[{"doi":"10.5061/DRYAD.7PVMCVDTJ","_id":"13061","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"10284"}]},"ec_funded":1,"year":"2021","article_processing_charge":"No","oa":1,"date_updated":"2023-08-14T11:45:28Z","citation":{"chicago":"Casillas Perez, Barbara E, Christopher Pull, Filip Naiser, Elisabeth Naderlinger, Jiri Matas, and Sylvia Cremer. “Early Queen Infection Shapes Developmental Dynamics and Induces Long-Term Disease Protection in Incipient Ant Colonies.” Dryad, 2021. <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">https://doi.org/10.5061/DRYAD.7PVMCVDTJ</a>.","ista":"Casillas Perez BE, Pull C, Naiser F, Naderlinger E, Matas J, Cremer S. 2021. Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>.","mla":"Casillas Perez, Barbara E., et al. <i>Early Queen Infection Shapes Developmental Dynamics and Induces Long-Term Disease Protection in Incipient Ant Colonies</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>.","ieee":"B. E. Casillas Perez, C. Pull, F. Naiser, E. Naderlinger, J. Matas, and S. Cremer, “Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies.” Dryad, 2021.","ama":"Casillas Perez BE, Pull C, Naiser F, Naderlinger E, Matas J, Cremer S. Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies. 2021. doi:<a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">10.5061/DRYAD.7PVMCVDTJ</a>","short":"B.E. Casillas Perez, C. Pull, F. Naiser, E. Naderlinger, J. Matas, S. Cremer, (2021).","apa":"Casillas Perez, B. E., Pull, C., Naiser, F., Naderlinger, E., Matas, J., &#38; Cremer, S. (2021). Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.7PVMCVDTJ\">https://doi.org/10.5061/DRYAD.7PVMCVDTJ</a>"},"date_created":"2023-05-23T16:14:35Z","title":"Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies","license":"https://creativecommons.org/publicdomain/zero/1.0/","status":"public","type":"research_data_reference","date_published":"2021-10-29T00:00:00Z","publisher":"Dryad","department":[{"_id":"SyCr"}],"project":[{"name":"Epidemics in ant societies on a chip","call_identifier":"H2020","grant_number":"771402","_id":"2649B4DE-B435-11E9-9278-68D0E5697425"}],"tmp":{"image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)","short":"CC0 (1.0)"},"month":"10","ddc":["570"],"abstract":[{"lang":"eng","text":"Infections early in life can have enduring effects on an organism’s development and immunity. In this study, we show that this equally applies to developing “superorganisms” – incipient social insect colonies. When we exposed newly mated Lasius niger ant queens to a low pathogen dose, their colonies grew more slowly than controls before winter, but reached similar sizes afterwards. Independent of exposure, queen hibernation survival improved when the ratio of pupae to workers was small. Queens that reared fewer pupae before worker emergence exhibited lower pathogen levels, indicating that high brood rearing efforts interfere with the ability of the queen’s immune system to suppress pathogen proliferation. Early-life queen pathogen-exposure also improved the immunocompetence of her worker offspring, as demonstrated by challenging the workers to the same pathogen a year later. Transgenerational transfer of the queen’s pathogen experience to her workforce can hence durably reduce the disease susceptibility of the whole superorganism."}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.7pvmcvdtj"}],"day":"29","oa_version":"Published Version","author":[{"first_name":"Barbara E","full_name":"Casillas Perez, Barbara E","id":"351ED2AA-F248-11E8-B48F-1D18A9856A87","last_name":"Casillas Perez"},{"orcid":"0000-0003-1122-3982","id":"3C7F4840-F248-11E8-B48F-1D18A9856A87","last_name":"Pull","full_name":"Pull, Christopher","first_name":"Christopher"},{"last_name":"Naiser","full_name":"Naiser, Filip","first_name":"Filip"},{"full_name":"Naderlinger, Elisabeth","first_name":"Elisabeth","last_name":"Naderlinger"},{"last_name":"Matas","first_name":"Jiri","full_name":"Matas, Jiri"},{"first_name":"Sylvia","full_name":"Cremer, Sylvia","id":"2F64EC8C-F248-11E8-B48F-1D18A9856A87","last_name":"Cremer","orcid":"0000-0002-2193-3868"}]},{"date_published":"2021-03-02T00:00:00Z","status":"public","type":"research_data_reference","publisher":"Dryad","department":[{"_id":"NiBa"}],"month":"03","tmp":{"image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)","short":"CC0 (1.0)"},"ddc":["570"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"This paper analyzes the conditions for local adaptation in a metapopulation with infinitely many islands under a model of hard selection, where population size depends on local fitness. Each island belongs to one of two distinct ecological niches or habitats. Fitness is influenced by an additive trait which is under habitat-dependent directional selection. Our analysis is based on the diffusion approximation and  accounts for both genetic drift and demographic stochasticity. By neglecting linkage disequilibria, it yields the joint distribution of allele frequencies and population size on each island. We find that under hard selection, the conditions for local adaptation in a rare habitat are more restrictive for more polygenic traits: even moderate migration load per locus at very many loci is sufficient for population sizes to decline. This further reduces the efficacy of selection at individual loci due to increased drift and because smaller populations are more prone to swamping due to migration, causing a positive feedback between increasing maladaptation and declining population sizes. Our analysis also highlights the importance of demographic stochasticity, which  exacerbates the decline in numbers of maladapted populations, leading to population collapse in the rare habitat at significantly lower migration than predicted by deterministic arguments."