--- OA_embargo: '12' OA_place: publisher _id: '22744' acknowledged_ssus: - _id: Bio - _id: EM-Fac - _id: ScienComp - _id: LifeSc acknowledgement: "This work was supported by the ERC StG grant ActinID (PRA01221F1049A) awarded to Florian\r\nSchur, the ERC-SyG grant Pushing from within (P01071793) awarded to Michael Sixt, and by ISTA.\r\nI would like to thank the Scientific Service Units at ISTA for their essential support throughout\r\nthis work. In particular, I am grateful to the Electron Microscopy Facility, Imaging and Optics\r\nFacility, Zebrafish Facility, Scientific Computing Facility, and Lab Support Facility for their services,\r\nand technical support, all of which were important for the successful completion of this project." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Manjunath full_name: Javoor, Manjunath id: 305ab18b-dc7d-11ea-9b2f-b58195228ea2 last_name: Javoor orcid: 0000-0003-2311-2112 citation: ama: Javoor M. Large-scale imaging of cellular actin networks at single filament resolution using montage cryo-electron tomography. 2026. doi:10.15479/AT-ISTA-22744 apa: Javoor, M. (2026). Large-scale imaging of cellular actin networks at single filament resolution using montage cryo-electron tomography. Institute of Science and Technology Austria . https://doi.org/10.15479/AT-ISTA-22744 chicago: Javoor, Manjunath. “Large-Scale Imaging of Cellular Actin Networks at Single Filament Resolution Using Montage Cryo-Electron Tomography.” Institute of Science and Technology Austria , 2026. https://doi.org/10.15479/AT-ISTA-22744. ieee: M. Javoor, “Large-scale imaging of cellular actin networks at single filament resolution using montage cryo-electron tomography,” Institute of Science and Technology Austria , 2026. ista: Javoor M. 2026. Large-scale imaging of cellular actin networks at single filament resolution using montage cryo-electron tomography. Institute of Science and Technology Austria . mla: Javoor, Manjunath. Large-Scale Imaging of Cellular Actin Networks at Single Filament Resolution Using Montage Cryo-Electron Tomography. Institute of Science and Technology Austria , 2026, doi:10.15479/AT-ISTA-22744. short: M. Javoor, Large-Scale Imaging of Cellular Actin Networks at Single Filament Resolution Using Montage Cryo-Electron Tomography, Institute of Science and Technology Austria , 2026. corr_author: '1' date_created: 2026-08-21T09:11:04Z date_published: 2026-08-21T00:00:00Z date_updated: 2026-09-03T09:36:24Z day: '21' ddc: - '570' degree_awarded: PhD department: - _id: GradSch - _id: FlSc - _id: MiSi doi: 10.15479/AT-ISTA-22744 doi_confirm: '1' file: - access_level: closed checksum: f9c2847df9f1ac5a3d60c06b3b81a450 content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: mjavoor date_created: 2026-08-26T12:02:47Z date_updated: 2026-08-27T12:48:42Z file_id: '22767' file_name: 2026_Javoor_Manjunath_Thesis.docx file_size: 27430796 relation: source_file - access_level: closed checksum: 8e9b4c0fcafbccf5c3796eacc9134a08 content_type: application/pdf creator: mjavoor date_created: 2026-08-26T12:02:46Z date_updated: 2026-08-26T12:02:46Z embargo: 2027-08-21 embargo_to: open_access file_id: '22768' file_name: 2026_Javoor_Manjunath_Thesis.pdf file_size: 19489230 relation: main_file file_date_updated: 2026-08-27T12:48:42Z fulldoi: https://doi.org/10.15479/AT-ISTA-22744 has_accepted_license: '1' keyword: - Actin cytoskeleton - Cell migration - cryo-electron tomography language: - iso: eng license: https://creativecommons.org/licenses/by-nc-nd/4.0/ month: '08' oa_version: None page: '121' project: - _id: bd980d18-d553-11ed-ba76-ceaa645c97eb grant_number: '101076260' name: A molecular atlas of Actin filament IDentities in the cell motility machinery - _id: bd91e723-d553-11ed-ba76-fe7eeb2185fd grant_number: '101071793' name: 'Pushing from within: Control of cell shape, integrity and motility by cytoskeletal pushing forces' publication_identifier: isbn: - '978-3-99078-090-9 ' issn: - 2663-337X publication_status: published publisher: 'Institute of Science and Technology Austria ' related_material: record: - id: '12334' relation: part_of_dissertation status: public - id: '21762' relation: part_of_dissertation status: public - id: '19795' relation: part_of_dissertation status: public - id: '12421' relation: part_of_dissertation status: public status: public supervisor: - first_name: Florian KM full_name: Schur, Florian KM id: 48AD8942-F248-11E8-B48F-1D18A9856A87 last_name: Schur orcid: 0000-0003-4790-8078 - first_name: Michael K full_name: Sixt, Michael K id: 41E9FBEA-F248-11E8-B48F-1D18A9856A87 last_name: Sixt orcid: 0000-0002-6620-9179 title: Large-scale imaging of cellular actin networks at single filament resolution using montage cryo-electron tomography tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2026' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '22750' abstract: - lang: eng text: 'Robust oxygenic photosynthesis requires the efficient assembly and repair of the multi-subunit oxygen-evolving photosystem II (PSII) complex. Previous cryogenic electron microscopy (cryo-EM) structures of PSII assembly/disassembly intermediates have relied on the analysis of deletion mutants or removal of PSII subunits in vitro. Here we report the cryo-EM structures of naturally occurring dimeric PSII intermediates from the cyanobacterium Thermosynechococcus vestitus at a resolution of about 2.2 Å. These intermediates contain inactive dimers lacking the oxygen-evolving complex (OEC) and semi-active dimers with the OEC present in one of the two monomers. Our structural data provide a mechanism for how assembly and disassembly of the Mn4CaO5 cluster is coordinated with the binding and release of the extrinsic proteins: restructuring of the C-terminal tail of D1 subunit during assembly or disassembly of the Mn cluster triggers conformational changes in D2, CP47 and CP43 to drive the binding/release of the extrinsic proteins. A combination of structural and mass spectrometry data also suggests that the inactive PSII complexes may include damaged complexes containing oxidized D1-His332, a monodentate ligand to one of the Mn ions of the OEC.' acknowledged_ssus: - _id: EM-Fac - _id: LifeSc - _id: ScienComp acknowledgement: 'P.J.N. is grateful for the support from the Imperial College Electron Microscopy Center. L.A.S. acknowledges the support from the Scientific Service Units (SSU) of IST Austria: the Electron Microscopy Facility (EMF), the Life Science Facility (LSF) and the IST high-performance computing cluster. P.J.N. is grateful for the support of the Biotechnology & Biological Sciences Research Council (awards BB/I00937X/1, BB/L003260/1 and BB/P00931X/1). L.A.S. is grateful to IST Austria for providing the funding.' article_number: '8433' article_processing_charge: Yes article_type: original author: - first_name: Ziyu full_name: Zhao, Ziyu id: a63fe682-9f3a-11ee-bf8c-cfdf919b9850 last_name: Zhao - first_name: Irene full_name: Vercellino, Irene id: 3ED6AF16-F248-11E8-B48F-1D18A9856A87 last_name: Vercellino orcid: 0000-0001-5618-3449 - first_name: Julian P. full_name: Whitelegge, Julian P. last_name: Whitelegge - first_name: Karim full_name: Maghlaoui, Karim last_name: Maghlaoui - first_name: Wojciech full_name: Białek, Wojciech last_name: Białek - first_name: Peter J. full_name: Nixon, Peter J. last_name: Nixon - first_name: Leonid A full_name: Sazanov, Leonid A id: 338D39FE-F248-11E8-B48F-1D18A9856A87 last_name: Sazanov orcid: 0000-0002-0977-7989 citation: ama: Zhao Z, Vercellino I, Whitelegge JP, et al. Cryo-EM structures of naturally occurring dimeric photosystem II complexes lacking the Mn4CaO5 cluster. Nature Communications. 2026;17. doi:10.1038/s41467-026-75324-x apa: Zhao, Z., Vercellino, I., Whitelegge, J. P., Maghlaoui, K., Białek, W., Nixon, P. J., & Sazanov, L. A. (2026). Cryo-EM structures of naturally occurring dimeric photosystem II complexes lacking the Mn4CaO5 cluster. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-026-75324-x chicago: Zhao, Ziyu, Irene Vercellino, Julian P. Whitelegge, Karim Maghlaoui, Wojciech Białek, Peter J. Nixon, and Leonid A Sazanov. “Cryo-EM Structures of Naturally Occurring Dimeric Photosystem II Complexes Lacking the Mn4CaO5 Cluster.” Nature Communications. Springer Nature, 2026. https://doi.org/10.1038/s41467-026-75324-x. ieee: Z. Zhao et al., “Cryo-EM structures of naturally occurring dimeric photosystem II complexes lacking the Mn4CaO5 cluster,” Nature Communications, vol. 17. Springer Nature, 2026. ista: Zhao Z, Vercellino I, Whitelegge JP, Maghlaoui K, Białek W, Nixon PJ, Sazanov LA. 2026. Cryo-EM structures of naturally occurring dimeric photosystem II complexes lacking the Mn4CaO5 cluster. Nature Communications. 17, 8433. mla: Zhao, Ziyu, et al. “Cryo-EM Structures of Naturally Occurring Dimeric Photosystem II Complexes Lacking the Mn4CaO5 Cluster.” Nature Communications, vol. 17, 8433, Springer Nature, 2026, doi:10.1038/s41467-026-75324-x. short: Z. Zhao, I. Vercellino, J.P. Whitelegge, K. Maghlaoui, W. Białek, P.J. Nixon, L.A. Sazanov, Nature Communications 17 (2026). corr_author: '1' das_tickbox: '1' dataavailabilitystatement: The cryo-EM maps are deposited in the Electron Microscopy Data Bank under accession number EMD- 51100 (inactive dimer), EMD- 51102 (active dimer) and EMD-51101 (semi-active dimer). The models are deposited in the Protein Data Bank under accession numbers 9G6F (inactive dimer), 9G6H (active dimer) and 9G6G (semi-active dimer). Mass spectrometry data was uploaded to MassIVE with accession code MSV000101057. Source data are provided with this paper. date_created: 2026-08-23T22:01:46Z date_published: 2026-08-17T00:00:00Z date_updated: 2026-09-07T13:17:53Z day: '17' ddc: - '570' department: - _id: LeSa doi: 10.1038/s41467-026-75324-x external_id: pmid: - '42420307' file: - access_level: open_access checksum: a820b736585de22bcfa30ebdd25a6dd2 content_type: application/pdf creator: dernst date_created: 2026-09-07T13:13:03Z date_updated: 2026-09-07T13:13:03Z file_id: '22843' file_name: 2026_NatureComm_Zhao.pdf file_size: 3370665 relation: main_file success: 1 file_date_updated: 2026-09-07T13:13:03Z fulldoi: https://doi.org/10.1038/s41467-026-75324-x has_accepted_license: '1' intvolume: ' 17' language: - iso: eng month: '08' oa: 1 oa_version: Published Version pmid: 1 publication: Nature Communications publication_identifier: eissn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: yes title: Cryo-EM structures of naturally occurring dimeric photosystem II complexes lacking the Mn4CaO5 cluster tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 17 year: '2026' ... --- OA_place: repository _id: '22852' abstract: - lang: eng text: "This Research Data contains supplemental videos for Chapter 4 \"Designing bistable nanostructures for target behavior\" of my PhD Thesis \"Biological functionality without biochemistry: designing nanomachines for target behavior\".\r\nSupplemental video 1: Video showing the transition pathway of a bistable nanostructure with sphere-based arms, corresponding to the Machine in Scenario 4.\r\nSupplemental video 2: Video showing the transition pathway of the Source in Scenario 1. The arm tips change sides during the transition, demonstrating that the arms pass through each other.\r\nSupplemental video 3: Video showing the transition pathway of a fully polyhedral hinge structure with unconstrained arms. Note that we only show the ends of the arms.\r\nSupplemental video 4: Video showing the transition pathway of the coupled energy-delivery reaction of a Machine (gray) and a Source (blue) nanostructure for the optimized parameters in Scenario 3. Note that we only show the ends of the arms." acknowledged_ssus: - _id: ScienComp article_processing_charge: No author: - first_name: Andreas full_name: Ehrmann, Andreas id: eaa689ed-f6e0-11ea-865d-bd98cbcf83c2 last_name: Ehrmann orcid: 0000-0002-0997-5678 citation: ama: Ehrmann A. Supplemental videos for Designing bistable nanostructures for target behavior. 2026. doi:10.15479/AT-ISTA-22852 apa: Ehrmann, A. (2026). Supplemental videos for Designing bistable nanostructures for target behavior. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-22852 chicago: Ehrmann, Andreas. “Supplemental Videos for Designing Bistable Nanostructures for Target Behavior.” Institute of Science and Technology Austria, 2026. https://doi.org/10.15479/AT-ISTA-22852. ieee: A. Ehrmann, “Supplemental videos for Designing bistable nanostructures for target behavior.” Institute of Science and Technology Austria, 2026. ista: Ehrmann A. 2026. Supplemental videos for Designing bistable nanostructures for target behavior, Institute of Science and Technology Austria, 10.15479/AT-ISTA-22852. mla: Ehrmann, Andreas. Supplemental Videos for Designing Bistable Nanostructures for Target Behavior. Institute of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-22852. short: A. Ehrmann, (2026). contributor: - contributor_type: data_collector first_name: Andreas id: eaa689ed-f6e0-11ea-865d-bd98cbcf83c2 last_name: Ehrmann orcid: 0000-0002-0997-5678 corr_author: '1' date_created: 2026-09-08T11:32:53Z date_published: 2026-09-09T00:00:00Z date_updated: 2026-09-09T07:24:10Z day: '09' department: - _id: GradSch - _id: CaGo - _id: EdHa doi: 10.15479/AT-ISTA-22852 doi_confirm: '1' file: - access_level: open_access checksum: 966417b3eab49523068bb0dfba4ecc07 content_type: video/mp4 creator: aehrmann date_created: 2026-09-08T11:31:32Z date_updated: 2026-09-08T11:31:32Z file_id: '22853' file_name: Supplemental video 1.mp4 file_size: 1513964 relation: main_file success: 1 - access_level: open_access checksum: deddcae30875bff8e59d5dc9ee3c196f content_type: video/mp4 creator: aehrmann date_created: 2026-09-08T11:31:37Z date_updated: 2026-09-08T11:31:37Z file_id: '22854' file_name: Supplemental video 2.mp4 file_size: 2634179 relation: main_file success: 1 - access_level: open_access checksum: 721446b7595edc670d877185637cf15c content_type: video/mp4 creator: aehrmann date_created: 2026-09-08T11:31:42Z date_updated: 2026-09-08T11:31:42Z file_id: '22855' file_name: Supplemental video 3.mp4 file_size: 2080684 relation: main_file success: 1 - access_level: open_access checksum: da44153821aa4f8f3cbc2860ef6d1bad content_type: video/mp4 creator: aehrmann date_created: 2026-09-08T11:31:48Z date_updated: 2026-09-08T11:31:48Z file_id: '22856' file_name: Supplemental video 4.mp4 file_size: 4047831 relation: main_file success: 1 - access_level: open_access checksum: 1e94cd067809e1a93e21676181b4c102 content_type: text/plain creator: aehrmann date_created: 2026-09-08T18:36:14Z date_updated: 2026-09-08T18:36:14Z file_id: '22860' file_name: README.txt file_size: 1122 relation: main_file success: 1 file_date_updated: 2026-09-08T18:36:14Z fulldoi: https://doi.org/10.15479/AT-ISTA-22852 has_accepted_license: '1' keyword: - functional nanostructures - bistability - target behavior - transition pathway month: '09' oa: 1 oa_version: None project: - _id: 90a98bb5-16d5-11f0-9cad-9675f3f8015d grant_number: PAT 8537123 name: Functional bio-inspired nanomachines from sticky colloids publisher: Institute of Science and Technology Austria status: public title: Supplemental videos for Designing bistable nanostructures for target behavior tmp: image: /images/cc_by_nc_sa.png legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) short: CC BY-NC-SA (4.0) type: research_data user_id: 68b8ca59-c5b3-11ee-8790-cd641c68093d year: '2026' ... --- OA_place: publisher _id: '22873' acknowledged_ssus: - _id: ScienComp - _id: CampIT acknowledgement: I acknowledge funding by the Austrian Science Fund (FWF) [10.55776/PAT8537123]. alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Andreas full_name: Ehrmann, Andreas id: eaa689ed-f6e0-11ea-865d-bd98cbcf83c2 last_name: Ehrmann orcid: 0000-0002-0997-5678 citation: ama: 'Ehrmann A. Biological functionality without biochemistry: Designing nanomachines for target behavior. 2026. doi:10.15479/AT-ISTA-22873' apa: 'Ehrmann, A. (2026). Biological functionality without biochemistry: Designing nanomachines for target behavior. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-22873' chicago: 'Ehrmann, Andreas. “Biological Functionality without Biochemistry: Designing Nanomachines for Target Behavior.” Institute of Science and Technology Austria, 2026. https://doi.org/10.15479/AT-ISTA-22873.' ieee: 'A. Ehrmann, “Biological functionality without biochemistry: Designing nanomachines for target behavior,” Institute of Science and Technology Austria, 2026.' ista: 'Ehrmann A. 2026. Biological functionality without biochemistry: Designing nanomachines for target behavior. Institute of Science and Technology Austria.' mla: 'Ehrmann, Andreas. Biological Functionality without Biochemistry: Designing Nanomachines for Target Behavior. Institute of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-22873.' short: 'A. Ehrmann, Biological Functionality without Biochemistry: Designing Nanomachines for Target Behavior, Institute of Science and Technology Austria, 2026.' corr_author: '1' das_tickbox: '0' date_created: 2026-09-09T12:18:24Z date_published: 2026-09-04T00:00:00Z date_updated: 2026-09-18T11:28:29Z day: '04' ddc: - '530' - '600' - '621' - '004' - '005' degree_awarded: PhD department: - _id: GradSch - _id: CaGo - _id: EdHa doi: 10.15479/AT-ISTA-22873 doi_confirm: '1' file: - access_level: closed checksum: a5e3d79e0e5f3f4fb3c414e48116dc9e content_type: application/pdf creator: aehrmann date_created: 2026-09-11T07:23:57Z date_updated: 2026-09-17T08:59:39Z embargo: 2027-01-15 embargo_to: open_access file_id: '22901' file_name: 2026_Ehrmann_Andreas_Thesis.pdf file_size: 14782662 relation: main_file - access_level: closed checksum: 0235b9c52fee0c97f402f1c3d4efc1eb content_type: application/zip creator: aehrmann date_created: 2026-09-11T07:24:14Z date_updated: 2026-09-11T07:24:14Z description: All LaTeX files to compile my PhD Thesis. file_id: '22902' file_name: thesis_latex_files.zip file_size: 155228 relation: source_file file_date_updated: 2026-09-17T08:59:39Z fulldoi: https://doi.org/10.15479/AT-ISTA-22873 has_accepted_license: '1' keyword: - PhD Thesis - functional nanomachines - biological functionality - nanotechnology - energy delivery - target behavior - dynamics - design principles - optimization - differentiable statistical physics - machine learning language: - iso: eng month: '09' oa_version: Published Version page: '168' project: - _id: 90a98bb5-16d5-11f0-9cad-9675f3f8015d grant_number: PAT 8537123 name: Functional bio-inspired nanomachines from sticky colloids publication_identifier: isbn: - 978-3-99078-092-3 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '22893' relation: part_of_dissertation status: public - id: '22892' relation: part_of_dissertation status: public researchdata_availability: upon request status: public supervisor: - first_name: Carl Peter full_name: Goodrich, Carl Peter id: EB352CD2-F68A-11E9-89C5-A432E6697425 last_name: Goodrich orcid: 0000-0002-1307-5074 - first_name: Edouard B full_name: Hannezo, Edouard B id: 3A9DB764-F248-11E8-B48F-1D18A9856A87 last_name: Hannezo orcid: 0000-0001-6005-1561 supplementarymaterial: not applicable title: 'Biological functionality without biochemistry: Designing nanomachines for target behavior' tmp: image: /images/cc_by_nc_sa.png legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) short: CC BY-NC-SA (4.0) type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2026' ... --- OA_place: publisher _id: '21198' abstract: - lang: eng text: "In recent years there has been a massive increase in the amount of data generated in a\r\ndecentralized manner. Ever more powerful edge devices, such as smartphones, have become\r\nubiquitous in most societies on earth. Through text typed, photos taken and apps used,\r\nthese devices, which we refer to as clients, generate enormous amounts of high quality and\r\ncomplex data. Moreover, the nature of these devices means the data they generate is often\r\nsensitive and privacy concerns prevent it being gathered and stored in a central location. This\r\npresents a challenge to the modern machine learning paradigm that requires central access\r\nto large amounts of data. Federated learning (FL) has emerged as one of the answers to\r\nthis problem. Rather than bringing the data to the model, FL sends the model to the data.\r\nModel training takes place on device, with periodically synchronized updates, allowing data to\r\nremain locally stored. While this approach offers significant privacy advantages it comes with\r\nits own set of unique challenges. These include: data heterogeneity, the notion that different\r\ndevices generate data in distinct ways which can negatively impact training dynamics; systems\r\nheterogeneity, meaning that different devices may have differing hardware specifications; high\r\ncommunication costs, which are induced by the repeated transferring of models over the\r\nnetwork and low device computational power, which limits the use of larger models on device.\r\nIn this thesis we present a range of methods for federated learning. We focus primarily on\r\nthe challenge of data heterogeneity, though the methods presented are designed to be well\r\nadapted to the other challenges of a federated setting, such as the constraints of limited\r\ncompute and communication overhead. We first present a method for explicitly modeling client\r\ndata heterogeneity. The approach formulates clients as samples from a certain probability\r\ndistribution and infers the parameters of this distribution from the available training clients.\r\nThis learned distribution then represents the heterogeneity present among the clients and can\r\nbe sampled from in order to create new simulated clients that are similar to the real clients we\r\nhave observed so far. Following this we present two methods for directly dealing with data\r\nheterogeneity through personalization. Highly heterogeneous client data distributions can mean\r\nthat learning a single global model becomes suboptimal, and some form of personalization of\r\nmodels to each individual client is required. Our approaches are based around hypernetworks,\r\nwhich we use to generate personalized model parameters without the need for additional\r\ntraining or finetuning. In the first approach we focus on generating full parameterizations of\r\nclient models using learned embeddings of client data and labels, with a hypernetwork located\r\non the central server. In the second approach we address the more challenging scenario where\r\nwe want to generate a personalized model for a client without any label information. The\r\nhypernetwork is trained to generate a low dimensional representation of a client’s personalized\r\nmodel parameters, allowing it to be transferred to and run on the client devices. In our final\r\npresented method, we change our focus and rather than aim to directly address the challenge\r\nof data heterogeneity, we instead ensure we are unaffected by it. This is done in the context\r\nof k-means clustering and we present a method for federated clustering with a focus on added\r\nprivacy guarantees." acknowledged_ssus: - _id: ScienComp acknowledgement: "This research was funded in part by the Austrian Science Fund (FWF)\r\n[10.55776/COE12]. Furthermore, the candidate acknowledges the support from the Scientific\r\nService Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp)." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Jonathan A full_name: Scott, Jonathan A id: e499926b-f6e0-11ea-865d-9c63db0031e8 last_name: Scott citation: ama: Scott JA. Data heterogeneity and personalization in federated learning. 2026. doi:10.15479/AT-ISTA-21198 apa: Scott, J. A. (2026). Data heterogeneity and personalization in federated learning. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-21198 chicago: Scott, Jonathan A. “Data Heterogeneity and Personalization in Federated Learning.” Institute of Science and Technology Austria, 2026. https://doi.org/10.15479/AT-ISTA-21198. ieee: J. A. Scott, “Data heterogeneity and personalization in federated learning,” Institute of Science and Technology Austria, 2026. ista: Scott JA. 2026. Data heterogeneity and personalization in federated learning. Institute of Science and Technology Austria. mla: Scott, Jonathan A. Data Heterogeneity and Personalization in Federated Learning. Institute of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-21198. short: J.A. Scott, Data Heterogeneity and Personalization in Federated Learning, Institute of Science and Technology Austria, 2026. corr_author: '1' date_created: 2026-02-09T14:59:53Z date_published: 2026-02-09T00:00:00Z date_updated: 2026-10-01T09:29:03Z day: '09' ddc: - '005' degree_awarded: PhD department: - _id: GradSch - _id: ChLa doi: 10.15479/AT-ISTA-21198 doi_confirm: '1' file: - access_level: closed checksum: 121c1d968bd86f3630aa7e81d5bbbcb0 content_type: application/zip creator: jscott date_created: 2026-02-17T11:46:22Z date_updated: 2026-02-17T11:46:22Z file_id: '21298' file_name: 2026_Scott_Jonathan_Thesis_Source.zip file_size: 272379252 relation: source_file - access_level: open_access checksum: 6e3e08ba474bbee8511cc8a839ab2077 content_type: application/pdf creator: jscott date_created: 2026-02-27T10:25:41Z date_updated: 2026-02-27T10:25:41Z file_id: '21366' file_name: 2026_Jonathan_Scott_Thesis.pdf file_size: 15220298 relation: main_file success: 1 file_date_updated: 2026-02-27T10:25:41Z fulldoi: https://doi.org/10.15479/AT-ISTA-21198 has_accepted_license: '1' language: - iso: eng month: '02' oa: 1 oa_version: Published Version page: '158' project: - _id: d8f03aaa-b035-11f1-8588-d5147fa879e0 grant_number: COE12 name: Bilateral Artificial Intelligence (Lampert) publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '21207' relation: part_of_dissertation status: public - id: '20819' relation: part_of_dissertation status: public - id: '17411' relation: part_of_dissertation status: public - id: '18120' relation: part_of_dissertation status: public status: public supervisor: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 title: Data heterogeneity and personalization in federated learning type: dissertation user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2026' ... --- OA_place: publisher _id: '21854' abstract: - lang: eng text: "As neural-network-based models grow both in size and popularity, interest has grown in making the models smaller and more efficient to train. To that end, many methods have been proposed to prune models by reducing their number of nonzero parameters. Additionally, parameter-efficient fine-tuning, in which a much smaller number of parameters than the total contained in the model is updated during training, has become very popular, especially in the space of Large Language Models. At the same time, the increasingly routine deployment of machine learning in real-world applications has spurred a drive to make them more trustworthy - in the sense of, among other things, being unbiased, interpretable, and editable. In this thesis, we examine the interplay between efficiency and trustworthiness.\r\n\r\nFirst, we analyze the effects of model pruning on bias in computer vision models, demonstrating that increased sparsity leads to greater bias, largely as a function of increased model uncertainty in marginal cases. Based on this observation, we propose several bias mitigation techniques. Then, we demonstrate that example-specific model pruning can improve model interpretation methods while improving pruning efficiency to make example-specific model pruning feasible in real time. Then, we investigate the effectiveness of parameter-efficient and data-efficient model personalization via fine-tuning, demonstrating that it is highly feasible with very small computational and data resources. Finally, we consider efficiency in editing model knowledge using a custom synthetic data framework, demonstrating that parameter-efficient, low-rank fine-tuning frequently outperforms full-rank fine-tuning, and, additionally, that restricting which model blocks are fine-tuned frequently improves results. Together, the results in this thesis provide new insights and techniques for combining trustworthiness and efficiency during neural network inference and training.