[{"oa":1,"project":[{"grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program","call_identifier":"H2020"}],"doi":"10.15479/at:ista:10799","supervisor":[{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","first_name":"Christoph","last_name":"Lampert","full_name":"Lampert, Christoph"}],"degree_awarded":"PhD","language":[{"iso":"eng"}],"month":"03","publication_identifier":{"issn":["2663-337X"],"isbn":["978-3-99078-015-2"]},"year":"2022","publication_status":"published","publisher":"Institute of Science and Technology Austria","department":[{"_id":"GradSch"},{"_id":"ChLa"}],"author":[{"id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87","last_name":"Konstantinov","first_name":"Nikola H","full_name":"Konstantinov, Nikola H"}],"related_material":{"record":[{"id":"8724","relation":"part_of_dissertation","status":"public"},{"relation":"part_of_dissertation","status":"public","id":"10803"},{"status":"public","relation":"part_of_dissertation","id":"10802"},{"id":"6590","relation":"part_of_dissertation","status":"public"}]},"date_created":"2022-02-28T13:03:49Z","date_updated":"2023-10-17T12:31:54Z","file_date_updated":"2022-03-10T12:11:48Z","ec_funded":1,"citation":{"short":"N.H. Konstantinov, Robustness and Fairness in Machine Learning, Institute of Science and Technology Austria, 2022.","mla":"Konstantinov, Nikola H. Robustness and Fairness in Machine Learning. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10799.","chicago":"Konstantinov, Nikola H. “Robustness and Fairness in Machine Learning.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:10799.","ama":"Konstantinov NH. Robustness and fairness in machine learning. 2022. doi:10.15479/at:ista:10799","ieee":"N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022.","apa":"Konstantinov, N. H. (2022). Robustness and fairness in machine learning. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10799","ista":"Konstantinov NH. 2022. Robustness and fairness in machine learning. Institute of Science and Technology Austria."},"page":"176","date_published":"2022-03-08T00:00:00Z","keyword":["robustness","fairness","machine learning","PAC learning","adversarial learning"],"day":"08","article_processing_charge":"No","has_accepted_license":"1","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"10799","ddc":["000"],"status":"public","title":"Robustness and fairness in machine learning","oa_version":"Published Version","file":[{"file_size":4204905,"content_type":"application/pdf","creator":"nkonstan","access_level":"open_access","file_name":"thesis.pdf","checksum":"626bc523ae8822d20e635d0e2d95182e","success":1,"date_updated":"2022-03-06T11:42:54Z","date_created":"2022-03-06T11:42:54Z","relation":"main_file","file_id":"10823"},{"relation":"source_file","file_id":"10824","checksum":"e2ca2b88350ac8ea1515b948885cbcb1","date_created":"2022-03-06T11:42:57Z","date_updated":"2022-03-10T12:11:48Z","access_level":"closed","file_name":"thesis.zip","content_type":"application/x-zip-compressed","file_size":22841103,"creator":"nkonstan"}],"type":"dissertation","alternative_title":["ISTA Thesis"],"abstract":[{"text":"Because of the increasing popularity of machine learning methods, it is becoming important to understand the impact of learned components on automated decision-making systems and to guarantee that their consequences are beneficial to society. In other words, it is necessary to ensure that machine learning is sufficiently trustworthy to be used in real-world applications. This thesis studies two properties of machine learning models that are highly desirable for the\r\nsake of reliability: robustness and fairness. In the first part of the thesis we study the robustness of learning algorithms to training data corruption. Previous work has shown that machine learning models are vulnerable to a range\r\nof training set issues, varying from label noise through systematic biases to worst-case data manipulations. This is an especially relevant problem from a present perspective, since modern machine learning methods are particularly data hungry and therefore practitioners often have to rely on data collected from various external sources, e.g. from the Internet, from app