}],"oa_version":"Published Version","day":"02","main_file_link":[{"url":"https://doi.org/10.5061/dryad.8gtht76p1","open_access":"1"}],"author":[{"last_name":"Szep","id":"485BB5A4-F248-11E8-B48F-1D18A9856A87","first_name":"Eniko","full_name":"Szep, Eniko"},{"id":"42377A0A-F248-11E8-B48F-1D18A9856A87","last_name":"Sachdeva","full_name":"Sachdeva, Himani","first_name":"Himani"},{"full_name":"Barton, Nicholas H","first_name":"Nicholas H","orcid":"0000-0002-8548-5240","last_name":"Barton","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"}],"doi":"10.5061/DRYAD.8GTHT76P1","_id":"13062","related_material":{"record":[{"status":"public","id":"9252","relation":"used_in_publication"}]},"article_processing_charge":"No","year":"2021","oa":1,"date_updated":"2023-09-05T15:44:05Z","citation":{"apa":"Szep, E., Sachdeva, H., &#38; Barton, N. H. (2021). Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model. Dryad. <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">https://doi.org/10.5061/DRYAD.8GTHT76P1</a>","ama":"Szep E, Sachdeva H, Barton NH. Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model. 2021. doi:<a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>","ieee":"E. Szep, H. Sachdeva, and N. H. Barton, “Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model.” Dryad, 2021.","short":"E. Szep, H. Sachdeva, N.H. Barton, (2021).","ista":"Szep E, Sachdeva H, Barton NH. 2021. Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model, Dryad, <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>.","chicago":"Szep, Eniko, Himani Sachdeva, and Nicholas H Barton. “Supplementary Code for: Polygenic Local Adaptation in Metapopulations: A Stochastic Eco-Evolutionary Model.” Dryad, 2021. <a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">https://doi.org/10.5061/DRYAD.8GTHT76P1</a>.","mla":"Szep, Eniko, et al. <i>Supplementary Code for: Polygenic Local Adaptation in Metapopulations: A Stochastic Eco-Evolutionary Model</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/DRYAD.8GTHT76P1\">10.5061/DRYAD.8GTHT76P1</a>."},"date_created":"2023-05-23T16:17:02Z","title":"Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model"},{"author":[{"first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson"}],"oa_version":"Published Version","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.sqv9s4n51"}],"day":"04","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only $\\leq$ 10\\% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having &gt;95% probability of contributing &gt;0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data.","lang":"eng"}],"ddc":["570"],"month":"11","tmp":{"image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)","short":"CC0 (1.0)"},"department":[{"_id":"MaRo"}],"publisher":"Dryad","status":"public","date_published":"2021-11-04T00:00:00Z","type":"research_data_reference","title":"Probabilistic inference of the genetic architecture of functional enrichment of complex traits","date_created":"2023-05-23T16:20:16Z","citation":{"ama":"Robinson MR. Probabilistic inference of the genetic architecture of functional enrichment of complex traits. 2021. doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>","ieee":"M. R. Robinson, “Probabilistic inference of the genetic architecture of functional enrichment of complex traits.” Dryad, 2021.","short":"M.R. Robinson, (2021).","ista":"Robinson MR. 2021. Probabilistic inference of the genetic architecture of functional enrichment of complex traits, Dryad, <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>.","chicago":"Robinson, Matthew Richard. “Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits.” Dryad, 2021. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>.","mla":"Robinson, Matthew Richard. <i>Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits</i>. Dryad, 2021, doi:<a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">10.5061/dryad.sqv9s4n51</a>.","apa":"Robinson, M. R. (2021). Probabilistic inference of the genetic architecture of functional enrichment of complex traits. Dryad. <a href=\"https://doi.org/10.5061/dryad.sqv9s4n51\">https://doi.org/10.5061/dryad.sqv9s4n51</a>"},"date_updated":"2023-09-26T10:36:15Z","oa":1,"year":"2021","article_processing_charge":"No","related_material":{"link":[{"relation":"software","url":"https://github.com/medical-genomics-group/gmrm"}],"record":[{"relation":"used_in_publication","id":"8429","status":"public"}]},"_id":"13063","doi":"10.5061/dryad.sqv9s4n51"},{"department":[{"_id":"EdHa"}],"publisher":"Zenodo","type":"research_data_reference","license":"https://creativecommons.org/licenses/by/4.0/","status":"public","date_published":"2021-07-30T00:00:00Z","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.6577226"}],"oa_version":"Published Version","day":"30","abstract":[{"lang":"eng","text":"Source data and source code for the graphs in \"Spatiotemporal dynamics of self-organized branching pancreatic cancer-derived organoids\"."}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ddc":["570"],"month":"07","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"author":[{"last_name":"Randriamanantsoa","full_name":"Randriamanantsoa, Samuel","first_name":"Samuel"},{"last_name":"Papargyriou","first_name":"Aristeidis","full_name":"Papargyriou, Aristeidis"},{"last_name":"Maurer","first_name":"Carlo","full_name":"Maurer, Carlo"},{"full_name":"Peschke, Katja","first_name":"Katja","last_name":"Peschke"},{"last_name":"Schuster","first_name":"Maximilian","full_name":"Schuster, Maximilian"},{"last_name":"Zecchin","first_name":"Giulia","full_name":"Zecchin, Giulia"},{"first_name":"Katja","full_name":"Steiger, Katja","last_name":"Steiger"},{"full_name":"Öllinger, Rupert","first_name":"Rupert","last_name":"Öllinger"},{"last_name":"Saur","first_name":"Dieter","full_name":"Saur, Dieter"},{"full_name":"Scheel, Christina","first_name":"Christina","last_name":"Scheel"},{"last_name":"Rad","first_name":"Roland","full_name":"Rad, Roland"},{"last_name":"Hannezo","id":"3A9DB764-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6005-1561","full_name":"Hannezo, Edouard B","first_name":"Edouard B"},{"last_name":"Reichert","first_name":"Maximilian","full_name":"Reichert, Maximilian"},{"last_name":"Bausch","full_name":"Bausch, Andreas R.","first_name":"Andreas R."