577-2357-11eb-997a-e566c5550886","last_name":"Cenameri","first_name":"Alban","full_name":"Cenameri, Alban"},{"last_name":"Sommer","first_name":"Christoph M","full_name":"Sommer, Christoph M","orcid":"0000-0003-1216-9105","id":"4DF26D8C-F248-11E8-B48F-1D18A9856A87"},{"id":"4CD6AAC6-F248-11E8-B48F-1D18A9856A87","last_name":"Amberg","first_name":"Nicole","full_name":"Amberg, Nicole","orcid":"0000-0002-3183-8207"},{"orcid":"0000-0003-2356-9403","full_name":"Venturino, Alessandro","first_name":"Alessandro","last_name":"Venturino","id":"41CB84B2-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Roessler","first_name":"Karl","full_name":"Roessler, Karl"},{"last_name":"Czech","first_name":"Thomas","full_name":"Czech, Thomas"},{"first_name":"Romana","last_name":"Höftberger","full_name":"Höftberger, Romana"},{"id":"36ACD32E-F248-11E8-B48F-1D18A9856A87","last_name":"Siegert","first_name":"Sandra","full_name":"Siegert, Sandra","orcid":"0000-0001-8635-0877"},{"id":"3E57A680-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-7673-7178","full_name":"Novarino, Gaia","first_name":"Gaia","last_name":"Novarino"},{"id":"353C1B58-F248-11E8-B48F-1D18A9856A87","full_name":"Jonas, Peter M","orcid":"0000-0001-5001-4804","last_name":"Jonas","first_name":"Peter M"},{"last_name":"Danzl","first_name":"Johann G","full_name":"Danzl, Johann G","orcid":"0000-0001-8559-3973","id":"42EFD3B6-F248-11E8-B48F-1D18A9856A87"}],"scopus_import":"1","_id":"14257","title":"Imaging brain tissue architecture across millimeter to nanometer scales","department":[{"_id":"SaSi"},{"_id":"GaNo"},{"_id":"PeJo"},{"_id":"JoDa"},{"_id":"Bio"},{"_id":"RySh"}],"article_processing_charge":"Yes (in subscription journal)","date_created":"2023-09-03T22:01:15Z","publication_status":"epub_ahead","acknowledgement":"We thank J. Vorlaufer, N. Agudelo-Dueñas, W. Jahr and A. Wartak for microscope maintenance and troubleshooting; C. Kreuzinger, A. Freeman and I. Erber for technical assistance; and M. Tomschik for support with obtaining human samples. We gratefully acknowledge E. Miguel for setting up webKnossos and M. Šuplata for computational support and hardware control. We are grateful to R. Shigemoto and B. Bickel for generous support and M. Sixt and S. Boyd (Stanford University) for discussions and critical reading of the paper. PSD95-HaloTag mice were kindly provided by S. Grant (University of Edinburgh). We acknowledge expert support by Institute of Science and Technology Austria’s scientific computing, imaging and optics, preclinical and lab support facilities and by the Miba machine shop and library. We gratefully acknowledge funding by the following sources: Austrian Science Fund (FWF) grant I3600-B27 (J.G.D.); Austrian Science Fund (FWF) grant DK W1232 (J.G.D. and J.M.M.); Austrian Science Fund (FWF) grant Z 312-B27, Wittgenstein award (P.J.); Austrian Science Fund (FWF) projects I4685-B, I6565-B (SYNABS) and DOC 33-B27 (R.H.); Gesellschaft für Forschungsförderung NÖ (NFB) grant LSC18-022 (J.G.D.); European Union’s Horizon 2020 research and innovation programme, European Research Council (ERC) grant 715508 – REVERSEAUTISM (G.N.); European Union’s Horizon 2020 research and innovation programme, European Research Council (ERC) grant 692692 – GIANTSYN (P.J.); Marie Skłodowska-Curie Actions Fellowship GA no. 665385 under the EU Horizon 2020 program (J.M.M. and J.L.); and Marie Skłodowska-Curie Actions Individual Fellowship no. 101026635 under the EU Horizon 2020 program (J.F.W.).","external_id":{"isi":["001065254200001"]},"isi":1,"year":"2023","citation":{"ama":"Michalska JM, Lyudchik J, Velicky P, et al. Imaging brain tissue architecture across millimeter to nanometer scales. <i>Nature Biotechnology</i>. 2023. doi:<a href=\"https://doi.org/10.1038/s41587-023-01911-8\">10.1038/s41587-023-01911-8</a>","apa":"Michalska, J. M., Lyudchik, J., Velicky, P., Korinkova, H., Watson, J., Cenameri, A., … Danzl, J. G. (2023). Imaging brain tissue architecture across millimeter to nanometer scales. <i>Nature Biotechnology</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41587-023-01911-8\">https://doi.org/10.1038/s41587-023-01911-8</a>","ieee":"J. M. Michalska <i>et al.