users or via crowdsourcing. Naturally, such sources vary greatly in the quality and reliability of the\r\ndata they provide. With these considerations in mind, we study the problem of designing machine learning algorithms that are robust to corruptions in data coming from multiple sources. We show that, in contrast to the case of a single dataset with outliers, successful learning within this model is possible both theoretically and practically, even under worst-case data corruptions. The second part of this thesis deals with fairness-aware machine learning. There are multiple areas where machine learning models have shown promising results, but where careful considerations are required, in order to avoid discrimanative decisions taken by such learned components. Ensuring fairness can be particularly challenging, because real-world training datasets are expected to contain various forms of historical bias that may affect the learning process. In this thesis we show that data corruption can indeed render the problem of achieving fairness impossible, by tightly characterizing the theoretical limits of fair learning under worst-case data manipulations. However, assuming access to clean data, we also show how fairness-aware learning can be made practical in contexts beyond binary classification, in particular in the challenging learning to rank setting.","lang":"eng"}]},{"publication_status":"published","publisher":"Institute of Science and Technology Austria","department":[{"_id":"GradSch"},{"_id":"JiFr"}],"year":"2022","date_created":"2022-07-20T11:21:53Z","date_updated":"2023-11-07T08:20:13Z","author":[{"id":"35A03822-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1286-7368","first_name":"Michelle C","last_name":"Gallei","full_name":"Gallei, Michelle C"}],"related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"8931"},{"status":"public","relation":"part_of_dissertation","id":"9287"},{"relation":"part_of_dissertation","status":"public","id":"7142"},{"id":"7465","status":"public","relation":"part_of_dissertation"},{"id":"8138","status":"public","relation":"part_of_dissertation"},{"status":"public","relation":"part_of_dissertation","id":"6260"},{"status":"public","relation":"part_of_dissertation","id":"10411"}]},"file_date_updated":"2022-07-25T11:48:45Z","ec_funded":1,"project":[{"name":"Tracing Evolution of Auxin Transport and Polarity in Plants","call_identifier":"H2020","grant_number":"742985","_id":"261099A6-B435-11E9-9278-68D0E5697425"}],"oa":1,"supervisor":[{"first_name":"Jiří","last_name":"Friml","id":"4159519E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8302-7596","full_name":"Friml, Jiří"},{"full_name":"Benková, Eva","id":"38F4F166-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8510-9739","first_name":"Eva","last_name":"Benková"},{"last_name":"Shani","first_name":"Eilon","full_name":"Shani, Eilon"}],"degree_awarded":"PhD","language":[{"iso":"eng"}],"doi":"10.15479/at:ista:11626","month":"07","publication_identifier":{"issn":["2663-337X"],"isbn":["978-3-99078-019-0"]},"status":"public","ddc":["575"],"title":"Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis thaliana","_id":"11626","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","file":[{"content_type":"application/pdf","file_size":9730864,"creator":"mgallei","file_name":"Thesis_Gallei.pdf","access_level":"open_access","date_updated":"2022-07-25T09:08:47Z","date_created":"2022-07-25T09:08:47Z","checksum":"bd7ac35403cf5b4b2607287d2a104b3a","relation":"main_file","file_id":"11645"},{"file_name":"Thesis_Gallei_source.docx","access_level":"closed","creator":"mgallei","file_size":19560720,"content_type":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","file_id":"11646","relation":"source_file","date_created":"2022-07-25T09:09:09Z","date_updated":"2022-07-25T09:39:58Z","checksum":"a9e54fe5471ba25dc13c2150c1b8ccbb"},{"file_name":"Thesis_Gallei_to_print.pdf","description":"This is the print version of the thesis including the full