}],"doi":"10.5281/ZENODO.5148117","_id":"13068","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"12217"}]},"citation":{"mla":"Randriamanantsoa, Samuel, et al. <i>Spatiotemporal Dynamics of Self-Organized Branching in Pancreas-Derived Organoids</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>.","chicago":"Randriamanantsoa, Samuel, Aristeidis Papargyriou, Carlo Maurer, Katja Peschke, Maximilian Schuster, Giulia Zecchin, Katja Steiger, et al. “Spatiotemporal Dynamics of Self-Organized Branching in Pancreas-Derived Organoids.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5148117\">https://doi.org/10.5281/ZENODO.5148117</a>.","ista":"Randriamanantsoa S, Papargyriou A, Maurer C, Peschke K, Schuster M, Zecchin G, Steiger K, Öllinger R, Saur D, Scheel C, Rad R, Hannezo EB, Reichert M, Bausch AR. 2021. Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>.","ama":"Randriamanantsoa S, Papargyriou A, Maurer C, et al. Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5148117\">10.5281/ZENODO.5148117</a>","ieee":"S. Randriamanantsoa <i>et al.</i>, “Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids.” Zenodo, 2021.","short":"S. Randriamanantsoa, A. Papargyriou, C. Maurer, K. Peschke, M. Schuster, G. Zecchin, K. Steiger, R. Öllinger, D. Saur, C. Scheel, R. Rad, E.B. Hannezo, M. Reichert, A.R. Bausch, (2021).","apa":"Randriamanantsoa, S., Papargyriou, A., Maurer, C., Peschke, K., Schuster, M., Zecchin, G., … Bausch, A. R. (2021). Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5148117\">https://doi.org/10.5281/ZENODO.5148117</a>"},"date_created":"2023-05-23T16:39:24Z","title":"Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids","oa":1,"date_updated":"2023-08-04T09:25:23Z","year":"2021","article_processing_charge":"No"},{"department":[{"_id":"MaDe"}],"status":"public","type":"research_data_reference","date_published":"2021-12-25T00:00:00Z","publisher":"Zenodo","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"To survive elevated temperatures, ectotherms adjust the fluidity of membranes by fine-tuning lipid desaturation levels in a process previously described to be cell-autonomous. We have discovered that, in Caenorhabditis elegans, neuronal Heat shock Factor 1 (HSF-1), the conserved master regulator of the heat shock response (HSR)- causes extensive fat remodelling in peripheral tissues. These changes include a decrease in fat desaturase and acid lipase expression in the intestine, and a global shift in the saturation levels of plasma membrane’s phospholipids. The observed remodelling of plasma membrane is in line with ectothermic adaptive responses and gives worms a cumulative advantage to warm temperatures. We have determined that at least six TAX-2/TAX-4 cGMP gated channel expressing sensory neurons and TGF-β/BMP are required for signalling across tissues to modulate fat desaturation. We also find neuronal hsf-1  is not only sufficient but also partially necessary to control the fat remodelling response and for survival at warm temperatures. This is the first study to show that a thermostat-based mechanism can cell non-autonomously coordinate membrane saturation and composition across tissues in a multicellular animal.","lang":"eng"}],"oa_version":"Published Version","day":"25","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.5547464"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"month":"12","ddc":["570"],"author":[{"first_name":"Laetitia","full_name":"Chauve, Laetitia","last_name":"Chauve"},{"full_name":"Hodge, Francesca","first_name":"Francesca","last_name":"Hodge"},{"full_name":"Murdoch, Sharlene","first_name":"Sharlene","last_name":"Murdoch"},{"last_name":"Masoudzadeh","first_name":"Fatemah","full_name":"Masoudzadeh, Fatemah"},{"last_name":"Mann","full_name":"Mann, Harry-Jack","first_name":"Harry-Jack"},{"last_name":"Lopez-Clavijo","first_name":"Andrea","full_name":"Lopez-Clavijo, Andrea"},{"first_name":"Hanneke","full_name":"Okkenhaug, Hanneke","last_name":"Okkenhaug"},{"last_name":"West","full_name":"West, Greg","first_name":"Greg"},{"full_name":"Sousa, Bebiana C.","first_name":"Bebiana C.","last_name":"Sousa"},{"first_name":"Anne","full_name":"Segonds-Pichon, Anne","last_name":"Segonds-Pichon"},{"full_name":"Li, Cheryl","first_name":"Cheryl","last_name":"Li"},{"full_name":"Wingett, Steven","first_name":"Steven","last_name":"Wingett"},{"first_name":"Hermine","full_name":"Kienberger, Hermine","last_name":"Kienberger"},{"full_name":"Kleigrewe, Karin","first_name":"Karin","last_name":"Kleigrewe"},{"first_name":"Mario","full_name":"de Bono, Mario","last_name":"de Bono","id":"4E3FF80E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8347-0443"},{"last_name":"Wakelam","full_name":"Wakelam, Michael","first_name":"Michael"},{"full_name":"Casanueva, Olivia","first_name":"Olivia","last_name":"Casanueva"}],"doi":"10.5281/ZENODO.5519410","_id":"13069","related_material":{"record":[{"relation":"used_in_publication","id":"10322","status":"public"}]},"date_created":"2023-05-23T16:40:56Z","title":"Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans","citation":{"apa":"Chauve, L., Hodge, F., Murdoch, S., Masoudzadeh, F., Mann, H.-J., Lopez-Clavijo, A., … Casanueva, O. (2021). Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5519410\">https://doi.org/10.5281/ZENODO.5519410</a>","ama":"Chauve L, Hodge F, Murdoch S, et al. Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>","ieee":"L. Chauve <i>et al.</i>, “Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans.” Zenodo, 2021.","short":"L. Chauve, F. Hodge, S. Murdoch, F. Masoudzadeh, H.-J. Mann, A. Lopez-Clavijo, H. Okkenhaug, G. West, B.C. Sousa, A. Segonds-Pichon, C. Li, S. Wingett, H. Kienberger, K. Kleigrewe, M. de Bono, M. Wakelam, O. Casanueva, (2021).","chicago":"Chauve, Laetitia, Francesca Hodge, Sharlene Murdoch, Fatemah Masoudzadeh, Harry-Jack Mann, Andrea Lopez-Clavijo, Hanneke Okkenhaug, et al. “Neuronal HSF-1 Coordinates the Propagation of Fat Desaturation across Tissues to Enable Adaptation to High Temperatures in C. Elegans.