\r\n\r\n" acknowledged_ssus: - _id: ScienComp acknowledgement: "The research in this Ph.D. was funded in whole\r\nor in part by the Austrian Science Fund (FWF) W1260-N35 (Vienna Graduate School for\r\nComputational Optimization). For open access purposes the author has applied a CC BY\r\npublic copyright license to any author accepted manuscript version arising from this submission\r\nwherever possible. Additionally, I am grateful to Alois Schlögl, Waleed Khalid, and the rest of\r\nthe ISTA Scientific Computing team for building and maintaining the infrastructure I used\r\nto run experiments. I’m also deeply grateful to the Alistarh group’s administrative assistant,\r\nChristine Francois, who always deals with our nonsense with common sense and a smile.\r\n" alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Eugenia B full_name: Iofinova, Eugenia B id: f9a17499-f6e0-11ea-865d-fdf9a3f77117 last_name: Iofinova orcid: 0000-0002-7778-3221 citation: ama: Iofinova EB. On the utility and effects of efficiency in artificial neural networks. 2026. doi:10.15479/AT-ISTA-21854 apa: Iofinova, E. B. (2026). On the utility and effects of efficiency in artificial neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-21854 chicago: Iofinova, Eugenia B. “On the Utility and Effects of Efficiency in Artificial Neural Networks.” Institute of Science and Technology Austria, 2026. https://doi.org/10.15479/AT-ISTA-21854. ieee: E. B. Iofinova, “On the utility and effects of efficiency in artificial neural networks,” Institute of Science and Technology Austria, 2026. ista: Iofinova EB. 2026. On the utility and effects of efficiency in artificial neural networks. Institute of Science and Technology Austria. mla: Iofinova, Eugenia B. On the Utility and Effects of Efficiency in Artificial Neural Networks. Institute of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-21854. short: E.B. Iofinova, On the Utility and Effects of Efficiency in Artificial Neural Networks, Institute of Science and Technology Austria, 2026. corr_author: '1' das_tickbox: '1' date_created: 2026-05-11T08:43:22Z date_published: 2026-05-11T00:00:00Z date_updated: 2026-10-01T09:32:47Z day: '11' ddc: - '000' degree_awarded: PhD department: - _id: GradSch - _id: DaAl doi: 10.15479/AT-ISTA-21854 doi_confirm: '1' file: - access_level: closed checksum: 2e148dad920e3f9b7c32796e0ba2e5f7 content_type: application/zip creator: eiofinov date_created: 2026-05-11T08:36:01Z date_updated: 2026-05-11T08:36:01Z file_id: '21856' file_name: EIofinova_thesis_FinalVersion.zip file_size: 28479571 relation: source_file - access_level: open_access checksum: b10c2933f386f532b2dbf28b19c5525c content_type: application/pdf creator: eiofinov date_created: 2026-05-13T13:10:48Z date_updated: 2026-05-13T13:10:48Z file_id: '21877' file_name: 2026_Iofinova_Eugenia_Thesis.pdf file_size: 18137757 relation: main_file success: 1 file_date_updated: 2026-05-13T13:10:48Z fulldoi: https://doi.org/10.15479/AT-ISTA-21854 has_accepted_license: '1' language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: '237' project: - _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A grant_number: W1260-N35 name: Vienna Graduate School on Computational Optimization publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria publisher_comment: In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of ISTA's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. If applicable, University Microfilms and/or ProQuest Library, or the Archives of Canada may supply single copies of the dissertation. related_material: record: - id: '14771' relation: part_of_dissertation status: public - id: '21858' relation: part_of_dissertation status: public - id: '21859' relation: part_of_dissertation status: public - id: '21857' relation: part_of_dissertation status: public - id: '18121' relation: part_of_dissertation status: public status: public supervisor: - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X title: On the utility and effects of efficiency in artificial neural networks type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2026' ... --- OA_place: publisher OA_type: hybrid PlanS_conform: '1' _id: '21982' abstract: - lang: eng text: A floating Leidenfrost droplet exhibits curvature inversion of its underside, due to the balance of vapor pressure and surface tension. Using interferometric imaging, we find different behavior for a levitated hydrogel sphere. Curvature inversion is observed briefly just after deposition, but quickly gives way to a steady state with no inversion. We show the essential role of vaporization in shaping the underbelly of the hydrogel, where changes due to direct mass loss are more significant than the balance of vapor pressure and elastic forces. acknowledged_ssus: - _id: M-Shop - _id: ScienComp acknowledgement: This research was supported by the Scientific Service Units of The Institute of Science and Technology Austria (ISTA) through resources provided by the Miba Machine Shop and the Scientific Computing Facility. J.B. acknowledges funding from the European Union's Horizon research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 101106500. article_number: L053502 article_processing_charge: Yes (via OA deal) article_type: letter_note arxiv: 1 author: - first_name: Vicente L full_name: Diaz Melian, Vicente L id: b6798902-eea0-11ea-9cbc-a8e14286c631 last_name: Diaz Melian - first_name: Isaac C full_name: Lenton, Isaac C id: a550210f-223c-11ec-8182-e2d45e817efb last_name: Lenton orcid: 0000-0002-5010-6984 - first_name: Jack full_name: Binysh, Jack last_name: Binysh - first_name: Anton full_name: Souslov, Anton last_name: Souslov - first_name: Scott R full_name: Waitukaitis, Scott R id: 3A1FFC16-F248-11E8-B48F-1D18A9856A87 last_name: Waitukaitis orcid: 0000-0002-2299-3176 citation: ama: Diaz Melian VL, Lenton IC, Binysh J, Souslov A, Waitukaitis SR. Geometry of the vapor layer under a Leidenfrost hydrogel sphere. Physical Review E. 2026;113(5). doi:10.1103/m7gr-2t6j apa: Diaz Melian, V. L., Lenton, I. C., Binysh, J., Souslov, A., & Waitukaitis, S. R. (2026). Geometry of the vapor layer under a Leidenfrost hydrogel sphere. Physical Review E. American Physical Society. https://doi.org/10.1103/m7gr-2t6j chicago: Diaz Melian, Vicente L, Isaac C Lenton, Jack Binysh, Anton Souslov, and Scott R Waitukaitis. “Geometry of the Vapor Layer under a Leidenfrost Hydrogel Sphere.” Physical Review E. American Physical Society, 2026. https://doi.org/10.1103/m7gr-2t6j. ieee: V. L. Diaz Melian, I. C. Lenton, J. Binysh, A. Souslov, and S. R. Waitukaitis, “Geometry of the vapor layer under a Leidenfrost hydrogel sphere,” Physical Review E, vol. 113, no. 5. American Physical Society, 2026. ista: Diaz Melian VL, Lenton IC, Binysh J, Souslov A, Waitukaitis SR. 2026. Geometry of the vapor layer under a Leidenfrost hydrogel sphere. Physical Review E. 113(5), L053502. mla: Diaz Melian, Vicente L., et al. “Geometry of the Vapor Layer under a Leidenfrost Hydrogel Sphere.” Physical Review E, vol. 113, no. 5, L053502, American Physical Society, 2026, doi:10.1103/m7gr-2t6j. short: V.L. Diaz Melian, I.C. Lenton, J. Binysh, A. Souslov, S.R. Waitukaitis, Physical Review E 113 (2026). corr_author: '1' date_created: 2026-06-10T07:36:41Z date_published: 2026-05-14T00:00:00Z date_updated: 2026-10-02T08:44:47Z day: '14' ddc: - '530' department: - _id: ScWa - _id: GradSch doi: 10.1103/m7gr-2t6j external_id: arxiv: - '2507.04982' file: - access_level: open_access checksum: 902cc8d177c8d3ae9cfe07c30375c9a9 content_type: application/pdf creator: dernst date_created: 2026-06-16T11:21:53Z date_updated: 2026-06-16T11:21:53Z file_id: '22014' file_name: 2026_PhysicalReviewE_DiazMelian.pdf file_size: 3173197 relation: main_file success: 1 file_date_updated: 2026-06-16T11:21:53Z fulldoi: https://doi.org/10.1103/m7gr-2t6j has_accepted_license: '1' intvolume: ' 113' issue: '5' language: - iso: eng month: '05' oa: 1 oa_version: Published Version publication: Physical Review E publication_identifier: eissn: - 2470-0053 issn: - 2470-0045 publication_status: published publisher: American Physical Society quality_controlled: '1' related_material: record: - id: '23005' relation: dissertation_contains status: public scopus_import: '1' status: public title: Geometry of the vapor layer under a Leidenfrost hydrogel sphere tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 113 year: '2026' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold PlanS_conform: '1' _id: '22972' abstract: - lang: eng text: Genomic measurements of DNA methylation, gene expression or protein levels are becoming more prevalent and are increasingly used to study health outcomes. However, most proposed association testing methods consider only marginal effects of each feature on a single outcome variable and are not set up to handle highly correlated, continuous data. Here, we introduce MAJA, a method to learn shared and outcome-specific effects for multiple traits in multi-omics data. MAJA determines the unique contribution of individual loci, genes, or molecular pathways to variation in one or more traits, conditional on all other measured “omics” data genome-wide. Simulations show MAJA accurately finds shared and distinct associations between omics-data and multiple traits and estimates omics-specific (co)variances, allowing for sparsity and correlations within the data. Applying MAJA to 12 outcome traits in Generation Scotland methylation data (n = 18 264), we find novel shared epigenetic probes among cholesterol metabolism, osteoarthritis, blood pressure and asthma. In contrast to marginal testing, we find only 10 CpG probes with significant effects above the genome-wide background. This highlights the need for joint association testing in highly correlated methylation data from whole blood and for studies of increased sample size in order to refine epigenomic associations in observational data. acknowledged_ssus: - _id: ScienComp acknowledgement: 'We thank members of the Medical Genomics group at ISTA for their comments, which improved this manuscript. This work was funded by an SNSF Eccellenza Grant to MRR [PCEGP3-181181], and by core funding from the Institute of Science and Technology Austria. We would like to acknowledge the participants and investigators of the Generation Scotland and Lothian Birth Cohort studies. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Genotyping and methylation typing of the GS: SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and Depression Longitudinally” (STRADL) Reference 104036/Z/14/Z]. DNA methylation data for Generation Scotland was also funded by a 2018 NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation [Ref: 27404; awardee: Dr David M Howard] and by a John, Margaret, Alfred and Stewart Sim Fellowship from the Royal College of Physicians of Edinburgh (Awardee: Dr Heather C Whalley). The LBC1936 is supported by the Biotechnology and Biological Sciences Research Council, and the Economic and Social Research Council [BB/W008793/1] (which supports SEH, and JC), Age UK (Disconnected Mind project), the Milton Damerel Trust, the Medical Research Council [G0701120, G1001245, MR/M013111/1, MR/R024065/1] and the University of Edinburgh. Methylation typing of LBC1936 was supported by the Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, The Wellcome Trust Institutional Strategic Support Fund, The University of Edinburgh, and The University of Queensland. HS is supported by funding from the Wellcome Trust 4-year PhD in Translational Neuroscience [218493/Z/19/Z]. SRC is also supported by a Sir Henry Dale Fellowship jointly funded by Wellcome and the Royal Society [221890/Z/20/Z]. High-performance computing was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp).' article_number: vbag231 article_processing_charge: Yes article_type: original author: - first_name: Ilse full_name: Krätschmer, Ilse id: 30d4014e-7753-11eb-b44b-db6d61112e73 last_name: Krätschmer orcid: 0000-0002-5636-9259 - first_name: Hannah M full_name: Smith, Hannah M last_name: Smith - first_name: Daniel L full_name: McCartney, Daniel L last_name: McCartney - first_name: Elena full_name: Bernabeu, Elena last_name: Bernabeu - first_name: Mahdi full_name: Mahmoudi, Mahdi last_name: Mahmoudi - first_name: Archie full_name: Campbell, Archie last_name: Campbell - first_name: Janie full_name: Corley, Janie last_name: Corley - first_name: Sarah E full_name: Harris, Sarah E last_name: Harris - first_name: Simon R full_name: Cox, Simon R last_name: Cox - first_name: Riccardo E full_name: Marioni, Riccardo E last_name: Marioni - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: 'Krätschmer I, Smith HM, McCartney DL, et al. MAJA: Multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes. Bioinformatics Advances. 2026;6(1). doi:10.1093/bioadv/vbag231' apa: 'Krätschmer, I., Smith, H. M., McCartney, D. L., Bernabeu, E., Mahmoudi, M., Campbell, A., … Robinson, M. R. (2026). MAJA: Multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes. Bioinformatics Advances. Oxford University Press. https://doi.org/10.1093/bioadv/vbag231' chicago: 'Krätschmer, Ilse, Hannah M Smith, Daniel L McCartney, Elena Bernabeu, Mahdi Mahmoudi, Archie Campbell, Janie Corley, et al. “MAJA: Multivariate Bayesian Model for Discovery of Shared Epigenetic Pathways across Human Phenotypes.” Bioinformatics Advances. Oxford University Press, 2026. https://doi.org/10.1093/bioadv/vbag231.' ieee: 'I. Krätschmer et al., “MAJA: Multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes,” Bioinformatics Advances, vol. 6, no. 1. Oxford University Press, 2026.' ista: 'Krätschmer I, Smith HM, McCartney DL, Bernabeu E, Mahmoudi M, Campbell A, Corley J, Harris SE, Cox SR, Marioni RE, Robinson MR. 2026. MAJA: Multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes. Bioinformatics Advances. 