</i>, “Imaging brain tissue architecture across millimeter to nanometer scales,” <i>Nature Biotechnology</i>. Springer Nature, 2023.","chicago":"Michalska, Julia M, Julia Lyudchik, Philipp Velicky, Hana Korinkova, Jake Watson, Alban Cenameri, Christoph M Sommer, et al. “Imaging Brain Tissue Architecture across Millimeter to Nanometer Scales.” <i>Nature Biotechnology</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41587-023-01911-8\">https://doi.org/10.1038/s41587-023-01911-8</a>.","mla":"Michalska, Julia M., et al. “Imaging Brain Tissue Architecture across Millimeter to Nanometer Scales.” <i>Nature Biotechnology</i>, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1038/s41587-023-01911-8\">10.1038/s41587-023-01911-8</a>.","short":"J.M. Michalska, J. Lyudchik, P. Velicky, H. Korinkova, J. Watson, A. Cenameri, C.M. Sommer, N. Amberg, A. Venturino, K. Roessler, T. Czech, R. Höftberger, S. Siegert, G. Novarino, P.M. Jonas, J.G. Danzl, Nature Biotechnology (2023).","ista":"Michalska JM, Lyudchik J, Velicky P, Korinkova H, Watson J, Cenameri A, Sommer CM, Amberg N, Venturino A, Roessler K, Czech T, Höftberger R, Siegert S, Novarino G, Jonas PM, Danzl JG. 2023. Imaging brain tissue architecture across millimeter to nanometer scales. Nature Biotechnology."},"date_updated":"2024-02-21T12:18:18Z","abstract":[{"text":"Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.","lang":"eng"}],"day":"31","doi":"10.1038/s41587-023-01911-8","language":[{"iso":"eng"}],"publication":"Nature Biotechnology","month":"08","project":[{"grant_number":"I03600","name":"Optical control of synaptic function via adhesion molecules","_id":"265CB4D0-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"_id":"2548AE96-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Molecular Drug Targets","grant_number":"W1232-B24"},{"call_identifier":"FWF","_id":"25C5A090-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize","grant_number":"Z00312"},{"_id":"23889792-32DE-11EA-91FC-C7463DDC885E","name":"High content imaging to decode human immune cell interactions in health and allergic disease"},{"call_identifier":"H2020","_id":"25444568-B435-11E9-9278-68D0E5697425","name":"Probing the Reversibility of Autism Spectrum Disorders by Employing in vivo and in vitro Models","grant_number":"715508"},{"grant_number":"692692","name":"Biophysics and circuit function of a giant cortical glumatergic synapse","_id":"25B7EB9E-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"665385","name":"International IST Doctoral Program"},{"call_identifier":"H2020","_id":"fc2be41b-9c52-11eb-aca3-faa90aa144e9","name":"Synaptic computations of the hippocampal CA3 circuitry","grant_number":"101026635"}],"acknowledged_ssus":[{"_id":"ScienComp"},{"_id":"Bio"},{"_id":"PreCl"},{"_id":"LifeSc"},{"_id":"M-Shop"},{"_id":"E-Lib"}],"oa_version":"Published Version","status":"public","related_material":{"record":[{"status":"public","id":"13126","relation":"research_data"}],"link":[{"url":"https://github.com/danzllab/CATS","relation":"software"}]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1038/s41587-023-01911-8"}],"type":"journal_article","date_published":"2023-08-31T00:00:00Z","oa":1,"publication_identifier":{"eissn":["1546-1696"],"issn":["1087-0156"]}},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ddc":["570"],"related_material":{"record":[{"id":"13267","relation":"used_in_publication","status":"public"}]},"status":"public","acknowledgement":"We thank J. Vorlaufer, N. Agudelo, A. Wartak for microscope maintenance and troubleshooting, C. Kreuzinger and A. Freeman for technical assistance, and M. Šuplata for hardware control support, and Márcia Cunha dos Santos for initial exploration of software. We thank Paul Henderson for advice on deep-learning training and Michael Sixt, Scott Boyd, and Tamara Weiss for discussions and critical reading of the manuscript. Luke Lavis (Janelia Research Campus) generously provided JF585-HaloTag ligand. 