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5519410\">https://doi.org/10.5281/ZENODO.5519410</a>.","mla":"Chauve, Laetitia, et al. <i>Neuronal HSF-1 Coordinates the Propagation of Fat Desaturation across Tissues to Enable Adaptation to High Temperatures in C. Elegans</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>.","ista":"Chauve L, Hodge F, Murdoch S, Masoudzadeh F, Mann H-J, Lopez-Clavijo A, Okkenhaug H, West G, Sousa BC, Segonds-Pichon A, Li C, Wingett S, Kienberger H, Kleigrewe K, de Bono M, Wakelam M, Casanueva O. 2021. Neuronal HSF-1 coordinates the propagation of fat desaturation across tissues to enable adaptation to high temperatures in C. elegans, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5519410\">10.5281/ZENODO.5519410</a>."},"year":"2021","article_processing_charge":"No","oa":1,"date_updated":"2023-08-14T11:53:26Z"},{"department":[{"_id":"MaRo"}],"type":"research_data_reference","status":"public","date_published":"2021-12-20T00:00:00Z","publisher":"Zenodo","author":[{"full_name":"McCartney, Daniel L","first_name":"Daniel L","last_name":"McCartney"},{"last_name":"Hillary","full_name":"Hillary, Robert F","first_name":"Robert F"},{"full_name":"Conole, Eleanor LS","first_name":"Eleanor LS","last_name":"Conole"},{"last_name":"Trejo Banos","full_name":"Trejo Banos, Daniel","first_name":"Daniel"},{"last_name":"Gadd","first_name":"Danni A","full_name":"Gadd, Danni A"},{"last_name":"Walker","full_name":"Walker, Rosie M","first_name":"Rosie M"},{"last_name":"Nangle","full_name":"Nangle, Cliff","first_name":"Cliff"},{"last_name":"Flaig","full_name":"Flaig, Robin","first_name":"Robin"},{"first_name":"Archie","full_name":"Campbell, Archie","last_name":"Campbell"},{"first_name":"Alison D","full_name":"Murray, Alison D","last_name":"Murray"},{"last_name":"Munoz Maniega","full_name":"Munoz Maniega, Susana","first_name":"Susana"},{"last_name":"del C Valdes-Hernandez","full_name":"del C Valdes-Hernandez, Maria","first_name":"Maria"},{"last_name":"Harris","full_name":"Harris, Mathew A","first_name":"Mathew A"},{"first_name":"Mark E","full_name":"Bastin, Mark E","last_name":"Bastin"},{"full_name":"Wardlaw, Joanna M","first_name":"Joanna M","last_name":"Wardlaw"},{"first_name":"Sarah E","full_name":"Harris, Sarah E","last_name":"Harris"},{"last_name":"Porteous","full_name":"Porteous, David J","first_name":"David J"},{"first_name":"Elliot M","full_name":"Tucker-Drob, Elliot M","last_name":"Tucker-Drob"},{"last_name":"McIntosh","first_name":"Andrew M","full_name":"McIntosh, Andrew M"},{"last_name":"Evans","full_name":"Evans, Kathryn L","first_name":"Kathryn L"},{"last_name":"Deary","first_name":"Ian J","full_name":"Deary, Ian J"},{"full_name":"Cox, Simon R","first_name":"Simon R","last_name":"Cox"},{"first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","orcid":"0000-0001-8982-8813"},{"last_name":"Marioni","full_name":"Marioni, Riccardo E","first_name":"Riccardo E"}],"abstract":[{"text":"CpGs and corresponding mean weights for DNAm-based prediction of cognitive abilities (6 traits)","lang":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","main_file_link":[{"url":"https://doi.org/10.5281/zenodo.5794029","open_access":"1"}],"day":"20","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"month":"12","ddc":["570"],"_id":"13072","doi":"10.5281/ZENODO.5794028","date_created":"2023-05-23T16:46:20Z","citation":{"apa":"McCartney, D. L., Hillary, R. F., Conole, E. L., Trejo Banos, D., Gadd, D. A., Walker, R. M., … Marioni, R. E. (2021). Blood-based epigenome-wide analyses of cognitive abilities. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.5794028\">https://doi.org/10.5281/ZENODO.5794028</a>","short":"D.L. McCartney, R.F. Hillary, E.L. Conole, D. Trejo Banos, D.A. Gadd, R.M. Walker, C. Nangle, R. Flaig, A. Campbell, A.D. Murray, S. Munoz Maniega, M. del C Valdes-Hernandez, M.A. Harris, M.E. Bastin, J.M. Wardlaw, S.E. Harris, D.J. Porteous, E.M. Tucker-Drob, A.M. McIntosh, K.L. Evans, I.J. Deary, S.R. Cox, M.R. Robinson, R.E. Marioni, (2021).","ama":"McCartney DL, Hillary RF, Conole EL, et al. Blood-based epigenome-wide analyses of cognitive abilities. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>","ieee":"D. L. McCartney <i>et al.</i>, “Blood-based epigenome-wide analyses of cognitive abilities.” Zenodo, 2021.","mla":"McCartney, Daniel L., et al. <i>Blood-Based Epigenome-Wide Analyses of Cognitive Abilities</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>.","ista":"McCartney DL, Hillary RF, Conole EL, Trejo Banos D, Gadd DA, Walker RM, Nangle C, Flaig R, Campbell A, Murray AD, Munoz Maniega S, del C Valdes-Hernandez M, Harris MA, Bastin ME, Wardlaw JM, Harris SE, Porteous DJ, Tucker-Drob EM, McIntosh AM, Evans KL, Deary IJ, Cox SR, Robinson MR, Marioni RE. 2021. Blood-based epigenome-wide analyses of cognitive abilities, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.5794028\">10.5281/ZENODO.5794028</a>.","chicago":"McCartney, Daniel L, Robert F Hillary, Eleanor LS Conole, Daniel Trejo Banos, Danni A Gadd, Rosie M Walker, Cliff Nangle, et al. “Blood-Based Epigenome-Wide Analyses of Cognitive Abilities.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.5794028\">https://doi.org/10.5281/ZENODO.5794028</a>."},"title":"Blood-based epigenome-wide analyses of cognitive abilities","year":"2021","article_processing_charge":"No","date_updated":"2023-08-02T14:05:12Z","oa":1,"related_material":{"record":[{"status":"public","id":"10702","relation":"used_in_publication"}]}},{"related_material":{"link":[{"relation":"software","url":"https://github.com/caslu85/Induced-Gap-Closing-Shared/tree/1.1.3"}],"record":[{"status":"public","id":"9570","relation":"used_in_publication"}]},"citation":{"ista":"Puglia D, Martinez E, Menard G, Pöschl A, Gronin S, Gardner G, Kallaher R, Manfra M, Marcus C, Higginbotham AP, Casparis L. 2021. Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.4592435\">10.5281/ZENODO.4592435</a>.","mla":"Puglia, Denise, et al. <i>Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire</i>. Zenodo, 2021, doi:<a href=\"https://doi.org/10.5281/ZENODO.4592435\">10.5281/ZENODO.4592435</a>.","chicago":"Puglia, Denise, Esteban Martinez, Gerbold Menard, Andreas Pöschl, Sergei Gronin, Geoffrey Gardner, Ray Kallaher, et al. “Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire.” Zenodo, 2021. <a href=\"https://doi.org/10.5281/ZENODO.4592435\">https://doi.org/10.5281/ZENODO.4592435</a>.","ieee":"D. Puglia <i>et al.</i>, “Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire.” Zenodo, 2021.","ama":"Puglia D, Martinez E, Menard G, et al. Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire. 2021. doi:<a href=\"https://doi.org/10.5281/ZENODO.4592435\">10.5281/ZENODO.4592435</a>","short":"D. Puglia, E. Martinez, G. Menard, A. Pöschl, S. Gronin, G. Gardner, R. Kallaher, M. Manfra, C. Marcus, A.P. Higginbotham, L. Casparis, (2021).","apa":"Puglia, D., Martinez, E., Menard, G., Pöschl, A., Gronin, S., Gardner, G., … Casparis, L. (2021). Data for ’Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.4592435\">https://doi.org/10.5281/ZENODO.4592435</a>"},"title":"Data for 'Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire","date_created":"2023-05-23T17:11:28Z","date_updated":"2023-08-08T14:08:07Z","oa":1,"year":"2021","article_processing_charge":"No","doi":"10.5281/ZENODO.4592435","_id":"13080","day":"09","oa_version":"Published Version","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.4592460"}],"abstract":[{"text":"Data for the manuscript 'Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire' ([2006.01275] Closing of the Induced Gap in a Hybrid Superconductor-Semiconductor Nanowire (arxiv.org))\r\n\r\nWe upload a pdf with extended data sets, and the raw data for these extended datasets as well.","lang":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ddc":["530"],"month":"03","author":[{"last_name":"Puglia","id":"4D495994-AE37-11E9-AC72-31CAE5697425","first_name":"Denise","full_name":"Puglia, Denise"},{"full_name":"Martinez, Esteban","first_name":"Esteban","last_name":"Martinez"},{"first_name":"Gerbold","full_name":"Menard, Gerbold","last_name":"Menard"},{"last_name":"Pöschl","full_name":"Pöschl, Andreas","first_name":"Andreas"},{"first_name":"Sergei","full_name":"Gronin, Sergei","last_name":"Gronin"},{"full_name":"Gardner, Geoffrey","first_name":"Geoffrey","last_name":"Gardner"},{"last_name":"Kallaher","first_name":"Ray","full_name":"Kallaher, Ray"},{"full_name":"Manfra, Michael","first_name":"Michael","last_name":"Manfra"},{"first_name":"Charles","full_name":"Marcus, Charles","last_name":"Marcus"},{"orcid":"0000-0003-2607-2363","last_name":"Higginbotham","id":"4AD6785A-F248-11E8-B48F-1D18A9856A87","full_name":"Higginbotham, Andrew P","first_name":"Andrew P"},{"last_name":"Casparis","full_name":"Casparis, Lucas","first_name":"Lucas"}],"department":[{"_id":"AnHi"}],"publisher":"Zenodo","date_published":"2021-03-09T00:00:00Z","type":"research_data_reference","status":"public"},{"publication":"Proceedings of the 38th International Conference on Machine Learning","language":[{"iso":"eng"}],"intvolume":"       139","scopus_import":"1","has_accepted_license":"1","title":"Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks","article_processing_charge":"No","oa":1,"date_updated":"2024-09-10T13:03:17Z","status":"public","type":"conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file":[{"success":1,"checksum":"19489cf5e16a0596b1f92e317d97c9b0","file_name":"2021_PMLR_Nguyen.pdf","file_id":"13155","date_created":"2023-06-19T10:49:12Z","date_updated":"2023-06-19T10:49:12Z","creator":"dernst","access_level":"open_access","relation":"main_file","content_type":"application/pdf","file_size":591332}],"day":"01","oa_version":"Published Version","month":"07","publication_identifier":{"eissn":["2640-3498"],"isbn":["9781713845065"]},"conference":{"start_date":"2021-07-18","name":"International Conference on Machine Learning","end_date":"2021-07-24","location":"Virtual"},"arxiv":1,"author":[{"full_name":"Nguyen, Quynh","first_name":"Quynh","last_name":"Nguyen"},{"orcid":"0000-0002-3242-7020","id":"27EB676C-8706-11E9-9510-7717E6697425","last_name":"Mondelli","full_name":"Mondelli, Marco","first_name":"Marco"},{"last_name":"Montufar","first_name":"Guido","full_name":"Montufar, Guido"}],"_id":"13146","file_date_updated":"2023-06-19T10:49:12Z","citation":{"ieee":"Q. Nguyen, M. Mondelli, and G. Montufar, “Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks,” in <i>Proceedings of the 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 8119–8129.","ama":"Nguyen Q, Mondelli M, Montufar G. Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. In: <i>Proceedings of the 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:8119-8129.","short":"Q. Nguyen, M. Mondelli, G. Montufar, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 8119–8129.","ista":"Nguyen Q, Mondelli M, Montufar G. 2021. Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. Proceedings of the 38th International Conference on Machine Learning. International Conference on Machine Learning vol. 139, 8119–8129.","mla":"Nguyen, Quynh, et al. “Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks.” <i>Proceedings of the 38th International Conference on Machine Learning</i>, vol. 139, ML Research Press, 2021, pp. 8119–29.","chicago":"Nguyen, Quynh, Marco Mondelli, and Guido Montufar. “Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks.” In <i>Proceedings of the 38th International Conference on Machine Learning</i>, 139:8119–29. ML Research Press, 2021.","apa":"Nguyen, Q., Mondelli, M., &#38; Montufar, G. (2021). Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. In <i>Proceedings of the 38th International Conference on Machine Learning</i> (Vol. 139, pp. 8119–8129). Virtual: ML Research Press."