appendix","access_level":"closed","file_size":24542837,"content_type":"application/pdf","creator":"mgallei","relation":"source_file","file_id":"11647","date_updated":"2022-07-25T09:39:58Z","date_created":"2022-07-25T09:09:32Z","checksum":"3994f7f20058941b5bb8a16886b21e71"},{"checksum":"f24acd3c0d864f4c6676e8b0d7bfa76b","date_created":"2022-07-25T11:48:45Z","date_updated":"2022-07-25T11:48:45Z","file_id":"11650","relation":"main_file","creator":"mgallei","file_size":15435966,"content_type":"application/pdf","access_level":"open_access","file_name":"Thesis_Gallei_Appendix.pdf"}],"oa_version":"Published Version","alternative_title":["ISTA Thesis"],"type":"dissertation","abstract":[{"lang":"eng","text":"Plant growth and development is well known to be both, flexible and dynamic. The high capacity for post-embryonic organ formation and tissue regeneration requires tightly regulated intercellular communication and coordinated tissue polarization. One of the most important drivers for patterning and polarity in plant development is the phytohormone auxin. Auxin has the unique characteristic to establish polarized channels for its own active directional cell to cell transport. This fascinating phenomenon is called auxin canalization. Those auxin transport channels are characterized by the expression and polar, subcellular localization of PIN auxin efflux carriers. PIN proteins have the ability to dynamically change their localization and auxin itself can affect this by interfering with trafficking. Most of the underlying molecular mechanisms of canalization still remain enigmatic. What is known so far is that canonical auxin signaling is indispensable but also other non-canonical signaling components are thought to play a role. In order to shed light into the mysteries auf auxin canalization this study revisits the branches of auxin signaling in detail. Further a new auxin analogue, PISA, is developed which triggers auxin-like responses but does not directly activate canonical transcriptional auxin signaling. We revisit the direct auxin effect on PIN trafficking where we found that, contradictory to previous observations, auxin is very specifically promoting endocytosis of PIN2 but has no overall effect on endocytosis. Further, we evaluate which cellular processes related to PIN subcellular dynamics are involved in the establishment of auxin conducting channels and the formation of vascular tissue. We are re-evaluating the function of AUXIN BINDING PROTEIN 1 (ABP1) and provide a comprehensive picture about its developmental phneotypes and involvement in auxin signaling and canalization. Lastly, we are focusing on the crosstalk between the hormone strigolactone (SL) and auxin and found that SL is interfering with essentially all processes involved in auxin canalization in a non-transcriptional manner. Lastly we identify a new way of SL perception and signaling which is emanating from mitochondria, is independent of canonical SL signaling and is modulating primary root growth."}],"page":"248","citation":{"ista":"Gallei MC. 2022. Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis thaliana. Institute of Science and Technology Austria.","ieee":"M. C. Gallei, “Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis thaliana,” Institute of Science and Technology Austria, 2022.","apa":"Gallei, M. C. (2022). Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis thaliana. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:11626","ama":"Gallei MC. Auxin and strigolactone non-canonical signaling regulating development in Arabidopsis thaliana. 2022. doi:10.15479/at:ista:11626","chicago":"Gallei, Michelle C. “Auxin and Strigolactone Non-Canonical Signaling Regulating Development in Arabidopsis Thaliana.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:11626.","mla":"Gallei, Michelle C. Auxin and Strigolactone Non-Canonical Signaling Regulating Development in Arabidopsis Thaliana. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:11626.","short":"M.C. Gallei, Auxin and Strigolactone Non-Canonical Signaling Regulating Development in Arabidopsis Thaliana, Institute of Science and Technology Austria, 2022."