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However, it has been hindered by insufficient 3D-resolution, inadequate signal-to-noise-ratio, and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine learning technology, LIONESS (Live Information-Optimized Nanoscopy Enabling Saturated Segmentation). It leverages optical modifications to stimulated emission depletion (STED) microscopy in comprehensively, extracellularly labelled tissue and prior information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise-ratio, and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D-reconstruction at synapse level incorporating molecular, activity, and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.","lang":"eng"}],"oa":1,"doi":"10.15479/AT:ISTA:12817","day":"19","date_published":"2023-05-19T00:00:00Z","type":"research_data","date_updated":"2024-01-10T08:37:48Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","short":"CC BY-SA (4.0)","name":"Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)","image":"/images/cc_by_sa.png"},"year":"2023","citation":{"chicago":"Danzl, Johann G. “Research Data for the Publication ‘Dense 4D Nanoscale Reconstruction of Living Brain Tissue.’” Institute of Science and Technology Austria, 2023. <a href=\"https://doi.org/10.15479/AT:ISTA:12817\">https://doi.org/10.15479/AT:ISTA:12817</a>.","ieee":"J. G. Danzl, “Research data for the publication ‘Dense 4D nanoscale reconstruction of living brain tissue.’” Institute of Science and Technology Austria, 2023.","ama":"Danzl JG. Research data for the publication “Dense 4D nanoscale reconstruction of living brain tissue.” 2023. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:12817\">10.15479/AT:ISTA:12817</a>","apa":"Danzl, J. G. (2023). Research data for the publication “Dense 4D nanoscale reconstruction of living brain tissue.” Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:12817\">https://doi.org/10.15479/AT:ISTA:12817</a>","ista":"Danzl JG. 2023. Research data for the publication ‘Dense 4D nanoscale reconstruction of living brain tissue’, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:12817\">10.15479/AT:ISTA:12817</a>.","mla":"Danzl, Johann G. <i>Research Data for the Publication “Dense 4D Nanoscale Reconstruction of Living Brain Tissue.”</i> Institute of Science and Technology Austria, 2023, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:12817\">10.15479/AT:ISTA:12817</a>.","short":"J.G. Danzl, (2023)."},"publisher":"Institute of Science and Technology Austria","contributor":[{"id":"39BDC62C-F248-11E8-B48F-1D18A9856A87","last_name":"Velicky","first_name":"Philipp","orcid":"0000-0002-2340-7431"},{"id":"3FB91342-F248-11E8-B48F-1D18A9856A87","first_name":"Eder","last_name":"Miguel Villalba"},{"id":"443DB6DE-F248-11E8-B48F-1D18A9856A87","last_name":"Michalska","first_name":"Julia M"},{"id":"46E28B80-F248-11E8-B48F-1D18A9856A87","first_name":"Julia","last_name":"Lyudchik"},{"first_name":"Donglai","last_name":"Wei"},{"last_name":"Lin","first_name":"Zudi"},{"id":"63836096-4690-11EA-BD4E-32803DDC885E","orcid":"0000-0002-8698-3823","last_name":"Watson","first_name":"Jake"},{"last_name":"Troidl","first_name":"Jakob"},{"first_name":"Johanna","last_name":"Beyer"},{"id":"43DF3136-F248-11E8-B48F-1D18A9856A87","last_name":"Ben Simon","first_name":"Yoav"},{"id":"4DF26D8C-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1216-9105","last_name":"Sommer","first_name":"Christoph M"},{"id":"425C1CE8-F248-11E8-B48F-1D18A9856A87","last_name":"Jahr","first_name":"Wiebke"},{"last_name":"Cenameri","first_name":"Alban","id":"9ac8f577-2357-11eb-997a-e566c5550886"},{"last_name":"Broichhagen","first_name":"Johannes"},{"first_name":"Seth G. N. ","last_name":"Grant"},{"first_name":"Peter