},"date_created":"2023-06-18T22:00:48Z","year":"2021","acknowledgement":"The authors would like to thank the anonymous reviewers for their helpful comments. MM was partially supported by the 2019 Lopez-Loreta Prize. QN and GM acknowledge support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no 757983).","volume":139,"quality_controlled":"1","page":"8119-8129","department":[{"_id":"MaMo"}],"date_published":"2021-07-01T00:00:00Z","publisher":"ML Research Press","project":[{"_id":"059876FA-7A3F-11EA-A408-12923DDC885E","name":"Prix Lopez-Loretta 2019 - Marco Mondelli"}],"abstract":[{"lang":"eng","text":"A recent line of work has analyzed the theoretical properties of deep neural networks via the Neural Tangent Kernel (NTK). In particular, the smallest eigenvalue of the NTK has been related to the memorization capacity, the global convergence of gradient descent algorithms and the generalization of deep nets. However, existing results either provide bounds in the two-layer setting or assume that the spectrum of the NTK matrices is bounded away from 0 for multi-layer networks. In this paper, we provide tight bounds on the smallest eigenvalue of NTK matrices for deep ReLU nets, both in the limiting case of infinite widths and for finite widths. In the finite-width setting, the network architectures we consider are fairly general: we require the existence of a wide layer with roughly order of N neurons, N being the number of data samples; and the scaling of the remaining layer widths is arbitrary (up to logarithmic factors). To obtain our results, we analyze various quantities of independent interest: we give lower bounds on the smallest singular value of hidden feature matrices, and upper bounds on the Lipschitz constant of input-output feature maps."}],"external_id":{"arxiv":["2012.11654"]},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"ddc":["000"],"publication_status":"published"},{"file_date_updated":"2023-06-19T10:41:05Z","_id":"13147","ec_funded":1,"date_created":"2023-06-18T22:00:48Z","citation":{"mla":"Alimisis, Foivos, et al. “Communication-Efficient Distributed Optimization with Quantized Preconditioners.” <i>Proceedings of the 38th International Conference on Machine Learning</i>, vol. 139, ML Research Press, 2021, pp. 196–206.","chicago":"Alimisis, Foivos, Peter Davies, and Dan-Adrian Alistarh. “Communication-Efficient Distributed Optimization with Quantized Preconditioners.” In <i>Proceedings of the 38th International Conference on Machine Learning</i>, 139:196–206. ML Research Press, 2021.","ista":"Alimisis F, Davies P, Alistarh D-A. 2021. Communication-efficient distributed optimization with quantized preconditioners. Proceedings of the 38th International Conference on Machine Learning. International Conference on Machine Learning vol. 139, 196–206.","short":"F. Alimisis, P. Davies, D.-A. Alistarh, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 196–206.","ieee":"F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed optimization with quantized preconditioners,” in <i>Proceedings of the 38th International Conference on Machine Learning</i>, Virtual, 2021, vol. 139, pp. 196–206.","ama":"Alimisis F, Davies P, Alistarh D-A. Communication-efficient distributed optimization with quantized preconditioners. In: <i>Proceedings of the 38th International Conference on Machine Learning</i>. Vol 139. ML Research Press; 2021:196-206.","apa":"Alimisis, F., Davies, P., &#38; Alistarh, D.-A. (2021). Communication-efficient distributed optimization with quantized preconditioners. In <i>Proceedings of the 38th International Conference on Machine Learning</i> (Vol. 139, pp. 196–206). Virtual: ML Research Press."},"acknowledgement":"The authors would like to thank Janne Korhonen, Aurelien Lucchi, Celestine MendlerDunner and Antonio Orvieto for helpful discussions. FA ¨and DA were supported during this work by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). PD was supported by the European Union’s Horizon 2020 programme under the Marie Skłodowska-Curie grant agreement No. 754411.","year":"2021","department":[{"_id":"DaAl"}],"page":"196-206","quality_controlled":"1","volume":139,"publisher":"ML Research Press","date_published":"2021-07-01T00:00:00Z","project":[{"call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223"},{"grant_number":"754411","_id":"260C2330-B435-11E9-9278-68D0E5697425","name":"ISTplus - Postdoctoral Fellowships","call_identifier":"H2020"}],"external_id":{"arxiv":["2102.07214"]},"abstract":[{"text":"We investigate fast and communication-efficient algorithms for the classic problem of minimizing a sum of strongly convex and smooth functions that are distributed among n\r\n different nodes, which can communicate using a limited number of bits. Most previous communication-efficient approaches for this problem are limited to first-order optimization, and therefore have \\emph{linear} dependence on the condition number in their communication complexity. We show that this dependence is not inherent: communication-efficient methods can in fact have sublinear dependence on the condition number. For this, we design and analyze the first communication-efficient distributed variants of preconditioned gradient descent for Generalized Linear Models, and for Newton’s method. Our results rely on a new technique for quantizing both the preconditioner and the descent direction at each step of the algorithms, while controlling their convergence rate. We also validate our findings experimentally, showing faster convergence and reduced communication relative to previous methods.","lang":"eng"}],"ddc":["000"],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publication_status":"published","publication":"Proceedings of the 38th International Conference on Machine Learning","language":[{"iso":"eng"}],"scopus_import":"1","intvolume":"       139","has_accepted_license":"1","title":"Communication-efficient distributed optimization with quantized preconditioners","date_updated":"2023-06-19T10:44:38Z","oa":1,"article_processing_charge":"No","type":"conference","status":"public","oa_version":"Published