6(1), vbag231.' mla: 'Krätschmer, Ilse, et al. “MAJA: Multivariate Bayesian Model for Discovery of Shared Epigenetic Pathways across Human Phenotypes.” Bioinformatics Advances, vol. 6, no. 1, vbag231, Oxford University Press, 2026, doi:10.1093/bioadv/vbag231.' short: I. Krätschmer, H.M. Smith, D.L. McCartney, E. Bernabeu, M. Mahmoudi, A. Campbell, J. Corley, S.E. Harris, S.R. Cox, R.E. Marioni, M.R. Robinson, Bioinformatics Advances 6 (2026). corr_author: '1' das_tickbox: '1' dataavailabilitystatement: "Access to the data is available with appropriate permission from the Generation Scotland Access Committee. Applications should be made to access@generationscotland.org. https://www.ed.ac.uk/lothian-birth-cohorts/data-access-collaboration gives information on the Lothian Birth Cohort data, which are available on request from the Lothian Birth Cohort Study, University of Edinburgh. Data from both cohorts are not publicly available as they contain information that could compromise participant consent and confidentiality.\r\n\r\nSource code is available at https://github.com/medical-genomics-group/MAJA." date_created: 2026-09-22T05:41:01Z date_published: 2026-09-01T00:00:00Z date_updated: 2026-10-06T12:50:50Z day: '01' ddc: - '570' department: - _id: MaRo doi: 10.1093/bioadv/vbag231 external_id: pmid: - '42761413' file: - access_level: open_access checksum: 86849f1b88c47d050be4aa0a40105a97 content_type: application/pdf creator: dernst date_created: 2026-10-06T12:49:18Z date_updated: 2026-10-06T12:49:18Z file_id: '23064' file_name: 2026_BioinformaticsAdvances_Kraetschmer.pdf file_size: 1370368 relation: main_file success: 1 file_date_updated: 2026-10-06T12:49:18Z fulldoi: https://doi.org/10.1093/bioadv/vbag231 has_accepted_license: '1' intvolume: ' 6' issue: '1' language: - iso: eng month: '09' oa: 1 oa_version: Published Version pmid: 1 publication: Bioinformatics Advances publication_identifier: eissn: - 2635-0041 publication_status: published publisher: Oxford University Press quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: yes title: 'MAJA: Multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 6 year: '2026' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '23089' abstract: - lang: eng text: The location of the hydrogen-helium (H/He) immiscibility boundary controls whether and where helium rain occurs in giant planets, yet it remains uncertain because high-pressure experiments are challenging and ab initio simulations are limited in system size and simulation time. We map this boundary by computing composition-dependent chemical potentials from large-scale molecular dynamics driven by machine learning potentials trained on three density functional approximations (PBE, vdW-DF, and the hybrid HSE). Across the pressure range of 100-1000 GPa, the demixing temperatures are typically ~ 2000 K lower than previous ab initio simulations using small system sizes, and this discrepancy is larger than the estimated error from the chosen density functionals, machine learning potentials, neglect of nuclear quantum effects, and statistical uncertainties. Fitting the H/He mixing free energy to a Redlich-Kister regular solution model rationalizes the thermodynamic driving force for phase separation, and provides a predictive representation of the boundary. Comparing with current planetary interior profiles indicates that helium rain is plausible in Saturn but unlikely in the warmer interior of Jupiter. Our results narrow the uncertainty in the H/He immiscibility boundary and provide inputs for planetary models that couple demixing, heat transport, and composition gradients in gas giants. acknowledged_ssus: - _id: ScienComp acknowledgement: The authors are thankful to Armin Bergermann and Ronald Redmer for generously sharing AIMD trajectories and data, and for valuable discussions and feedback on the manuscript. The authors also acknowledge the research computing facilities provided by the high-performance computing (HPC) cluster at the Institute of Science and Technology Austria (ISTA), Berkeley Research Computing (BRC) at the University of California, Berkeley (UCB), and Lawrence Livermore National Laboratory (LLNL). B.C. was supported by the Laboratory Directed Research and Development (LDRD) Program at Lawrence Berkeley National Laboratory (LBNL) under project ID 110990-001. Part of this work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344. S.H. was supported by the LDRD Program at LLNL under project tracking code 23-SI-006. article_number: '10382' article_processing_charge: Yes article_type: original author: - first_name: Xiaoyu full_name: Wang, Xiaoyu id: 8dff9c62-32b0-11ee-9fa8-fc73025e10f3 last_name: Wang - first_name: Sebastien full_name: Hamel, Sebastien last_name: Hamel - first_name: Bingqing full_name: Cheng, Bingqing id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9 last_name: Cheng orcid: 0000-0002-3584-9632 citation: ama: Wang X, Hamel S, Cheng B. Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics. Nature Communications. 2026;17. doi:10.1038/s41467-026-77117-8 apa: Wang, X., Hamel, S., & Cheng, B. (2026). Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-026-77117-8 chicago: Wang, Xiaoyu, Sebastien Hamel, and Bingqing Cheng. “Hydrogen-Helium Immiscibility Boundary in Gas-Giant Planetary Interiors from Machine-Learning Molecular Dynamics.” Nature Communications. Springer Nature, 2026. https://doi.org/10.1038/s41467-026-77117-8. ieee: X. Wang, S. Hamel, and B. Cheng, “Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics,” Nature Communications, vol. 17. Springer Nature, 2026. ista: Wang X, Hamel S, Cheng B. 2026. Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics. Nature Communications. 17, 10382. mla: Wang, Xiaoyu, et al. “Hydrogen-Helium Immiscibility Boundary in Gas-Giant Planetary Interiors from Machine-Learning Molecular Dynamics.” Nature Communications, vol. 17, 10382, Springer Nature, 2026, doi:10.1038/s41467-026-77117-8. short: X. Wang, S. Hamel, B. Cheng, Nature Communications 17 (2026). corr_author: '1' das_tickbox: '1' dataavailabilitystatement: 'The training data, fitted MLPs, MLP molecular dynamics results, and source data underlying all figures and tables in the main text and Supplementary Information have been deposited in the Zenodo repository: https://doi.org/10.5281/zenodo.21632034. The data-analysis and plotting scripts used to generate the figures and tables in the main text and Supplementary Information are available in the GitHub repository: https://github.com/ChengUCB/HighPressure_HHe.' date_created: 2026-10-11T22:02:04Z date_published: 2026-09-01T00:00:00Z date_updated: 2026-10-12T11:00:16Z day: '01' ddc: - '540' department: - _id: GradSch - _id: BiCh doi: 10.1038/s41467-026-77117-8 external_id: pmid: - '42816492' file: - access_level: open_access checksum: f39b50a550623a65aea2405b527e068c content_type: application/pdf creator: dernst date_created: 2026-10-12T10:57:42Z date_updated: 2026-10-12T10:57:42Z file_id: '23107' file_name: 2026_NatureComm_Wang.pdf file_size: 1559219 relation: main_file success: 1 file_date_updated: 2026-10-12T10:57:42Z fulldoi: https://doi.org/10.1038/s41467-026-77117-8 has_accepted_license: '1' intvolume: ' 17' language: - iso: eng month: '09' oa: 1 oa_version: Published Version pmid: 1 publication: Nature Communications publication_identifier: eissn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: yes title: Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 17 year: '2026' ... --- OA_place: publisher _id: '23072' abstract: - lang: eng text: "Inferring signal from large quantities of data is one of the most important problems in today’s\r\nworld, with neural networks emerging as the most powerful solution. While there have been\r\nmassive breakthroughs in the ML space, there are two main drawbacks. The first is that\r\nmost of these models are “black boxes”, with very little understanding of why they work,\r\nwhich makes it hard to find the correct architecture for a given problem. The architecture is\r\ncrucial, as the widely used training algorithms are “local” and thus rely on the loss landscape\r\nbeing benign enough to find a global optimum. This brings us to the second problem: while\r\nthere is a lot of evidence that this can be overcome with large enough data, this is less clear\r\nfor datasets which are noisy, do not have enough samples, or have a non-standard structure.\r\nHistorically, on a smaller scale, the alternative has been simple (Bayesian) statistical methods,\r\nwhich also have the advantage of being interpretable and adaptable. However, many of the\r\nclassical results cannot be easily extended to the high-dimensional regime in which modern\r\ndata lives.\r\nIn this work we want to consider two different settings relating to the above. The first is\r\napproximate message passing (AMP), an iterative algorithm for high-dimensional (generalized)\r\nlinear models that easily lends itself to Bayesian setups, and which in many cases is provably\r\nBayes-optimal. The second is an autoencoder with a shallow encoder, for which we can\r\nanalyze the weights along the training trajectory of gradient descent. Surprisingly, we will find\r\na connection between the two, as we recover certain structures of AMP.\r\nWe will first introduce the AMP framework by studying an extension that can be applied\r\nto a wider variety of data: rotationally invariant generalized approximate message passing\r\n(RI-GAMP). This shows that one can extend the AMP framework to a much wider class of\r\nalgorithms, relevant to more practical settings. The results are then also confirmed on real\r\ndata that go beyond the theoretical setting.\r\nWe then move to the setup of an autoencoder with a shallow encoder and a Bayesian prior in\r\nthe high-dimensional regime, in which the input and compression dimensions are large and\r\nproportional; this is also a first step towards opening one of the black boxes above, as we\r\ncan say precisely when and why the network works. While autoencoders are widely used in\r\nmodern ML architectures, most fundamental research in this direction has focused on extreme\r\nasymptotic regimes, where the autoencoder is either severely over- or under-parameterized,\r\nand which do not extend to the proportional high-dimensional regime. We show that for\r\nGaussian priors the network finds the global optimum, and these theoretical findings extrapolate\r\nstrikingly well to real image data. Next we extend this analysis to more structured priors and\r\na deeper decoder. For sparse Gaussian inputs in the training regime studied here, the shallow\r\ndecoder does not exploit sparsity and yields the same dynamics as in the Gaussian setting.\r\nWe then show how a deeper decoder can break this behavior, and interestingly, if we treat the\r\nproblem as an AMP problem, the network eventually learns the same decoding as AMP. This\r\ntoo extrapolates to real image data.\r\nLastly, we will show that the AMP setup has real practical use, by using a practical variant,\r\ngenetic vector approximate message passing (gVAMP), for predicting plasma protein levels\r\nfrom genetic information using the UKB GWAS data. This is a perfect example of the problems\r\nin the high-dimensional regime: approximately 8.4 million dimensions and 40,000 samples,\r\nwhere the high noise, low sample count, and large size make it hard for ML methods to work.\r\nTo get the optimal prediction accuracy we consider a two-step inference. We first consider a\r\npre-processed version of the data, of about 880,000 dimensions, on which we run the gVAMP\r\nalgorithm; this allows us to find predictive regions, taking into account joint effects between\r\ndifferent regions, and for most proteins we end up selecting only about 0.1% of the dimensions.\r\nWe then consider the analogue of this small subset, on which we can now run a standard tree\r\nalgorithm (XGB) that no longer suffers from the aforementioned problems. This pipeline yields\r\nbetter prediction accuracy than recent nonlinear deep learning approaches applied to this data,\r\nas we are able to handle the pre-processing in a principled Bayesian way." acknowledged_ssus: - _id: ScienComp acknowledgement: "This project was partially supported by the 2019 Lopez-Loreta Prize and\r\nthe ERC INF2 project (project number 101161364). Furthermore, the candidate acknowledges\r\nthe support from the Scientific Service Units of the Institute of Science and Technology Austria\r\nthrough resources provided by Scientific Computing." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Kevin full_name: Kögler, Kevin id: 94ec913c-dc85-11ea-9058-e5051ab2428b last_name: Kögler citation: ama: 'Kögler K. Compression and inference in high dimensions: An approximate message passing perspective. 2026. doi:10.15479/AT-ISTA-23072' apa: 'Kögler, K. (2026). Compression and inference in high dimensions: An approximate message passing perspective. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-23072' chicago: 'Kögler, Kevin. “Compression and Inference in High Dimensions: An Approximate Message Passing Perspective.” Institute of Science and Technology Austria, 2026. https://doi.org/10.15479/AT-ISTA-23072.' ieee: 'K. Kögler, “Compression and inference in high dimensions: An approximate message passing perspective,” Institute of Science and Technology Austria, 2026.' ista: 'Kögler K. 2026. Compression and inference in high dimensions: An approximate message passing perspective. Institute of Science and Technology Austria.' mla: 'Kögler, Kevin. Compression and Inference in High Dimensions: An Approximate Message Passing Perspective. Institute of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-23072.' short: 'K. Kögler, Compression and Inference in High Dimensions: An Approximate Message Passing Perspective, Institute of Science and Technology Austria, 2026.' corr_author: '1' date_created: 2026-10-07T11:20:26Z date_published: 2026-10-08T00:00:00Z date_updated: 2026-10-12T13:04:49Z day: '08' ddc: - '519' degree_awarded: PhD department: - _id: MaMo - _id: GradSch doi: 10.15479/AT-ISTA-23072 doi_confirm: '1' file: - access_level: closed checksum: 5fb2ff02dc9d8c659b2fb5e0802b06ff content_type: application/zip creator: kkoegler date_created: 2026-10-09T11:20:09Z date_updated: 2026-10-09T12:22:55Z file_id: '23084' file_name: 2026_Koegler_Kevin_Thesis.zip file_size: 8504109 relation: source_file - access_level: open_access checksum: a6df3b04c97628a1703f63356fb56f96 content_type: application/pdf creator: kkoegler date_created: 2026-10-09T13:25:49Z date_updated: 2026-10-09T13:25:49Z file_id: '23085' file_name: 2026_Koegler_Kevin_Thesis.pdf file_size: 6692605 relation: main_file success: 1 file_date_updated: 2026-10-09T13:25:49Z fulldoi: https://doi.org/10.15479/AT-ISTA-23072 has_accepted_license: '1' language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: '222' project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '14459' relation: part_of_dissertation status: public - id: '12540' relation: part_of_dissertation status: public - id: '17469' relation: part_of_dissertation status: public status: public supervisor: - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 title: 'Compression and inference in high dimensions: An approximate message passing perspective' type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2026' ... --- OA_place: publisher _id: '22857' abstract: - lang: eng text: "Artificial intelligence and machine learning have undergone an unprecedented evolution in the past decade, motivating a research effort toward a theory able to capture the qualitative behavior of large-scale neural systems. A central puzzle has been the clear benefit of scaling architecture size and overfitting the training set in supervised learning tasks. This evidence, in apparent contradiction with classical statistical learning theory, pushed researchers to develop a new theory capturing the interplay between the algorithmic and architectural bias of training and the specific target function, differently from previous methods rooted in uniform stability.\r\nThis approach has enabled a grounded understanding of novel learning regimes, typically through formal limits where the number of training samples $n$, data dimensions $d$, and model parameters $p$ grow to infinity at different rates. \\\\\r\nIn this thesis, we follow this approach, focusing on the trustworthiness of high-dimensional models: properties that are difficult to control during training or deployment and often emerge under unpredictable or adversarial conditions. In such settings, it is crucial to formally ensure a priori the reliability of machine learning systems.\r\nFirst, we study data memorization, both as label fitting and as the storage of private information about training samples in trained parameters. We prove that $p = \\Omega(n)$ parameters are sufficient for a deep neural network to memorize a generic set of labels, and for a model to memorize spurious features across training data. We then give evidence that $p = \\Omega(dn)$ parameters are instead necessary for an adversary to reconstruct the full training set from the trained parameters.\r\nSecond, we study robustness, both to adversarial perturbations and to distribution shift. We first prove that $p = \\Omega(dn)$ parameters can be sufficient for a class of neural networks to overfit the training data while guaranteeing robustness to adversarial perturbations. Then, we focus on spurious correlations learning in high-dimensional regression, studying the effect of the ridge regularization parameter in the proportional regime $n = \\Theta(d)$, and connecting it via an equivalence argument to the role of over-parameterization $p = \\Omega(n)$ in neural networks. We also investigate the architectural bias of attention-based networks, showing that they are sensitive to the replacement of individual words in an embedded sentence, allowing them to generalize on sentences where the contextual meaning depends on one or few words.\r\nFinally, we study differentially private optimization in high-dimensional regimes. We prove that standard private gradient methods do not suffer in the over-parameterized regime $p = \\Omega(n)$, challenging the current wisdom based on stability-derived generalization bounds. We then consider linear regression in the proportional regime $n = \\Theta(d)$, showing that standard private gradient descent can achieve optimal rates under appropriate hyper-parameter scaling, such as sufficiently small gradient clipping constants, whose role is still debated in practice." acknowledged_ssus: - _id: ScienComp acknowledgement: "This project was partially supported by the 2019 Lopez-Loreta prize,\r\nthe European Union (ERC, INF2\r\n, project number 101161364), the Austrian Science Fund\r\n(FWF) 10.55776/COE12, and a Google PhD fellowship in machine intelligence. Furthermore,\r\nthe candidate acknowledges the support from the Scientific Service Units of the Institute of\r\nScience and Technology Austria through resources provided by Scientific Computing." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Simone full_name: Bombari, Simone id: ca726dda-de17-11ea-bc14-f9da834f63aa last_name: Bombari citation: ama: Bombari S. Trustworthy machine learning in high dimensions. 2026. doi:10.15479/AT-ISTA-22857 apa: Bombari, S. (2026). Trustworthy machine learning in high dimensions. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-22857 chicago: Bombari, Simone. “Trustworthy Machine Learning in High Dimensions.” Institute of Science and Technology Austria, 2026. https://doi.org/10.15479/AT-ISTA-22857. ieee: S. Bombari, “Trustworthy machine learning in high dimensions,” Institute of Science and Technology Austria, 2026. ista: Bombari S. 2026. Trustworthy machine learning in high dimensions. Institute of Science and Technology Austria. mla: Bombari, Simone. Trustworthy Machine Learning in High Dimensions. Institute of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-22857. short: S. Bombari, Trustworthy Machine Learning in High Dimensions, Institute of Science and Technology Austria, 2026. corr_author: '1' das_tickbox: '0' date_created: 2026-09-08T13:40:08Z date_published: 2026-09-08T00:00:00Z date_updated: 2026-10-16T10:42:36Z day: '08' ddc: - '519' degree_awarded: PhD department: - _id: GradSch - _id: MaMo doi: 10.15479/AT-ISTA-22857 doi_confirm: '1' file: - access_level: closed checksum: 8eab99d6dc6e4476826bdfc7c00fdebb content_type: application/zip creator: sbombari date_created: 2026-09-08T13:28:38Z date_updated: 2026-09-08T13:28:38Z file_id: '22859' file_name: Thesis copy.zip file_size: 99154325 relation: source_file - access_level: open_access checksum: 007033bafe4622ff4c2e758301354df1 content_type: application/pdf creator: sbombari date_created: 2026-09-10T10:03:48Z date_updated: 2026-09-10T10:03:48Z file_id: '22898' file_name: 2026_Bombari_Simone_Thesis.pdf file_size: 12856211 relation: main_file success: 1 file_date_updated: 2026-09-10T10:03:48Z fulldoi: https://doi.org/10.15479/AT-ISTA-22857 has_accepted_license: '1' keyword: - machine learning - high-dimensional statistics - deep learning theory - privacy - memorization - robustness language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: '446' project: - _id: 92099302-16d5-11f0-9cad-f9a785f54fbd name: 'Trustworthy Deep Learning Theory: Private Over-Parameterized Models and Robust LLMs' - _id: 911e6d1f-16d5-11f0-9cad-c5c68c6a1cdf grant_number: '101161364' name: 'Inference in High Dimensions: Light-speed Algorithms and Information Limits' - _id: 74caaef7-b034-11f1-8f2d-e0e993bb422e grant_number: COE12 name: Bilateral Artificial Intelligence (Mondelli) publication_identifier: isbn: - 978-3-99078-091-6 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '12537' relation: part_of_dissertation status: public - id: '21324' relation: part_of_dissertation status: public - id: '22894' relation: part_of_dissertation status: public - id: '19627' relation: part_of_dissertation status: public - id: '12859' relation: part_of_dissertation status: public - id: '18973' relation: part_of_dissertation status: public - id: '18972' relation: part_of_dissertation status: public researchdata_availability: no status: public supervisor: - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 supplementarymaterial: no title: Trustworthy machine learning in high dimensions type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2026' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '23043' abstract: - lang: eng text: Mendelian randomization, the standard instrument variable causal-inference approach for genomics, can be severely biased by inadequate selection of appropriate instrument variables and horizontal pleiotropy. We introduce Causal Inference GWAS, a graphical approach that selects genetic instruments from summary statistics controlling for linkage and pleiotropy, accommodates rare variants and binary outcomes, distinguishes direct from indirect risk factors in high dimensions, and flags latent confounding. Applied to 9 risk factors, 4 metabolic disease outcomes, and 8.4M variants across 458,747 UK Biobank individuals, our method runs in 20 minutes, identifying only 696 genome-wide valid instruments. We replicate nearly all previously reported risk factor-outcome paths, but find that most are indistinguishable from unmeasured confounding. This suggests robust biobank causal inference will require longitudinal and family data to resolve temporal precedence from reverse causation. Our approach offers a principled first step toward screening genuine causal signal from phenotypic correlation in biobank data. acknowledged_ssus: - _id: ScienComp acknowledgement: "We thank Zoltan Kutalik andmembers of the Robinson group at ISTA for\r\ntheir comments, which improved this manuscript. We would like to\r\nacknowledge the participants and investigators of the UK Biobank study.\r\nHigh-performance computing was supported by the Scientific Service\r\nUnits (SSU) of IST Austria through resources provided by Scientific\r\nComputing (SciComp). This work was funded by a research collaboration agreement between\r\nBoehringer Ingelheim and the research group of MRR at the Institute of\r\nScience and Technology Austria. Additional funding was also provided\r\nby an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core\r\nfunding from the Institute of Science and Technology Austria. MB was\r\nfunded by the National Science Center Poland grant SONATINA UMO-\r\n2021/40/C/NZ2/00218." article_number: '10153 ' article_processing_charge: Yes article_type: original author: - first_name: Nick N full_name: Machnik, Nick N id: 3591A0AA-F248-11E8-B48F-1D18A9856A87 last_name: Machnik orcid: 0000-0001-6617-9742 - first_name: Seyed Mahdi full_name: Mahmoudi, Seyed Mahdi id: b9f6d5ef-7774-11eb-a47f-df2c75c02ee7 last_name: Mahmoudi - first_name: Malgorzata full_name: Borczyk, Malgorzata last_name: Borczyk - first_name: Ilse full_name: Krätschmer, Ilse id: 30d4014e-7753-11eb-b44b-db6d61112e73 last_name: Krätschmer orcid: 0000-0002-5636-9259 - first_name: Markus J. full_name: Bauer, Markus J. last_name: Bauer - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. Causal inference for multiple risk factors and diseases from genomics data. Nature Communications. 2026;17. doi:10.1038/s41467-026-76877-7 apa: Machnik, N. N., Mahmoudi, S. M., Borczyk, M., Krätschmer, I., Bauer, M. J., & Robinson, M. R. (2026). Causal inference for multiple risk factors and diseases from genomics data. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-026-76877-7 chicago: Machnik, Nick N, Seyed Mahdi Mahmoudi, Malgorzata Borczyk, Ilse Krätschmer, Markus J. Bauer, and Matthew Richard Robinson. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” Nature Communications. Springer Nature, 2026. https://doi.org/10.1038/s41467-026-76877-7. ieee: N. N. Machnik, S. M. Mahmoudi, M. Borczyk, I. Krätschmer, M. J. Bauer, and M. R. Robinson, “Causal inference for multiple risk factors and diseases from genomics data,” Nature Communications, vol. 17. Springer Nature, 2026. ista: Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. 2026. Causal inference for multiple risk factors and diseases from genomics data. Nature Communications. 17, 10153. mla: Machnik, Nick N., et al. “Causal Inference for Multiple Risk Factors and Diseases from Genomics Data.” Nature Communications, vol. 17, 10153, Springer Nature, 2026, doi:10.1038/s41467-026-76877-7. short: N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R. Robinson, Nature Communications 17 (2026). corr_author: '1' das_tickbox: '1' dataavailabilitystatement: "This project uses the UK Biobank data under project number 35520.\r\nUK Biobank genotypic and phenotypic data is available through a\r\nformal request at (http://www.ukbiobank.ac.uk). All of the data generated\r\nin this study are provided in the Source Data file. Source data\r\nare provided with this paper. The CI-GWAS code is fully open source and available at https://github.