M","last_name":"Jonas","orcid":"0000-0001-5001-4804","id":"353C1B58-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0002-7673-7178","first_name":"Gaia","last_name":"Novarino","id":"3E57A680-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Pfister","first_name":"Hanspeter"},{"last_name":"Bickel","first_name":"Bernd","orcid":"0000-0001-6511-9385","id":"49876194-F248-11E8-B48F-1D18A9856A87"}],"file_date_updated":"2023-05-18T19:51:52Z","title":"Research data for the publication \"Dense 4D nanoscale reconstruction of living brain tissue\"","month":"05","acknowledged_ssus":[{"_id":"ScienComp"},{"_id":"Bio"},{"_id":"PreCl"},{"_id":"LifeSc"},{"_id":"M-Shop"},{"_id":"E-Lib"}],"oa_version":"Published Version","date_created":"2023-04-07T11:37:40Z","department":[{"_id":"JoDa"}],"article_processing_charge":"No","author":[{"first_name":"Johann G","last_name":"Danzl","orcid":"0000-0001-8559-3973","full_name":"Danzl, Johann G","id":"42EFD3B6-F248-11E8-B48F-1D18A9856A87"}],"_id":"12817","license":"https://creativecommons.org/licenses/by-sa/4.0/","has_accepted_license":"1"},{"oa_version":"Published Version","acknowledged_ssus":[{"_id":"ScienComp"},{"_id":"CampIT"},{"_id":"E-Lib"}],"month":"05","has_accepted_license":"1","language":[{"iso":"eng"}],"publication_identifier":{"issn":["2663-337X"]},"oa":1,"supervisor":[{"full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","last_name":"Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"type":"dissertation","date_published":"2021-05-30T00:00:00Z","file":[{"success":1,"relation":"main_file","access_level":"open_access","file_id":"9419","creator":"bphuong","date_created":"2021-05-24T11:22:29Z","file_size":2673905,"checksum":"4f0abe64114cfed264f9d36e8d1197e3","date_updated":"2021-05-24T11:22:29Z","file_name":"mph-thesis-v519-pdfimages.pdf","content_type":"application/pdf"},{"date_created":"2021-05-24T11:56:02Z","checksum":"f5699e876bc770a9b0df8345a77720a2","file_size":92995100,"date_updated":"2021-05-24T11:56:02Z","content_type":"application/zip","file_name":"thesis.zip","access_level":"closed","relation":"source_file","file_id":"9420","creator":"bphuong"}],"status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","related_material":{"record":[{"relation":"part_of_dissertation","id":"7435","status":"deleted"},{"id":"7481","relation":"part_of_dissertation","status":"public"},{"relation":"part_of_dissertation","id":"9416","status":"public"},{"relation":"part_of_dissertation","id":"7479","status":"public"}]},"article_processing_charge":"No","department":[{"_id":"GradSch"},{"_id":"ChLa"}],"date_created":"2021-05-24T13:06:23Z","publication_status":"published","alternative_title":["ISTA Thesis"],"title":"Underspecification in deep learning","_id":"9418","author":[{"id":"3EC6EE64-F248-11E8-B48F-1D18A9856A87","full_name":"Bui Thi Mai, Phuong","last_name":"Bui Thi Mai","first_name":"Phuong"}],"publisher":"Institute of Science and Technology Austria","page":"125","file_date_updated":"2021-05-24T11:56:02Z","day":"30","degree_awarded":"PhD","doi":"10.15479/AT:ISTA:9418","abstract":[{"text":"Deep learning is best known for its empirical success across a wide range of applications\r\nspanning computer vision, natural language processing and speech. Of equal significance,\r\nthough perhaps less known, are its ramifications for learning theory: deep networks have\r\nbeen observed to perform surprisingly well in the high-capacity regime, aka the overfitting\r\nor underspecified regime. Classically, this regime on the far right of the bias-variance curve\r\nis associated with poor generalisation; however, recent experiments with deep networks\r\nchallenge this view.\r\n\r\nThis thesis is devoted to investigating various aspects of underspecification in deep learning.\r\nFirst, we argue that deep learning models are underspecified on two levels: a) any given\r\ntraining dataset can be fit by many different functions, and b) any given function can be\r\nexpressed by many different parameter configurations. We refer to the second kind of\r\nunderspecification as parameterisation redundancy and we precisely characterise its extent.