},"date_published":"2022-07-20T00:00:00Z","day":"20","has_accepted_license":"1","article_processing_charge":"No"},{"ec_funded":1,"file_date_updated":"2023-02-02T09:39:25Z","department":[{"_id":"GradSch"},{"_id":"ChWo"}],"publisher":"Institute of Science and Technology Austria","publication_status":"published","year":"2022","date_updated":"2024-02-28T12:57:46Z","date_created":"2023-01-24T10:49:46Z","related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"11736"},{"id":"9818","relation":"part_of_dissertation","status":"public"},{"relation":"part_of_dissertation","status":"public","id":"8385"}]},"author":[{"full_name":"Sperl, Georg","last_name":"Sperl","first_name":"Georg","id":"4DD40360-F248-11E8-B48F-1D18A9856A87"}],"publication_identifier":{"isbn":["978-3-99078-020-6"],"issn":["2663-337X"]},"month":"09","project":[{"name":"Efficient Simulation of Natural Phenomena at Extremely Large Scales","call_identifier":"H2020","grant_number":"638176","_id":"2533E772-B435-11E9-9278-68D0E5697425"}],"oa":1,"language":[{"iso":"eng"}],"acknowledged_ssus":[{"_id":"SSU"}],"degree_awarded":"PhD","supervisor":[{"full_name":"Wojtan, Christopher J","id":"3C61F1D2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6646-5546","first_name":"Christopher J","last_name":"Wojtan"}],"doi":"10.15479/at:ista:12103","alternative_title":["ISTA Thesis"],"type":"dissertation","abstract":[{"lang":"eng","text":"The complex yarn structure of knitted and woven fabrics gives rise to both a mechanical and\r\nvisual complexity. The small-scale interactions of yarns colliding with and pulling on each\r\nother result in drastically different large-scale stretching and bending behavior, introducing\r\nanisotropy, curling, and more. While simulating cloth as individual yarns can reproduce this\r\ncomplexity and match the quality of real fabric, it may be too computationally expensive for\r\nlarge fabrics. On the other hand, continuum-based approaches do not need to discretize the\r\ncloth at a stitch-level, but it is non-trivial to find a material model that would replicate the\r\nlarge-scale behavior of yarn fabrics, and they discard the intricate visual detail. In this thesis,\r\nwe discuss three methods to try and bridge the gap between small-scale and large-scale yarn\r\nmechanics using numerical homogenization: fitting a continuum model to periodic yarn simulations, adding mechanics-aware yarn detail onto thin-shell simulations, and quantitatively\r\nfitting yarn parameters to physical measurements of real fabric.\r\nTo start, we present a method for animating yarn-level cloth effects using a thin-shell solver.\r\nWe first use a large number of periodic yarn-level simulations to build a model of the potential\r\nenergy density of the cloth, and then use it to compute forces in a thin-shell simulator. The\r\nresulting simulations faithfully reproduce expected effects like the stiffening of woven fabrics\r\nand the highly deformable nature and anisotropy of knitted fabrics at a fraction of the cost of\r\nfull yarn-level simulation.\r\nWhile our thin-shell simulations are able to capture large-scale yarn mechanics, they lack\r\nthe rich visual detail of yarn-level simulations. Therefore, we propose a method to animate\r\nyarn-level cloth geometry on top of an underlying deforming mesh in a mechanics-aware\r\nfashion in real time. Using triangle strains to interpolate precomputed yarn geometry, we are\r\nable to reproduce effects such as knit loops tightening under stretching at negligible cost.\r\nFinally, we introduce a methodology for inverse-modeling of yarn-level mechanics of cloth,\r\nbased on the mechanical response of fabrics in the real world. We compile a database from\r\nphysical tests of several knitted fabrics used in the textile industry spanning diverse physical\r\nproperties like stiffness, nonlinearity, and anisotropy. We then develop a system for approximating these mechanical responses with yarn-level cloth simulation, using homogenized\r\nshell models to speed up computation and adding some small-but-necessary extensions to\r\nyarn-level models used in computer graphics.\r\n"}],"status":"public","title":"Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting","ddc":["000","620"],"_id":"12358","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","file":[{"description":"This is the main PDF file of the thesis. 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Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale Detail, and Quantitative Fitting. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:12103.","short":"G. Sperl, Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale Detail, and Quantitative Fitting, Institute of Science and Technology Austria, 2022.","ista":"Sperl G. 2022. Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. Institute of Science and Technology Austria.","ieee":"G. Sperl, “Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting,” Institute of Science and Technology Austria, 2022.","apa":"Sperl, G. (2022). Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12103","ama":"Sperl G. Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. 2022. doi:10.15479/at:ista:12103"},"date_published":"2022-09-22T00:00:00Z"},{"file_date_updated":"2022-02-22T07:20:12Z","ec_funded":1,"author":[{"id":"48C55298-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-1106-4419","first_name":"Wojciech","last_name":"Rzadkowski","full_name":"Rzadkowski, Wojciech"}],"related_material":{"record":[{"status":"public","relation":"part_of_dissertation","id":"10762"},{"id":"8644","relation":"part_of_dissertation","status":"public"},{"relation":"part_of_dissertation","status":"public","id":"7956"},{"status":"public","relation":"part_of_dissertation","id":"415"}]},"date_updated":"2024-02-28T13:01:59Z","date_created":"2022-02-16T13:27:37Z","year":"2022","publication_status":"published","department":[{"_id":"GradSch"},{"_id":"MiLe"}],"publisher":"Institute of Science and Technology Austria","month":"02","publication_identifier":{"issn":["2663-337X"]},"doi":"10.15479/at:ista:10759","degree_awarded":"PhD","supervisor":[{"full_name":"Lemeshko, Mikhail","first_name":"Mikhail","last_name":"Lemeshko","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6990-7802"}],"language":[{"iso":"eng"}],"oa":1,"project":[{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","grant_number":"665385","call_identifier":"H2020","name":"International IST Doctoral Program"}],"abstract":[{"lang":"eng","text":"In this Thesis, I study composite quantum impurities with variational techniques, both inspired by machine learning as well as fully analytic. I supplement this with exploration of other applications of machine learning, in particular artificial neural networks, in many-body physics. In Chapters 3 and 4, I study quasiparticle systems with variational approach. I derive a Hamiltonian describing the angulon quasiparticle in the presence of a magnetic field. I apply analytic variational treatment to this Hamiltonian. Then, I introduce a variational approach for non-additive systems, based on artificial neural networks. I exemplify this approach on the example of the polaron quasiparticle (Fröhlich Hamiltonian). In Chapter 5, I continue using artificial neural networks, albeit in a different setting. I apply artificial neural networks to detect phases from snapshots of two types physical systems. Namely, I study Monte Carlo snapshots of multilayer classical spin models as well as molecular dynamics maps of colloidal systems. The main type of networks that I use here are convolutional neural networks, known for their applicability to image data."}],"type":"dissertation","alternative_title":["ISTA Thesis"],"oa_version":"Published Version","file":[{"access_level":"closed","file_name":"Rzadkowski_thesis_final_source.zip","content_type":"application/zip","file_size":17668233,"creator":"wrzadkow","relation":"source_file","file_id":"10785","checksum":"0fc54ad1eaede879c665ac9b53c93e22","date_updated":"2022-02-22T07:20:12Z","date_created":"2022-02-21T13:58:16Z"},{"file_name":"Rzadkowski_thesis_final.pdf","access_level":"open_access","file_size":13307331,"content_type":"application/pdf","creator":"wrzadkow","relation":"main_file","file_id":"10786","date_created":"2022-02-21T14:02:54Z","date_updated":"2022-02-21T14:02:54Z","checksum":"22d2d7af37ca31f6b1730c26cac7bced","success":1}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"10759","title":"Analytic and machine learning approaches to composite quantum impurities","ddc":["530"],"status":"public","day":"21","has_accepted_license":"1","article_processing_charge":"No","date_published":"2022-02-21T00:00:00Z","citation":{"mla":"Rzadkowski, Wojciech. Analytic and Machine Learning Approaches to Composite Quantum Impurities. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10759.","short":"W. Rzadkowski, Analytic and Machine Learning Approaches to Composite Quantum Impurities, Institute of Science and Technology Austria, 2022.","chicago":"Rzadkowski, Wojciech. “Analytic and Machine Learning Approaches to Composite Quantum Impurities.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:10759.","ama":"Rzadkowski W. Analytic and machine learning approaches to composite quantum impurities. 2022. doi:10.15479/at:ista:10759","ista":"Rzadkowski W. 2022. Analytic and machine learning approaches to composite quantum impurities. Institute of Science and Technology Austria.","apa":"Rzadkowski, W. (2022). Analytic and machine learning approaches to composite quantum impurities. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10759","ieee":"W. Rzadkowski, “Analytic and machine learning approaches to composite quantum impurities,” Institute of Science and Technology Austria, 2022."},"page":"120"},{"_id":"11196","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","status":"public","title":"Nanoarchitecture of hippocampal mossy fiber-CA3 pyramidal neuron synapses","ddc":["570"],"file":[{"checksum":"1616a8bf6f13a57c892dac873dcd0936","date_created":"2022-04-20T14:21:56Z","date_updated":"2023-04-20T22:30:03Z","relation":"main_file","file_id":"11220","embargo":"2023-04-19","content_type":"application/pdf","file_size":21273537,"creator":"okim","access_level":"open_access","file_name":"Olena_KIM_thesis_final.pdf"},{"file_name":"KIM_thesis_final.zip","embargo_to":"open_access","access_level":"closed","creator":"okim","content_type":"application/x-zip-compressed","file_size":59248569,"file_id":"11221","relation":"source_file","date_created":"2022-04-20T14:22:56Z","date_updated":"2023-04-20T22:30:03Z","checksum":"1acb433f98dc42abb0b4b0cbb0c4b918"}],"oa_version":"Published Version","type":"dissertation","alternative_title":["ISTA Thesis"],"abstract":[{"lang":"eng","text":"One of the fundamental questions in Neuroscience is how the structure of synapses and their physiological properties are related. While synaptic transmission remains a dynamic process, electron microscopy provides images with comparably low temporal resolution (Studer et al., 2014). The current work overcomes this challenge and describes an improved “Flash and Freeze” technique (Watanabe et al., 2013a; Watanabe et al., 2013b) to study synaptic transmission at the hippocampal mossy fiber-CA3 pyramidal neuron synapses, using mouse acute brain slices and organotypic slices culture. The improved method allowed for selective stimulation of presynaptic mossy fiber boutons and the observation of synaptic vesicle pool dynamics at the active zones. Our results uncovered several intriguing morphological features of mossy fiber boutons. First, the docked vesicle pool was largely depleted (more than 70%) after stimulation, implying that the docked synaptic vesicles pool and readily releasable pool are vastly overlapping in mossy fiber boutons. Second, the synaptic vesicles are skewed towards larger diameters, displaying a wide range of sizes. An increase in the mean diameter of synaptic vesicles, after single and repetitive stimulation, suggests that smaller vesicles have a higher release probability. Third, we observed putative endocytotic structures after moderate light stimulation, matching the timing of previously described ultrafast endocytosis (Watanabe et al., 2013a; Delvendahl et al., 2016). \r\n\tIn addition, synaptic transmission depends on a sophisticated system of protein machinery and calcium channels (Südhof, 2013b), which amplifies the challenge in studying synaptic communication as these interactions can be potentially modified during synaptic plasticity. And although recent study elucidated the potential correlation between physiological and morphological properties of synapses during synaptic plasticity (Vandael et al., 2020), the molecular underpinning of it remains unknown. Thus, the presented work tries to overcome this challenge and aims to pinpoint