\r\ncom/medical-genomics-group/ci-gwasand has been archived on\r\nZenodo52. The scripts used to execute the model are available at\r\nhttps://github.com/medical-genomics-group/ci-gwas. R version 4.2.1 is\r\navailable at https://www.r-project.org/. Plink version 1.9 is available at\r\nhttps://www.cog-genomics.org/plink/1.9/." date_created: 2026-10-04T22:01:53Z date_published: 2026-08-25T00:00:00Z date_updated: 2026-10-16T11:01:16Z day: '25' ddc: - '570' department: - _id: MaRo doi: 10.1038/s41467-026-76877-7 file: - access_level: open_access checksum: 05517674168a275c70f18e964ce33d8b content_type: application/pdf creator: dernst date_created: 2026-10-07T09:50:18Z date_updated: 2026-10-07T09:50:18Z file_id: '23070' file_name: 2026_NatureComm_Machnik.pdf file_size: 2345578 relation: main_file success: 1 file_date_updated: 2026-10-07T09:50:18Z fulldoi: https://doi.org/10.1038/s41467-026-76877-7 has_accepted_license: '1' intvolume: ' 17' language: - iso: eng month: '08' oa: 1 oa_version: Published Version publication: Nature Communications publication_identifier: eissn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '18648' relation: earlier_version status: public researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: no title: Causal inference for multiple risk factors and diseases from genomics data tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 17 year: '2026' ... --- OA_place: repository _id: '21423' abstract: - lang: eng text: "Biological systems, such as cytoskeletal filaments and multicellular collectives, exhibit rich \r\nself-organization driven by their intrinsic activity and physical constraints. Across scales, \r\ngeometry plays a central role: the shape of active units and the structure of their environment \r\nstrongly influence how order emerges in these far-from-equilibrium systems. In this thesis, \r\nwe combine minimal active-matter models with bottom-up in vitro experiments to identify \r\nthe principles by which geometry and activity shape collective behavior in biological matter. \r\nAt the subcellular scale, we investigate how the physical properties of chiral FtsZ filaments \r\ngovern their large-scale organization. Using a mesoscopic model validated by reconstituted \r\nfilament assays, we show that filament shape, intrinsic curvature, and flexibility, together with \r\nactivity-driven straightening, determine whether filaments form chiral rings, polar bands, or \r\nnematic-like states. Our model shows that small changes in filament mechanics produce large \r\nshifts in collective structures, revealing how the geometry of individual filaments amplifies \r\nthrough activity to control patterns relevant for bacterial cytokinesis. \r\nAt the cellular scale, we examine how geometry of the external environment influences \r\ncollective cancer cell migration. Experiments in controlled microfabricated obstacle \r\nenvironments demonstrate that geometric disorder alone can induce a transition from \r\ncollective to single-cell invasion. A minimal model of active particles in disordered geometries \r\nreproduces these effects, demonstrating that cell dissemination results both from local \r\ngeometrical constraints and a global integration of spatial disorder over time. Surprisingly, \r\nthis transition is accompanied by a change in the universality class of interface dynamics, \r\nrevealing that cell-environment interactions can shift the collective behavior of an invasive \r\nfront purely through mechanics. \r\nIn the last system, we study how cell-intrinsic geometry and chirality affect collective motion \r\nin confined and unconfined settings. Using a minimal model inspired by in vitro experiments, \r\nwe analyze motile cells that exhibit persistent rotation due to intrinsic left-right asymmetry. \r\nIn circular confinement, this simplified chiral model reproduces several experimentally \r\nobserved features within specific parameter regimes. At high densities in unconfined \r\nenvironments, however, intrinsic chirality becomes masked even before the onset of the \r\njamming transition. These results show that single-cell geometric asymmetry does not \r\nnecessarily translate into large-scale chiral flows, offering new insight into how forces and \r\nsymmetries shape collective motion in dense cellular systems. \r\nOverall, this work uncovers how geometric constraints – embedded in the intrinsic \r\narchitecture of an active filament or encoded in the complexity of a microenvironment \r\ncouple to active forces to generate different classes of complex behaviors. " acknowledged_ssus: - _id: ScienComp acknowledgement: "Finally, I gratefully acknowledge funding from the DOC Fellowship of the Austrian Academy\r\nof Sciences (OeAW): grant agreement 26360." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Zuzana full_name: Dunajova, Zuzana id: 4B39F286-F248-11E8-B48F-1D18A9856A87 last_name: Dunajova citation: ama: Dunajova Z. Geometry-driven self-organization of migrating cells and chiral filaments. 2026. doi:10.15479/AT-ISTA-21423 apa: Dunajova, Z. (2026). Geometry-driven self-organization of migrating cells and chiral filaments. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-21423 chicago: Dunajova, Zuzana. “Geometry-Driven Self-Organization of Migrating Cells and Chiral Filaments.” Institute of Science and Technology Austria, 2026. https://doi.org/10.15479/AT-ISTA-21423. ieee: Z. Dunajova, “Geometry-driven self-organization of migrating cells and chiral filaments,” Institute of Science and Technology Austria, 2026. ista: Dunajova Z. 2026. Geometry-driven self-organization of migrating cells and chiral filaments. Institute of Science and Technology Austria. mla: Dunajova, Zuzana. Geometry-Driven Self-Organization of Migrating Cells and Chiral Filaments. Institute of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-21423. short: Z. Dunajova, Geometry-Driven Self-Organization of Migrating Cells and Chiral Filaments, Institute of Science and Technology Austria, 2026. corr_author: '1' date_created: 2026-03-11T08:30:49Z date_published: 2026-03-11T00:00:00Z date_updated: 2026-10-16T08:12:11Z day: '11' ddc: - '539' - '570' degree_awarded: PhD department: - _id: GradSch - _id: EdHa doi: 10.15479/AT-ISTA-21423 doi_confirm: '1' file: - access_level: open_access checksum: 47ce6a48a0c63f28eca6e64c9ffd2c84 content_type: application/pdf creator: zdunajov date_created: 2026-03-12T20:38:52Z date_updated: 2026-09-11T22:30:02Z embargo: 2026-09-11 file_id: '21446' file_name: 2026_Dunajova_Zuzana_Thesis_pdfA.pdf file_size: 14662770 relation: main_file - access_level: closed checksum: 5dec5afdffd47c2b0b162d0fe1bed925 content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: zdunajov date_created: 2026-03-12T20:40:18Z date_updated: 2026-09-11T22:30:02Z embargo_to: open_access file_id: '21447' file_name: Thesis-Dunajova_source_file.docx file_size: 32961408 relation: source_file file_date_updated: 2026-09-11T22:30:02Z fulldoi: https://doi.org/10.15479/AT-ISTA-21423 has_accepted_license: '1' language: - iso: eng month: '03' oa: 1 oa_version: Published Version page: '110' project: - _id: 34d75525-11ca-11ed-8bc3-89b6307fee9d grant_number: '26360' name: Motile active matter models of migrating cells and chiral filaments publication_identifier: isbn: - 978-3-99078-076-3 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '13314' relation: part_of_dissertation status: public - id: '13116' relation: research_data status: public - id: '21439' relation: research_data status: public - id: '21427' relation: part_of_dissertation status: public status: public supervisor: - first_name: Edouard B full_name: Hannezo, Edouard B id: 3A9DB764-F248-11E8-B48F-1D18A9856A87 last_name: Hannezo orcid: 0000-0001-6005-1561 title: Geometry-driven self-organization of migrating cells and chiral filaments tmp: image: /images/cc_by_nc_sa.png legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) short: CC BY-NC-SA (4.0) type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2026' ... --- OA_place: repository OA_type: free access _id: '21439' abstract: - lang: eng text: These files contain supplementary movies accompanying the PhD thesis “Geometry-driven self-organization of migrating cells and chiral filaments” by Zuzana Dunajova (2026). The videos provide additional visual material supporting the experiments and results described in the thesis. acknowledged_ssus: - _id: Bio - _id: ScienComp article_processing_charge: No author: - first_name: Zuzana full_name: Dunajova, Zuzana id: 4B39F286-F248-11E8-B48F-1D18A9856A87 last_name: Dunajova citation: ama: Dunajova Z. Supplementary movies to PhD thesis “Geometry-driven self-organization of migrating cells and chiral filaments.” 2026. doi:10.15479/AT-ISTA-21439 apa: Dunajova, Z. (2026). Supplementary movies to PhD thesis “Geometry-driven self-organization of migrating cells and chiral filaments.” Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-21439 chicago: Dunajova, Zuzana. “Supplementary Movies to PhD Thesis ‘Geometry-Driven Self-Organization of Migrating Cells and Chiral Filaments.’” Institute of Science and Technology Austria, 2026. https://doi.org/10.15479/AT-ISTA-21439. ieee: Z. Dunajova, “Supplementary movies to PhD thesis ‘Geometry-driven self-organization of migrating cells and chiral filaments.’” Institute of Science and Technology Austria, 2026. ista: Dunajova Z. 2026. Supplementary movies to PhD thesis “Geometry-driven self-organization of migrating cells and chiral filaments”, Institute of Science and Technology Austria, 10.15479/AT-ISTA-21439. mla: Dunajova, Zuzana. Supplementary Movies to PhD Thesis “Geometry-Driven Self-Organization of Migrating Cells and Chiral Filaments.” Institute of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-21439. short: Z. Dunajova, (2026). contributor: - contributor_type: researcher first_name: Saren id: 4323B49C-F248-11E8-B48F-1D18A9856A87 last_name: Tasciyan orcid: 0000-0003-1671-393X - contributor_type: researcher first_name: Philipp id: 40136C2A-F248-11E8-B48F-1D18A9856A87 last_name: Radler orcid: '0000-0001-9198-2182 ' corr_author: '1' date_created: 2026-03-11T21:05:20Z date_published: 2026-03-12T00:00:00Z date_updated: 2026-10-16T22:31:29Z day: '12' ddc: - '570' department: - _id: GradSch - _id: EdHa doi: 10.15479/AT-ISTA-21439 file: - access_level: open_access checksum: 47809a9a31b748b16e21e92d11ddc87f content_type: application/zip creator: zdunajov date_created: 2026-03-11T20:41:28Z date_updated: 2026-03-11T20:41:28Z file_id: '21440' file_name: Supplementary_movies_Thesis_Dunajova.zip file_size: 154465214 relation: main_file success: 1 - access_level: open_access checksum: a64a174bc6abf0a5e77631e4fd121f1f content_type: text/plain creator: zdunajov date_created: 2026-03-11T20:52:39Z date_updated: 2026-03-11T20:52:39Z file_id: '21441' file_name: readme.txt file_size: 2289 relation: main_file success: 1 file_date_updated: 2026-03-11T20:52:39Z fulldoi: https://doi.org/10.15479/AT-ISTA-21439 has_accepted_license: '1' month: '03' oa: 1 oa_version: Published Version project: - _id: 34d75525-11ca-11ed-8bc3-89b6307fee9d grant_number: '26360' name: Motile active matter models of migrating cells and chiral filaments publisher: Institute of Science and Technology Austria related_material: record: - id: '13314' relation: used_in_publication status: public - id: '21427' relation: used_in_publication status: public - id: '21423' relation: used_in_publication status: public status: public title: Supplementary movies to PhD thesis “Geometry-driven self-organization of migrating cells and chiral filaments” tmp: image: /images/cc_by_nc_sa.png legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) short: CC BY-NC-SA (4.0) type: research_data user_id: 68b8ca59-c5b3-11ee-8790-cd641c68093d year: '2026' ... --- OA_place: publisher OA_type: hybrid PlanS_conform: '1' _id: '20847' abstract: - lang: eng text: "We report on an experimental active matter system with motion restricted to four cardinal directions. Our particles are magnetite-doped colloidal spheres driven by the Quincke electrorotational instability. The absence of a magnetic field (|\U0001D469|=0) leads to circular trajectories interspersed with short spontaneous runs. Intermediate fields (|\U0001D469|≲20mT) linearize the motion along the axis perpendicular to \U0001D469. At high magnetic fields, we observe the surprising emergence of a second, distinct linearization along the axis parallel to \U0001D469. With numerical simulations, we show that this behavior can be explained by anisotropic magnetic susceptibility." acknowledged_ssus: - _id: M-Shop - _id: NanoFab - _id: ScienComp - _id: LifeSc acknowledgement: "This research was funded in whole or in part by the Austrian Science Fund (FWF) [Grant DOI: 10.55776/ESP298]. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant\r\nAgreement No. 949120). This research was supported by the Scientific Service Units of The Institute of Science and Technology Austria (ISTA) through resources provided by the Miba Machine Shop, Nanofabrication Facility, Scientific Computing Facility, and Lab Support Facility. We wish to acknowledge the crucial contributions of Alexandre Morin in getting the project off the ground, and Jack Merrin for creating the SU-8 deposition protocol used in the construction of our\r\ncells. We also wish to thank Kimberley Modic and Hamza Nasir for their work on single-particle characterization. " article_number: '065418' article_processing_charge: Yes (via OA deal) article_type: original arxiv: 1 author: - first_name: Eavan full_name: Fitzgerald, Eavan id: 2df8ab8f-080d-11ed-979a-bfe651ca3afa last_name: Fitzgerald - first_name: Cécile full_name: Clavaud, Cécile id: 5f654c5d-04a1-11eb-ab36-ba9ffec58bd8 last_name: Clavaud orcid: 0000-0002-1843-3803 - first_name: Debasish full_name: Das, Debasish last_name: Das - first_name: Isaac C full_name: Lenton, Isaac C id: a550210f-223c-11ec-8182-e2d45e817efb last_name: Lenton orcid: 0000-0002-5010-6984 - first_name: Scott R full_name: Waitukaitis, Scott R id: 3A1FFC16-F248-11E8-B48F-1D18A9856A87 last_name: Waitukaitis orcid: 0000-0002-2299-3176 citation: ama: 'Fitzgerald E, Clavaud C, Das D, Lenton IC, Waitukaitis SR. Rolling at right angles: Magnetic anisotropy enables dual-anisotropic active matter. Physical Review E. 2025;112(6). doi:10.1103/1ss8-31rb' apa: 'Fitzgerald, E., Clavaud, C., Das, D., Lenton, I. C., & Waitukaitis, S. R. (2025). Rolling at right angles: Magnetic anisotropy enables dual-anisotropic active matter. Physical Review E. American Physical Society. https://doi.org/10.1103/1ss8-31rb' chicago: 'Fitzgerald, Eavan, Cécile Clavaud, Debasish Das, Isaac C Lenton, and Scott R Waitukaitis. “Rolling at Right Angles: Magnetic Anisotropy Enables Dual-Anisotropic Active Matter.” Physical Review E. American Physical Society, 2025. https://doi.org/10.1103/1ss8-31rb.' ieee: 'E. Fitzgerald, C. Clavaud, D. Das, I. C. Lenton, and S. R. Waitukaitis, “Rolling at right angles: Magnetic anisotropy enables dual-anisotropic active matter,” Physical Review E, vol. 112, no. 6. American Physical Society, 2025.' ista: 'Fitzgerald E, Clavaud C, Das D, Lenton IC, Waitukaitis SR. 2025. Rolling at right angles: Magnetic anisotropy enables dual-anisotropic active matter. Physical Review E. 112(6), 065418.' mla: 'Fitzgerald, Eavan, et al. “Rolling at Right Angles: Magnetic Anisotropy Enables Dual-Anisotropic Active Matter.” Physical Review E, vol. 112, no. 6, 065418, American Physical Society, 2025, doi:10.1103/1ss8-31rb.' short: E. Fitzgerald, C. Clavaud, D. Das, I.C. Lenton, S.R. Waitukaitis, Physical Review E 112 (2025). corr_author: '1' date_created: 2025-12-21T23:01:34Z date_published: 2025-12-01T00:00:00Z date_updated: 2025-12-29T11:19:34Z day: '01' ddc: - '530' department: - _id: ScWa doi: 10.1103/1ss8-31rb ec_funded: 1 external_id: arxiv: - '2508.05643' file: - access_level: open_access checksum: d593e933f976c3f3cde37ad66539d57d content_type: application/pdf creator: dernst date_created: 2025-12-29T11:15:42Z date_updated: 2025-12-29T11:15:42Z file_id: '20862' file_name: 2025_PhysReviewE_Fitzgerald.pdf file_size: 2131491 relation: main_file success: 1 file_date_updated: 2025-12-29T11:15:42Z fulldoi: https://doi.org/10.1103/1ss8-31rb has_accepted_license: '1' intvolume: ' 112' issue: '6' language: - iso: eng month: '12' oa: 1 oa_version: Published Version project: - _id: bd8eede5-d553-11ed-ba76-eaded0d13485 grant_number: E 298 name: 'MixQUIckR: Mixing with QUIncke Rollers' - _id: 0aa60e99-070f-11eb-9043-a6de6bdc3afa call_identifier: H2020 grant_number: '949120' name: 'Tribocharge: a multi-scale approach to an enduring problem in physics' publication: Physical Review E publication_identifier: eissn: - 2470-0053 issn: - 2470-0045 publication_status: published publisher: American Physical Society quality_controlled: '1' scopus_import: '1' status: public title: 'Rolling at right angles: Magnetic anisotropy enables dual-anisotropic active matter' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 112 year: '2025' ... --- OA_place: publisher OA_type: gold _id: '21327' abstract: - lang: eng text: Proteins exist as a dynamic ensemble of multiple conformations, and these motions are often crucial for their functions. However, current structure prediction methods predominantly yield a single conformation, overlooking the conformational heterogeneity revealed by diverse experimental modalities. Here, we present a framework for building experiment-grounded protein structure generative models that infer conformational ensembles consistent with measured experimental data. The key idea is to treat stateof-the-art protein structure predictors (e.g., AlphaFold3) as sequence-conditioned structural priors, and cast ensemble modeling as posterior inference of protein structures given experimental measurements. Through extensive real-data experiments, we demonstrate the generality of our method to incorporate a variety of experimental measurements. In particular, our framework uncovers previously unmodeled conformational heterogeneity from crystallographic densities, and generates high-accuracy NMR ensembles orders of magnitude faster than the status quo. Notably, we demonstrate that our ensembles outperform AlphaFold3 (Abramson et al., 2024) and sometimes better fit experimental data than publicly deposited structures to the Protein Data Bank (PDB, Burley et al. (2017)). We believe that this approach will unlock building predictive models that fully embrace experimentally observed conformational diversity. acknowledged_ssus: - _id: ScienComp acknowledgement: 'This work was supported by the Israeli Science Foundation (ISF) grant number 1834/24. We acknowledge support from the Austrian Science Fund (FWF, grant numbers I5812-B and I6223) and the financial support of the Helmsley Fellowships Program for Sustainability and Health. This research uses resources of the Institute of Science and Technology Austria’s scientific computing cluster. ' alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Sai A full_name: Maddipatla, Sai A id: e957f5e5-91c9-11f0-a95f-e090f66ecb4d last_name: Maddipatla - first_name: Nadav E full_name: Sellam, Nadav E id: ef280fe0-91c9-11f0-a95f-8dea3f5bc513 last_name: Sellam - first_name: Meital I full_name: Bojan, Meital I id: 11d88cf5-91ca-11f0-a95f-edf9f08f47b7 last_name: Bojan - first_name: Sanketh full_name: Vedula, Sanketh id: 94f2fe44-70fa-11f0-b76b-92922c09452b last_name: Vedula - first_name: Paul full_name: Schanda, Paul id: 7B541462-FAF6-11E9-A490-E8DFE5697425 last_name: Schanda orcid: 0000-0002-9350-7606 - first_name: Ailie full_name: Marx, Ailie last_name: Marx - first_name: Alexander full_name: Bronstein, Alexander id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6 last_name: Bronstein orcid: 0000-0001-9699-8730 citation: ama: 'Maddipatla SA, Sellam NE, Bojan MI, et al. Inverse problems with experiment-guided AlphaFold. In: Proceedings of the 42nd International Conference on Machine Learning. Vol 267. ML Research Press; 2025:42366-42393.' apa: 'Maddipatla, S. A., Sellam, N. E., Bojan, M. I., Vedula, S., Schanda, P., Marx, A., & Bronstein, A. M. (2025). Inverse problems with experiment-guided AlphaFold. In Proceedings of the 42nd International Conference on Machine Learning (Vol. 267, pp. 42366–42393). Vancouver, Canada: ML Research Press.' chicago: Maddipatla, Sai A, Nadav E Sellam, Meital I Bojan, Sanketh Vedula, Paul Schanda, Ailie Marx, and Alex M. Bronstein. “Inverse Problems with Experiment-Guided AlphaFold.” In Proceedings of the 42nd International Conference on Machine Learning, 267:42366–93. ML Research Press, 2025. ieee: S. A. Maddipatla et al., “Inverse problems with experiment-guided AlphaFold,” in Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada, 2025, vol. 267, pp. 42366–42393. ista: 'Maddipatla SA, Sellam NE, Bojan MI, Vedula S, Schanda P, Marx A, Bronstein AM. 2025. Inverse problems with experiment-guided AlphaFold. Proceedings of the 42nd International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 267, 42366–42393.' mla: Maddipatla, Sai A., et al. “Inverse Problems with Experiment-Guided AlphaFold.” Proceedings of the 42nd International Conference on Machine Learning, vol. 267, ML Research Press, 2025, pp. 42366–93. short: S.A. Maddipatla, N.E. Sellam, M.I. Bojan, S. Vedula, P. Schanda, A. Marx, A.M. Bronstein, in:, Proceedings of the 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 42366–42393. conference: end_date: 2025-07-19 location: Vancouver, Canada name: 'ICML: International Conference on Machine Learning' start_date: 2025-07-13 corr_author: '1' date_created: 2026-02-18T12:11:17Z date_published: 2025-07-30T00:00:00Z date_updated: 2026-02-19T08:56:43Z day: '30' ddc: - '000' - '540' department: - _id: PaSc - _id: AlBr - _id: GradSch external_id: arxiv: - '2502.09372' file: - access_level: open_access checksum: f33230a6d59b7978d4cd72795e4e9059 content_type: application/pdf creator: dernst date_created: 2026-02-19T08:56:10Z date_updated: 2026-02-19T08:56:10Z file_id: '21338' file_name: 2025_ICML_Maddipatla.pdf file_size: 1924177 relation: main_file success: 1 file_date_updated: 2026-02-19T08:56:10Z has_accepted_license: '1' intvolume: ' 267' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 42366 - 42393 project: - _id: eb9c82eb-77a9-11ec-83b8-aadd536561cf grant_number: I05812 name: AlloSpace. The emergence and mechanisms of allostery - _id: bdb9578d-d553-11ed-ba76-ed5d39fce6f0 grant_number: I06223 name: Structure and mechanism of the mitochondrial MIM insertase publication: Proceedings of the 42nd International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' status: public title: Inverse problems with experiment-guided AlphaFold tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 267 year: '2025' ... --- OA_place: repository OA_type: gold _id: '18697' abstract: - lang: eng text: The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules but dense, synapse-level circuit reconstruction by light microscopy has been out of reach due to limitations in resolution, contrast, and volumetric imaging capability. Here we developed light-microscopy based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning based segmentation and analysis of connectivity, thus directly incorporating molecular information in synapse-level brain tissue reconstructions. LICONN will allow synapse-level brain tissue phenotyping in biological experiments in a readily adoptable manner. acknowledged_ssus: - _id: Bio - _id: LifeSc - _id: ScienComp - _id: PreCl - _id: M-Shop - _id: E-Lib article_processing_charge: No author: - first_name: Johann G full_name: Danzl, Johann G id: 42EFD3B6-F248-11E8-B48F-1D18A9856A87 last_name: Danzl orcid: 0000-0001-8559-3973 - first_name: Julia full_name: Lyudchik, Julia id: 46E28B80-F248-11E8-B48F-1D18A9856A87 last_name: Lyudchik - first_name: Caroline full_name: Kreuzinger, Caroline id: 382077BA-F248-11E8-B48F-1D18A9856A87 last_name: Kreuzinger citation: ama: Danzl JG, Lyudchik J, Kreuzinger C. Light-microscopy based connectomic reconstruction of mammalian brain tissue. 2025. doi:10.15479/AT:ISTA:18697 apa: Danzl, J. G., Lyudchik, J., & Kreuzinger, C. (2025). Light-microscopy based connectomic reconstruction of mammalian brain tissue. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:18697 chicago: Danzl, Johann G, Julia Lyudchik, and Caroline Kreuzinger. “Light-Microscopy Based Connectomic Reconstruction of Mammalian Brain Tissue.” Institute of Science and Technology Austria, 2025. https://doi.org/10.15479/AT:ISTA:18697. ieee: J. G. Danzl, J. Lyudchik, and C. Kreuzinger, “Light-microscopy based connectomic reconstruction of mammalian brain tissue.” Institute of Science and Technology Austria, 2025. ista: Danzl JG, Lyudchik J, Kreuzinger C. 2025. Light-microscopy based connectomic reconstruction of mammalian brain tissue, Institute of Science and Technology Austria, 10.15479/AT:ISTA:18697. mla: Danzl, Johann G., et al. Light-Microscopy Based Connectomic Reconstruction of Mammalian Brain Tissue. Institute of Science and Technology Austria, 2025, doi:10.15479/AT:ISTA:18697. short: J.G. Danzl, J. Lyudchik, C. Kreuzinger, (2025). contributor: - contributor_type: researcher first_name: Mojtaba id: 3A0A06F4-F248-11E8-B48F-1D18A9856A87 last_name: Tavakoli orcid: 0000-0002-7667-6854 - contributor_type: researcher first_name: Julia id: 46E28B80-F248-11E8-B48F-1D18A9856A87 last_name: Lyudchik - contributor_type: researcher first_name: Michal last_name: Januszewski - contributor_type: researcher first_name: Vitali id: 7e146587-8972-11ed-ae7b-d7a32ea86a81 last_name: Vistunou - contributor_type: researcher first_name: Nathalie id: 40E7F008-F248-11E8-B48F-1D18A9856A87 last_name: Agudelo Duenas - contributor_type: researcher first_name: Jakob id: 937696FA-C996-11E9-8C7C-CF13E6697425 last_name: Vorlaufer - contributor_type: researcher first_name: Christoph M id: 4DF26D8C-F248-11E8-B48F-1D18A9856A87 last_name: Sommer orcid: 0000-0003-1216-9105 - contributor_type: researcher first_name: Caroline id: 382077BA-F248-11E8-B48F-1D18A9856A87 last_name: Kreuzinger - contributor_type: researcher first_name: Bárbara id: 3B03AA1A-F248-11E8-B48F-1D18A9856A87 last_name: Oliveira - 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lang: eng text: 'This file contains the code associated with the manuscript ''Effect of assortative mating and sexual selection on polygenic barriers to gene flow''. ' acknowledged_ssus: - _id: ScienComp article_processing_charge: No author: - first_name: Parvathy full_name: Surendranadh, Parvathy id: 455235B8-F248-11E8-B48F-1D18A9856A87 last_name: Surendranadh orcid: 0000-0001-6395-386X - first_name: Himani full_name: Sachdeva, Himani last_name: Sachdeva citation: ama: Surendranadh P, Sachdeva H. Mathematica notebook and Fortran code for “Effect of assortative mating and sexual selection on polygenic barriers to gene flow.” 2025. doi:10.15479/AT:ISTA:17344 apa: Surendranadh, P., & Sachdeva, H. (2025). Mathematica notebook and Fortran code for “Effect of assortative mating and sexual selection on polygenic barriers to gene flow.” Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:17344 chicago: Surendranadh, Parvathy, and Himani Sachdeva. “Mathematica Notebook and Fortran Code for ‘Effect of Assortative Mating and Sexual Selection on Polygenic Barriers to Gene Flow.’” Institute of Science and Technology Austria, 2025. https://doi.org/10.15479/AT:ISTA:17344. ieee: P. Surendranadh and H. Sachdeva, “Mathematica notebook and Fortran code for ‘Effect of assortative mating and sexual selection on polygenic barriers to gene flow.’” Institute of Science and Technology Austria, 2025. ista: Surendranadh P, Sachdeva H. 2025. Mathematica notebook and Fortran code for ‘Effect of assortative mating and sexual selection on polygenic barriers to gene flow’, Institute of Science and Technology Austria, 10.15479/AT:ISTA:17344. mla: Surendranadh, Parvathy, and Himani Sachdeva. Mathematica Notebook and Fortran Code for “Effect of Assortative Mating and Sexual Selection on Polygenic Barriers to Gene Flow.” Institute of Science and Technology Austria, 2025, doi:10.15479/AT:ISTA:17344. short: P. Surendranadh, H. Sachdeva, (2025). corr_author: '1' date_created: 2025-01-01T15:28:27Z date_published: 2025-01-07T00:00:00Z date_updated: 2025-12-30T08:44:12Z day: '07' ddc: - '576' department: - _id: GradSch - _id: NiBa doi: 10.15479/AT:ISTA:17344 file: - access_level: open_access checksum: 9c5f91876014706990a0728c3675cd2a content_type: application/zip creator: psurendr date_created: 2025-01-02T12:30:27Z date_updated: 2025-01-02T12:30:27Z file_id: '18722' file_name: Codes.zip file_size: 326835 relation: main_file success: 1 - access_level: open_access checksum: 47fe98b7cc526e634e42de58f5eae288 content_type: text/plain creator: psurendr date_created: 2025-01-02T12:30:39Z date_updated: 2025-01-02T12:30:39Z file_id: '18723' file_name: ReadMe.txt file_size: 620 relation: main_file success: 1 file_date_updated: 2025-01-02T12:30:39Z fulldoi: https://doi.org/10.15479/AT:ISTA:17344 has_accepted_license: '1' month: '01' oa: 1 oa_version: Published Version publisher: Institute of Science and Technology Austria related_material: record: - id: '19876' relation: used_in_publication status: public status: public title: Mathematica notebook and Fortran code for 'Effect of assortative mating and sexual selection on polygenic barriers to gene flow' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: research_data user_id: 9947682f-b9fa-11ee-9c4a-b3ffaafe6614 year: '2025' ... --- APC_amount: 12348 EUR OA_place: publisher OA_type: hybrid _id: '18778' abstract: - lang: eng text: Transcription by RNA polymerase II (Pol II) can be repressed by noncoding RNA, including the human RNA Alu. However, the mechanism by which endogenous RNAs repress transcription remains unclear. Here we present cryogenic-electron microscopy structures of Pol II bound to Alu RNA, which reveal that Alu RNA mimics how DNA and RNA bind to Pol II during transcription elongation. Further, we show how distinct domains of the general transcription factor TFIIF control repressive activity. Together, we reveal how a noncoding RNA can regulate mammalian gene expression. acknowledged_ssus: - _id: LifeSc - _id: EM-Fac - _id: ScienComp - _id: PreCl acknowledgement: We thank the members of the Bernecky laboratory for helpful discussions and A. Hlavata for providing Pol II for use in the fluorescence anisotropy binding assay. We thank V.-V. Hodirnau for SerialEM data collection and support with EPU data collection. We thank D. Slade (Max Perutz Laboratories and Medical University of Vienna, Vienna, Austria) for the wild-type TFIIF expression plasmid. We thank N. Thompson and R. Burgess (McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, WI, USA) for the 8WG16 hybridoma cell line. We thank C. Plaschka and M. Loose for critical reading of the manuscript. This work was supported by Austrian Science Fund (FWF) grant no. P34185 (DOI 10.55776/P34185) (C.B.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This research was further supported by the Scientific Service Units of ISTA through resources provided by the Laboratory Support Facility, Electron Microscopy Facility, Scientific Computing and the Preclinical Facility. article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Katarina full_name: Tluckova, Katarina id: 4AC7D980-F248-11E8-B48F-1D18A9856A87 last_name: Tluckova - first_name: Beata M full_name: Kaczmarek, Beata M id: 36FA4AFA-F248-11E8-B48F-1D18A9856A87 last_name: Kaczmarek - first_name: Anita P full_name: Testa Salmazo, Anita P id: 41F1F098-F248-11E8-B48F-1D18A9856A87 last_name: Testa Salmazo - first_name: Carrie A full_name: Bernecky, Carrie A id: 2CB9DFE2-F248-11E8-B48F-1D18A9856A87 last_name: Bernecky orcid: 0000-0003-0893-7036 citation: ama: Tluckova K, Kaczmarek BM, Testa Salmazo AP, Bernecky C. Mechanism of mammalian transcriptional repression by noncoding RNA. Nature Structural & Molecular Biology. 2025;32:607-612. doi:10.1038/s41594-024-01448-7 apa: Tluckova, K., Kaczmarek, B. M., Testa Salmazo, A. P., & Bernecky, C. (2025). Mechanism of mammalian transcriptional repression by noncoding RNA. Nature Structural & Molecular Biology. Springer Nature. https://doi.org/10.1038/s41594-024-01448-7 chicago: Tluckova, Katarina, Beata M Kaczmarek, Anita P Testa Salmazo, and Carrie Bernecky. “Mechanism of Mammalian Transcriptional Repression by Noncoding RNA.” Nature Structural & Molecular Biology. Springer Nature, 2025. https://doi.org/10.1038/s41594-024-01448-7. ieee: K. Tluckova, B. M. Kaczmarek, A. P. Testa Salmazo, and C. Bernecky, “Mechanism of mammalian transcriptional repression by noncoding RNA,” Nature Structural & Molecular Biology, vol. 32. Springer Nature, pp. 607–612, 2025. ista: Tluckova K, Kaczmarek BM, Testa Salmazo AP, Bernecky C. 2025. Mechanism of mammalian transcriptional repression by noncoding RNA. Nature Structural & Molecular Biology. 32, 607–612. mla: Tluckova, Katarina, et al. “Mechanism of Mammalian Transcriptional Repression by Noncoding RNA.” Nature Structural & Molecular Biology, vol. 32, Springer Nature, 2025, pp. 607–12, doi:10.1038/s41594-024-01448-7. short: K. Tluckova, B.M. Kaczmarek, A.P. Testa Salmazo, C. Bernecky, Nature Structural & Molecular Biology 32 (2025) 607–612. corr_author: '1' date_created: 2025-01-08T11:20:20Z date_published: 2025-04-01T00:00:00Z date_updated: 2025-11-20T10:28:36Z day: '01' ddc: - '570' department: - _id: CaBe doi: 10.1038/s41594-024-01448-7 external_id: isi: - '001390268000001' pmid: - '39762629' file: - access_level: open_access checksum: 2919b30b271f395888e880076a680d73 content_type: application/pdf creator: dernst date_created: 2025-04-16T08:17:27Z date_updated: 2025-04-16T08:17:27Z file_id: '19573' file_name: 2025_NatureStrucMolBiol_Tluckova.pdf file_size: 9306639 relation: main_file success: 1 file_date_updated: 2025-04-16T08:17:27Z fulldoi: https://doi.org/10.1038/s41594-024-01448-7 has_accepted_license: '1' intvolume: ' 32' isi: 1 language: - iso: eng month: '04' oa: 1 oa_version: Published Version page: 607-612 pmid: 1 project: - _id: c08a6700-5a5b-11eb-8a69-82a722b2bc30 grant_number: P34185 name: Regulation of mammalian transcription by noncoding RNA publication: Nature Structural & Molecular Biology publication_identifier: eissn: - 1545-9985 issn: - 1545-9993 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '14644' relation: earlier_version status: public scopus_import: '1' status: public title: Mechanism of mammalian transcriptional repression by noncoding RNA tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 32 year: '2025' ... --- OA_place: publisher OA_type: hybrid _id: '18879' abstract: - lang: eng text: 'Our brain has remarkable computational power, generating sophisticated behaviors, storing memories over an individual’s lifetime, and producing higher cognitive functions. However, little of our neuroscience knowledge covers the human brain. Is this organ truly unique, or is it a scaled version of the extensively studied rodent brain? Combining multicellular patch-clamp recording with expansion-based superresolution microscopy and full-scale modeling, we determined the cellular and microcircuit properties of the human hippocampal CA3 region, a fundamental circuit for memory storage. In contrast to neocortical networks, human hippocampal CA3 displayed sparse connectivity, providing a circuit architecture that maximizes associational power. Human synapses showed unique reliability, high precision, and long integration times, exhibiting both species- and circuit-specific properties. Together with expanded neuronal numbers, these circuit characteristics greatly enhanced the memory storage capacity of CA3. Our results reveal distinct microcircuit properties of the human hippocampus and begin to unravel the inner workings of our most complex organ. ' acknowledged_ssus: - _id: Bio - _id: PreCl - _id: LifeSc - _id: M-Shop - _id: ScienComp acknowledgement: We thank Florian Marr for excellent technical assistance, Christina Altmutter and Julia Flor for technical support, Alois Schlögl for programming, Todor Asenov for development of the transportation box for human brain tissue, Tim Vogels for guidance on simulations, Marcus Huber for mathematical advice, Walter Kaufmann for assistance with handling frozen tissue, and Eleftheria Kralli-Beller for manuscript editing. This research was supported by the Scientific Services Units (SSUs) of ISTA, and we are grateful for assistance from Christoph Sommer and the Imaging and Optics Facility, Preclinical Facility, Lab Support Facility, Miba Machine Shop, and Scientific Computing. We are particularly grateful to the patient donors for their support of this project and also acknowledge the excellent support of the Medical University of Vienna Department of Neurosurgery staff; Romana Hoeftberger and the Division of Neuropathology and Neurochemistry; Gregor Kasprian and the Division of Neuroradiology and Musculoskeletal Radiology; and Christoph Baumgartner, Martha Feucht, and Ekaterina Pataraia for their clinical care of the patients included in this study. We thank Laura Jonkman, the NABCA biobank, and postmortem brain sample donors for their support of this research. The project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (advanced grant no. 692692 to P.J. and Marie Skłodowska-Curie Actions Individual Fellowship no. 101026635 to J.F.W.), the Austrian Science Fund (FWF; grant PAT 4178023 to P.J. and grant DK W1232 to M.R.T. and J.G.D.), the Austrian Academy of Sciences (DOC fellowship 26137 to M.R.T.), and a NOMIS-ISTA fellowship (to A.N.-O.). article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Jake full_name: Watson, Jake id: 63836096-4690-11EA-BD4E-32803DDC885E last_name: Watson orcid: 0000-0002-8698-3823 - first_name: Victor M full_name: Vargas Barroso, Victor M id: 2F55A9DE-F248-11E8-B48F-1D18A9856A87 last_name: Vargas Barroso - first_name: Rebecca full_name: Morse, Rebecca id: ceb89ae7-dc8d-11ea-abe3-da3301d0eab4 last_name: Morse - first_name: Andrea C full_name: Navas Olivé, Andrea C id: 739d26c9-52e8-11ee-8d72-f14d3893b4ce last_name: Navas Olivé orcid: 0000-0002-9280-8597 - first_name: Mojtaba full_name: Tavakoli, Mojtaba id: 3A0A06F4-F248-11E8-B48F-1D18A9856A87 last_name: Tavakoli orcid: 0000-0002-7667-6854 - first_name: Johann G full_name: Danzl, Johann G id: 42EFD3B6-F248-11E8-B48F-1D18A9856A87 last_name: Danzl orcid: 0000-0001-8559-3973 - first_name: Matthias full_name: Tomschik, Matthias last_name: Tomschik - first_name: Karl full_name: Rössler, Karl last_name: Rössler - first_name: Peter M full_name: Jonas, Peter M id: 353C1B58-F248-11E8-B48F-1D18A9856A87 last_name: Jonas orcid: 0000-0001-5001-4804 citation: ama: Watson J, Vargas Barroso VM, Morse R, et al. Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory. Cell. 2025;188(2):501-514.e18. doi:10.1016/j.cell.2024.11.022 apa: Watson, J., Vargas Barroso, V. M., Morse, R., Navas Olivé, A. C., Tavakoli, M., Danzl, J. G., … Jonas, P. M. (2025). Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory. Cell. Elsevier. https://doi.org/10.1016/j.cell.2024.11.022 chicago: Watson, Jake, Victor M Vargas Barroso, Rebecca Morse, Andrea C Navas Olivé, Mojtaba Tavakoli, Johann G Danzl, Matthias Tomschik, Karl Rössler, and Peter M Jonas. “Human Hippocampal CA3 Uses Specific Functional Connectivity Rules for Efficient Associative Memory.” Cell. Elsevier, 2025. https://doi.org/10.1016/j.cell.2024.11.022. ieee: J. Watson et al., “Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory,” Cell, vol. 188, no. 2. Elsevier, p. 501–514.e18, 2025. ista: Watson J, Vargas Barroso VM, Morse R, Navas Olivé AC, Tavakoli M, Danzl JG, Tomschik M, Rössler K, Jonas PM. 2025. Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory. Cell. 188(2), 501–514.e18. mla: Watson, Jake, et al. “Human Hippocampal CA3 Uses Specific Functional Connectivity Rules for Efficient Associative Memory.” Cell, vol. 188, no. 2, Elsevier, 2025, p. 501–514.e18, doi:10.1016/j.cell.2024.11.022. short: J. Watson, V.M. Vargas Barroso, R. Morse, A.C. Navas Olivé, M. Tavakoli, J.G. Danzl, M. Tomschik, K. Rössler, P.M. Jonas, Cell 188 (2025) 501–514.e18. corr_author: '1' date_created: 2025-01-26T23:01:49Z date_published: 2025-01-23T00:00:00Z date_updated: 2026-04-14T08:34:32Z day: '23' ddc: - '570' department: - _id: JoDa - _id: PeJo - _id: GradSch doi: 10.1016/j.cell.2024.11.022 ec_funded: 1 external_id: isi: - '001408395600001' pmid: - '39667938' file: - access_level: open_access checksum: d5a818edc32d249cdf75e1bb5b70a4b7 content_type: application/pdf creator: dernst date_created: 2025-01-27T08:46:33Z date_updated: 2025-01-27T08:46:33Z file_id: '18884' file_name: 2025_Cell_Watson.pdf file_size: 14082343 relation: main_file success: 1 file_date_updated: 2025-01-27T08:46:33Z fulldoi: https://doi.org/10.1016/j.cell.2024.11.022 has_accepted_license: '1' intvolume: ' 188' isi: 1 issue: '2' language: - iso: eng month: '01' oa: 1 oa_version: Published Version page: 501-514.e18 pmid: 1 project: - _id: 25B7EB9E-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '692692' name: Biophysics and circuit function of a giant cortical glutamatergic synapse - _id: fc2be41b-9c52-11eb-aca3-faa90aa144e9 call_identifier: H2020 grant_number: '101026635' name: Synaptic computations of the hippocampal CA3 circuitry - _id: 6285a163-2b32-11ec-9570-8e204ca2dba5 grant_number: '26137' name: Studying Organelle Structure and Function at Nanoscale Resolution with Expansion Microscopy - _id: 2548AE96-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: W1232 name: Molecular Drug Targets - _id: 8d9195e9-16d5-11f0-9cad-d075be887a1e grant_number: PAT 4178023 name: Synaptic networks of human brain - _id: 9B861AAC-BA93-11EA-9121-9846C619BF3A name: NOMIS Fellowship Program publication: Cell publication_identifier: eissn: - 1097-4172 issn: - 0092-8674 publication_status: published publisher: Elsevier quality_controlled: '1' related_material: record: - id: '18688' relation: earlier_version status: public scopus_import: '1' status: public title: Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 188 year: '2025' ...