\r\nSecond, we characterise the implicit criteria (the inductive bias) that guide learning in the\r\nunderspecified regime. Specifically, we consider a nonlinear but tractable classification\r\nsetting, and show that given the choice, neural networks learn classifiers with a large margin.\r\nThird, we consider learning scenarios where the inductive bias is not by itself sufficient to\r\ndeal with underspecification. We then study different ways of ‘tightening the specification’: i)\r\nIn the setting of representation learning with variational autoencoders, we propose a hand-\r\ncrafted regulariser based on mutual information. ii) In the setting of binary classification, we\r\nconsider soft-label (real-valued) supervision. We derive a generalisation bound for linear\r\nnetworks supervised in this way and verify that soft labels facilitate fast learning. Finally, we\r\nexplore an application of soft-label supervision to the training of multi-exit models.","lang":"eng"}],"citation":{"chicago":"Phuong, Mary. “Underspecification in Deep Learning.” Institute of Science and Technology Austria, 2021. <a href=\"https://doi.org/10.15479/AT:ISTA:9418\">https://doi.org/10.15479/AT:ISTA:9418</a>.","ieee":"M. Phuong, “Underspecification in deep learning,” Institute of Science and Technology Austria, 2021.","apa":"Phuong, M. (2021). <i>Underspecification in deep learning</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:9418\">https://doi.org/10.15479/AT:ISTA:9418</a>","ama":"Phuong M. Underspecification in deep learning. 2021. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:9418\">10.15479/AT:ISTA:9418</a>","ista":"Phuong M. 2021. Underspecification in deep learning. Institute of Science and Technology Austria.","mla":"Phuong, Mary. <i>Underspecification in Deep Learning</i>. Institute of Science and Technology Austria, 2021, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:9418\">10.15479/AT:ISTA:9418</a>.","short":"M. Phuong, Underspecification in Deep Learning, Institute of Science and Technology Austria, 2021."},"year":"2021","date_updated":"2023-09-08T11:11:12Z","ddc":["000"]},{"language":[{"iso":"eng"}],"has_accepted_license":"1","month":"06","acknowledged_ssus":[{"_id":"E-Lib"},{"_id":"CampIT"}],"oa_version":"Published Version","related_material":{"record":[{"relation":"dissertation_contains","id":"6556","status":"public"},{"relation":"dissertation_contains","id":"7093","status":"public"}]},"status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","file":[{"relation":"main_file","access_level":"open_access","file_id":"8034","creator":"khuszar","date_created":"2020-06-26T10:03:58Z","checksum":"bd8be6e4f1addc863dfcc0fad29ee9c3","file_size":2637562,"date_updated":"2020-07-14T12:48:08Z","file_name":"Kristof_Huszar-Thesis.pdf","content_type":"application/pdf"},{"content_type":"application/x-zip-compressed","file_name":"Kristof_Huszar-Thesis-source.zip","date_updated":"2020-07-14T12:48:08Z","checksum":"d5f8456202b32f4a77552ef47a2837d1","file_size":7163491,"date_created":"2020-06-26T10:10:06Z","creator":"khuszar","file_id":"8035","access_level":"closed","relation":"source_file"}],"type":"dissertation","date_published":"2020-06-26T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"supervisor":[{"orcid":"0000-0002-1494-0568","full_name":"Wagner, Uli","first_name":"Uli","last_name":"Wagner","id":"36690CA2-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Spreer, Jonathan","last_name":"Spreer","first_name":"Jonathan"}],"publication_identifier":{"issn":["2663-337X"],"isbn":["978-3-99078-006-0"]},"file_date_updated":"2020-07-14T12:48:08Z","page":"xviii+120","publisher":"Institute of Science and Technology Austria","author":[{"id":"33C26278-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5445-5057","full_name":"Huszár, Kristóf","first_name":"Kristóf","last_name":"Huszár"}],"_id":"8032","title":"Combinatorial width parameters for 3-dimensional manifolds","alternative_title":["ISTA Thesis"],"date_created":"2020-06-26T10:00:36Z","article_processing_charge":"No","department":[{"_id":"UlWa"}],"publication_status":"published","ddc":["514"],"citation":{"ista":"Huszár K. 2020. Combinatorial width parameters for 3-dimensional manifolds. Institute of Science and Technology Austria.","mla":"Huszár, Kristóf. <i>Combinatorial Width Parameters for 3-Dimensional Manifolds</i>. Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8032\">10.15479/AT:ISTA:8032</a>.","short":"K. Huszár, Combinatorial Width Parameters for 3-Dimensional Manifolds, Institute of Science and Technology Austria, 2020.","chicago":"Huszár, Kristóf. “Combinatorial Width Parameters for 3-Dimensional Manifolds.” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:8032\">https://doi.org/10.15479/AT:ISTA:8032</a>.","ieee":"K. Huszár, “Combinatorial width parameters for 3-dimensional manifolds,” Institute of Science and Technology Austria, 2020.","apa":"Huszár, K. (2020). <i>Combinatorial width parameters for 3-dimensional manifolds</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:8032\">https://doi.org/10.15479/AT:ISTA:8032</a>","ama":"Huszár K. Combinatorial width parameters for 3-dimensional manifolds. 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8032\">10.15479/AT:ISTA:8032</a>"},"year":"2020","date_updated":"2023-09-07T13:18:27Z","abstract":[{"lang":"eng","text":"Algorithms in computational 3-manifold topology typically take a triangulation as an input and return topological information about the underlying 3-manifold. However, extracting the desired information from a triangulation (e.g., evaluating an invariant) is often computationally very expensive. In recent years this complexity barrier has been successfully tackled in some cases by importing ideas from the theory of parameterized algorithms into the realm of 3-manifolds. Various computationally hard problems were shown to be efficiently solvable for input triangulations that are sufficiently “tree-like.”\r\nIn this thesis we focus on the key combinatorial parameter in the above context: we consider the treewidth of a compact, orientable 3-manifold, i.e., the smallest treewidth of the dual graph of any triangulation thereof. By building on the work of Scharlemann–Thompson and Scharlemann–Schultens–Saito on generalized Heegaard splittings, and on the work of Jaco–Rubinstein on layered triangulations, we establish quantitative relations between the treewidth and classical topological invariants of a 3-manifold. In particular, among other results, we show that the treewidth of a closed, orientable, irreducible, non-Haken 3-manifold is always within a constant factor of its Heegaard genus."}],"day":"26","degree_awarded":"PhD","doi":"10.15479/AT:ISTA:8032"},{"publication_identifier":{"issn":["2663-337X"]},"supervisor":[{"last_name":"Siekhaus","first_name":"Daria E","full_name":"Siekhaus, Daria E","orcid":"0000-0001-8323-8353","id":"3D224B9E-F248-11E8-B48F-1D18A9856A87"}],"oa":1,"date_published":"2020-12-30T00:00:00Z","type":"dissertation","file":[{"creator":"semtenan","file_id":"8984","relation":"main_file","access_level":"open_access","file_name":"Thesis_Shamsi_Emtenani_pdfA.pdf","content_type":"application/pdf","date_updated":"2021-12-31T23:30:04Z","file_size":10848175,"checksum":"ec2797ab7a6f253b35df0572b36d1b43","embargo":"2021-12-30","date_created":"2020-12-30T15:34:01Z"},{"file_size":10073648,"checksum":"cc30e6608a9815414024cf548dff3b3a","embargo_to":"open_access","date_created":"2020-12-30T15:37:36Z","file_name":"Thesis_Shamsi_Emtenani_source file.pdf","content_type":"application/pdf","date_updated":"2021-12-31T23:30:04Z","access_level":"closed","relation":"source_file","creator":"semtenan","file_id":"8985"}],"related_material":{"record":[{"relation":"part_of_dissertation","id":"8557","status":"public"},{"id":"6187","relation":"part_of_dissertation","status":"public"}]},"status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","oa_version":"Published