Version","day":"01","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file":[{"creator":"dernst","content_type":"application/pdf","access_level":"open_access","relation":"main_file","file_size":429087,"file_name":"2021_PMLR_Alimisis.pdf","checksum":"7ec0d59bac268b49c76bf2e036dedd7a","file_id":"13154","success":1,"date_updated":"2023-06-19T10:41:05Z","date_created":"2023-06-19T10:41:05Z"}],"conference":{"location":"Virtual","end_date":"2021-07-24","name":"International Conference on Machine Learning","start_date":"2021-07-18"},"month":"07","publication_identifier":{"isbn":["9781713845065"],"eissn":["2640-3498"]},"author":[{"last_name":"Alimisis","first_name":"Foivos","full_name":"Alimisis, Foivos"},{"full_name":"Davies, Peter","first_name":"Peter","last_name":"Davies","id":"11396234-BB50-11E9-B24C-90FCE5697425","orcid":"0000-0002-5646-9524"},{"first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","orcid":"0000-0003-3650-940X"}],"arxiv":1},{"type":"journal_article","status":"public","author":[{"first_name":"Asaf A.","full_name":"Diringer, Asaf A.","last_name":"Diringer"},{"full_name":"Gulden, Tobias","first_name":"Tobias","last_name":"Gulden","id":"1083E038-9F73-11E9-A4B5-532AE6697425","orcid":"0000-0001-6814-7541"}],"arxiv":1,"month":"06","publication_identifier":{"eissn":["24699969"],"issn":["24699950"]},"day":"21","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2007.14879"}],"oa_version":"Preprint","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","language":[{"iso":"eng"}],"publication":"Physical Review B","date_updated":"2023-08-04T10:56:33Z","oa":1,"article_processing_charge":"No","issue":"21","title":"Impact of drive harmonics on the stability of Floquet many-body localization","isi":1,"intvolume":"       103","project":[{"call_identifier":"H2020","name":"ISTplus - Postdoctoral Fellowships","_id":"260C2330-B435-11E9-9278-68D0E5697425","grant_number":"754411"}],"publisher":"American Physical Society","date_published":"2021-06-21T00:00:00Z","department":[{"_id":"MaSe"}],"quality_controlled":"1","volume":103,"publication_status":"published","external_id":{"arxiv":["2007.14879"],"isi":["000664429700005"]},"abstract":[{"lang":"eng","text":"We investigate how the critical driving amplitude at the Floquet many-body localized (MBL) to ergodic phase transition differs between smooth and nonsmooth drives. To this end, we numerically study a disordered spin-1/2 chain which is periodically driven by a sine or square-wave drive over a wide range of driving frequencies. In both cases the critical driving amplitude increases monotonically with the frequency, and at large frequencies it is identical for the two drives. However, at low and intermediate frequencies the critical amplitude of the square-wave drive depends strongly on the frequency, while that of the sinusoidal drive is almost constant over a wide frequency range. By analyzing the density of drive-induced resonances we conclude that this difference is due to resonances induced by the higher harmonics which are present (absent) in the Fourier spectrum of the square-wave (sine) drive. Furthermore, we suggest a numerically efficient method for estimating the frequency dependence of the critical driving amplitudes for different drives which is based on calculating the density of drive-induced resonances. We conclude that delocalization occurs once the density of drive-induced resonances reaches a critical value determined only by the static system."}],"article_type":"original","_id":"8198","doi":"10.1103/PhysRevB.103.214204","acknowledgement":"We thank Y. Bar Lev, T. Biadse, and, particularly, E. Bairey and B. Katzir for illuminating discussions and their many insights and help. The authors thank N. Lindner for his support throughout this project. We are further grateful to M. Serbyn, A. Kamenev, A. Turner, and S. de Nicola for reading the manuscript and providing good feedback and suggestions. We acknowledge financial support from the Defense Advanced Research Projects Agency through the DRINQS program, Grant No. D18AC00025. T.G. was in part supported by an Aly Kaufman Fellowship at the Technion. T.G. acknowledges funding from the Institute of Science and Technology (IST) Austria and from the European Union’s Horizon 2020 research and innovation program under Marie SkłodowskaCurie Grant Agreement No. 754411.under the Marie Skłodowska-Curie Grant Agreement No.754411.","year":"2021","date_created":"2020-08-04T13:03:40Z","citation":{"short":"A.A. Diringer, T. Gulden, Physical Review B 103 (2021).","ieee":"A. A. Diringer and T. Gulden, “Impact of drive harmonics on the stability of Floquet many-body localization,” <i>Physical Review B</i>, vol. 103, no. 21. American Physical Society, 2021.","ama":"Diringer AA, Gulden T. Impact of drive harmonics on the stability of Floquet many-body localization. <i>Physical Review B</i>. 2021;103(21). doi:<a href=\"https://doi.org/10.1103/PhysRevB.103.214204\">10.1103/PhysRevB.103.214204</a>","mla":"Diringer, Asaf A., and Tobias Gulden. “Impact of Drive Harmonics on the Stability of Floquet Many-Body Localization.” <i>Physical Review B</i>, vol. 103, no. 21, 214204, American Physical Society, 2021, doi:<a href=\"https://doi.org/10.1103/PhysRevB.103.214204\">10.1103/PhysRevB.103.214204</a>.","ista":"Diringer AA, Gulden T. 2021. Impact of drive harmonics on the stability of Floquet many-body localization. Physical Review B. 103(21), 214204.","chicago":"Diringer, Asaf A., and Tobias Gulden. “Impact of Drive Harmonics on the Stability of Floquet Many-Body Localization.” <i>Physical Review B</i>. American Physical Society, 2021. <a href=\"https://doi.org/10.1103/PhysRevB.103.214204\">https://doi.org/10.1103/PhysRevB.103.214204</a>.","apa":"Diringer, A. A., &#38; Gulden, T. (2021). Impact of drive harmonics on the stability of Floquet many-body localization. <i>Physical Review