changes in the molecular architecture at hippocampal mossy fiber bouton synapses during short- and long-term potentiation (STP and LTP), we combined chemical potentiation, with the application of a cyclic adenosine monophosphate agonist (i.e. forskolin) and freeze-fracture replica immunolabelling. This method allowed the localization of membrane-bound proteins with nanometer precision within the active zone, in particular, P/Q-type calcium channels and synaptic vesicle priming proteins Munc13-1/2. First, we found that the number of clusters of Munc13-1 in the mossy fiber bouton active zone increased significantly during STP, but decreased to lower than the control value during LTP. Secondly, although the distance between the calcium channels and Munc13-1s did not change after induction of STP, it shortened during the LTP phase. Additionally, forskolin did not affect Munc13-2 distribution during STP and LTP. These results indicate the existence of two distinct mechanisms that govern STP and LTP at mossy fiber bouton synapses: an increase in the readily realizable pool in the case of STP and a potential increase in release probability during LTP. “Flash and freeze” and functional electron microscopy, are versatile methods that can be successfully applied to intact brain circuits to study synaptic transmission even at the molecular level.\r\n"}],"citation":{"ama":"Kim O. Nanoarchitecture of hippocampal mossy fiber-CA3 pyramidal neuron synapses. 2022. doi:10.15479/at:ista:11196","ista":"Kim O. 2022. Nanoarchitecture of hippocampal mossy fiber-CA3 pyramidal neuron synapses. Institute of Science and Technology Austria.","ieee":"O. Kim, “Nanoarchitecture of hippocampal mossy fiber-CA3 pyramidal neuron synapses,” Institute of Science and Technology Austria, 2022.","apa":"Kim, O. (2022). Nanoarchitecture of hippocampal mossy fiber-CA3 pyramidal neuron synapses. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:11196","mla":"Kim, Olena. Nanoarchitecture of Hippocampal Mossy Fiber-CA3 Pyramidal Neuron Synapses. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:11196.","short":"O. Kim, Nanoarchitecture of Hippocampal Mossy Fiber-CA3 Pyramidal Neuron Synapses, Institute of Science and Technology Austria, 2022.","chicago":"Kim, Olena. “Nanoarchitecture of Hippocampal Mossy Fiber-CA3 Pyramidal Neuron Synapses.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:11196."},"page":"132","date_published":"2022-04-20T00:00:00Z","article_processing_charge":"No","has_accepted_license":"1","day":"20","year":"2022","publisher":"Institute of Science and Technology Austria","department":[{"_id":"PeJo"},{"_id":"GradSch"}],"publication_status":"published","related_material":{"record":[{"id":"11222","relation":"part_of_dissertation","status":"public"},{"status":"public","relation":"part_of_dissertation","id":"7473"}]},"author":[{"id":"3F8ABDDA-F248-11E8-B48F-1D18A9856A87","first_name":"Olena","last_name":"Kim","full_name":"Kim, Olena"}],"date_updated":"2023-08-18T06:31:52Z","date_created":"2022-04-20T09:47:12Z","ec_funded":1,"file_date_updated":"2023-04-20T22:30:03Z","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png"},"oa":1,"project":[{"name":"Presynaptic calcium channels distribution and impact on coupling at the hippocampal mossy fiber synapse","call_identifier":"H2020","_id":"25BAF7B2-B435-11E9-9278-68D0E5697425","grant_number":"708497"},{"grant_number":"692692","_id":"25B7EB9E-B435-11E9-9278-68D0E5697425","name":"Biophysics and circuit function of a giant cortical glumatergic synapse","call_identifier":"H2020"},{"name":"Zellkommunikation in Gesundheit und Krankheit","call_identifier":"FWF","grant_number":"W01205","_id":"25C3DBB6-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FWF","name":"The Wittgenstein Prize","_id":"25C5A090-B435-11E9-9278-68D0E5697425","grant_number":"Z00312"}],"doi":"10.15479/at:ista:11196","language":[{"iso":"eng"}],"supervisor":[{"last_name":"Jonas","first_name":"Peter M","orcid":"0000-0001-5001-4804","id":"353C1B58-F248-11E8-B48F-1D18A9856A87","full_name":"Jonas, Peter M"}],"degree_awarded":"PhD","acknowledged_ssus":[{"_id":"EM-Fac"},{"_id":"PreCl"}],"publication_identifier":{"issn":["2663-337X"]},"month":"04"}]