Version","acknowledged_ssus":[{"_id":"Bio"},{"_id":"LifeSc"},{"_id":"E-Lib"},{"_id":"CampIT"}],"month":"12","has_accepted_license":"1","language":[{"iso":"eng"}],"degree_awarded":"PhD","doi":"10.15479/AT:ISTA:8983","day":"30","abstract":[{"lang":"eng","text":"Metabolic adaptation is a critical feature of migrating cells. It tunes the metabolic programs of migrating cells to allow them to efficiently exert their crucial roles in development, inflammatory responses and tumor metastasis. Cell migration through physically challenging contexts requires energy. However, how the metabolic reprogramming that underlies in vivo cell invasion is controlled is still unanswered. In my PhD project, I identify a novel conserved metabolic shift in Drosophila melanogaster immune cells that by modulating their bioenergetic potential controls developmentally programmed tissue invasion. We show that this regulation requires a novel conserved nuclear protein, named Atossa. Atossa enhances the transcription of a set of proteins, including an RNA helicase Porthos and two metabolic enzymes, each of which increases the tissue invasion of leading Drosophila macrophages and can rescue the atossa mutant phenotype. Porthos selectively regulates the translational efficiency of a subset of mRNAs containing a 5’-UTR cis-regulatory TOP-like sequence. These 5’TOPL mRNA targets encode mitochondrial-related proteins, including subunits of mitochondrial oxidative phosphorylation (OXPHOS) components III and V and other metabolic-related proteins. Porthos powers up mitochondrial OXPHOS to engender a sufficient ATP supply, which is required for tissue invasion of leading macrophages. Atossa’s two vertebrate orthologs rescue the invasion defect. In my PhD project, I elucidate that Atossa displays a conserved developmental metabolic control to modulate metabolic capacities and the cellular energy state, through altered transcription and translation, to aid the tissue infiltration of leading cells into energy demanding barriers."}],"date_updated":"2023-09-07T13:24:17Z","citation":{"mla":"Emtenani, Shamsi. <i>Metabolic Regulation of Drosophila Macrophage Tissue Invasion</i>. Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8983\">10.15479/AT:ISTA:8983</a>.","short":"S. Emtenani, Metabolic Regulation of Drosophila Macrophage Tissue Invasion, Institute of Science and Technology Austria, 2020.","ista":"Emtenani S. 2020. Metabolic regulation of Drosophila macrophage tissue invasion. Institute of Science and Technology Austria.","ama":"Emtenani S. Metabolic regulation of Drosophila macrophage tissue invasion. 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8983\">10.15479/AT:ISTA:8983</a>","apa":"Emtenani, S. (2020). <i>Metabolic regulation of Drosophila macrophage tissue invasion</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:8983\">https://doi.org/10.15479/AT:ISTA:8983</a>","chicago":"Emtenani, Shamsi. “Metabolic Regulation of Drosophila Macrophage Tissue Invasion.” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:8983\">https://doi.org/10.15479/AT:ISTA:8983</a>.","ieee":"S. Emtenani, “Metabolic regulation of Drosophila macrophage tissue invasion,” Institute of Science and Technology Austria, 2020."},"year":"2020","acknowledgement":"Also, I would like to express my appreciation and thanks to the Bioimaging facility, LSF, GSO, library, and IT people at IST Austria.","ddc":["570"],"publication_status":"published","article_processing_charge":"No","date_created":"2020-12-30T15:41:26Z","department":[{"_id":"DaSi"}],"title":"Metabolic regulation of Drosophila macrophage tissue invasion","alternative_title":["ISTA Thesis"],"_id":"8983","author":[{"first_name":"Shamsi","last_name":"Emtenani","orcid":"0000-0001-6981-6938","full_name":"Emtenani, Shamsi","id":"49D32318-F248-11E8-B48F-1D18A9856A87"}],"publisher":"Institute of Science and Technology Austria","page":"141","file_date_updated":"2021-12-31T23:30:04Z"}]