B</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevB.103.214204\">https://doi.org/10.1103/PhysRevB.103.214204</a>"},"ec_funded":1,"article_number":"214204"},{"publication_identifier":{"issn":["0179-5376"],"eissn":["1432-0444"]},"month":"09","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1007/s00454-020-00233-9"}],"day":"01","author":[{"full_name":"Boissonnat, Jean-Daniel","first_name":"Jean-Daniel","last_name":"Boissonnat"},{"first_name":"Ramsay","full_name":"Dyer, Ramsay","last_name":"Dyer"},{"first_name":"Arijit","full_name":"Ghosh, Arijit","last_name":"Ghosh"},{"last_name":"Lieutier","first_name":"Andre","full_name":"Lieutier, Andre"},{"orcid":"0000-0002-7472-2220","last_name":"Wintraecken","id":"307CFBC8-F248-11E8-B48F-1D18A9856A87","first_name":"Mathijs","full_name":"Wintraecken, Mathijs"}],"status":"public","type":"journal_article","isi":1,"has_accepted_license":"1","intvolume":"        66","scopus_import":"1","article_processing_charge":"Yes (via OA deal)","oa":1,"date_updated":"2024-03-07T14:54:59Z","title":"Local conditions for triangulating submanifolds of Euclidean space","publication":"Discrete and Computational Geometry","language":[{"iso":"eng"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"ddc":["510"],"abstract":[{"lang":"eng","text":"We consider the following setting: suppose that we are given a manifold M in Rd with positive reach. Moreover assume that we have an embedded simplical complex A without boundary, whose vertex set lies on the manifold, is sufficiently dense and such that all simplices in A have sufficient quality. We prove that if, locally, interiors of the projection of the simplices onto the tangent space do not intersect, then A is a triangulation of the manifold, that is, they are homeomorphic."}],"external_id":{"isi":["000558119300001"]},"publication_status":"published","date_published":"2021-09-01T00:00:00Z","publisher":"Springer Nature","volume":66,"quality_controlled":"1","department":[{"_id":"HeEd"}],"page":"666-686","project":[{"_id":"260C2330-B435-11E9-9278-68D0E5697425","grant_number":"754411","name":"ISTplus - Postdoctoral Fellowships","call_identifier":"H2020"}],"ec_funded":1,"year":"2021","acknowledgement":"Open access funding provided by the Institute of Science and Technology (IST Austria). Arijit Ghosh is supported by the Ramanujan Fellowship (No. SB/S2/RJN-064/2015), India.\r\nThis work has been funded by the European Research Council under the European Union’s ERC Grant Agreement number 339025 GUDHI (Algorithmic Foundations of Geometric Understanding in Higher Dimensions). The third author is supported by Ramanujan Fellowship (No. SB/S2/RJN-064/2015), India. The fifth author also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 754411.","citation":{"apa":"Boissonnat, J.-D., Dyer, R., Ghosh, A., Lieutier, A., &#38; Wintraecken, M. (2021). Local conditions for triangulating submanifolds of Euclidean space. <i>Discrete and Computational Geometry</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s00454-020-00233-9\">https://doi.org/10.1007/s00454-020-00233-9</a>","ieee":"J.-D. Boissonnat, R. Dyer, A. Ghosh, A. Lieutier, and M. Wintraecken, “Local conditions for triangulating submanifolds of Euclidean space,” <i>Discrete and Computational Geometry</i>, vol. 66. Springer Nature, pp. 666–686, 2021.","ama":"Boissonnat J-D, Dyer R, Ghosh A, Lieutier A, Wintraecken M. Local conditions for triangulating submanifolds of Euclidean space. <i>Discrete and Computational Geometry</i>. 2021;66:666-686. doi:<a href=\"https://doi.org/10.1007/s00454-020-00233-9\">10.1007/s00454-020-00233-9</a>","short":"J.-D. Boissonnat, R. Dyer, A. Ghosh, A. Lieutier, M. Wintraecken, Discrete and Computational Geometry 66 (2021) 666–686.","chicago":"Boissonnat, Jean-Daniel, Ramsay Dyer, Arijit Ghosh, Andre Lieutier, and Mathijs Wintraecken. “Local Conditions for Triangulating Submanifolds of Euclidean Space.” <i>Discrete and Computational Geometry</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/s00454-020-00233-9\">https://doi.org/10.1007/s00454-020-00233-9</a>.","ista":"Boissonnat J-D, Dyer R, Ghosh A, Lieutier A, Wintraecken M. 2021. Local conditions for triangulating submanifolds of Euclidean space. Discrete and Computational Geometry. 66, 666–686.","mla":"Boissonnat, Jean-Daniel, et al. “Local Conditions for Triangulating Submanifolds of Euclidean Space.” <i>Discrete and Computational Geometry</i>, vol. 66, Springer Nature, 2021, pp. 666–86, doi:<a href=\"https://doi.org/10.1007/s00454-020-00233-9\">10.1007/s00454-020-00233-9</a>."},"date_created":"2020-08-11T07:11:51Z","doi":"10.1007/s00454-020-00233-9","_id":"8248","article_type":"original"},{"publication_status":"published","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"ddc":["000","570"],"abstract":[{"lang":"eng","text":"Brains process information in spiking neural networks. Their intricate connections shape the diverse functions these networks perform. In comparison, the functional capabilities of models of spiking networks are still rudimentary. This shortcoming is mainly due to the lack of insight and practical algorithms to construct the necessary connectivity. Any such algorithm typically attempts to build networks by iteratively reducing the error compared to a desired output. But assigning credit to hidden units in multi-layered spiking networks has remained challenging due to the non-differentiable nonlinearity of spikes. To avoid this issue, one can employ surrogate gradients to discover the required connectivity in spiking network models. However, the choice of a surrogate is not unique, raising the question of how its implementation influences the effectiveness of the method. Here, we use numerical simulations to systematically study how essential design parameters of surrogate gradients impact learning performance on a range of classification problems. We show that surrogate gradient learning is robust to different shapes of underlying surrogate derivatives, but the choice of the derivative’s scale can substantially affect learning performance. When we combine surrogate gradients with a suitable activity regularization technique, robust information processing can be achieved in spiking networks even at the sparse activity limit. Our study provides a systematic account of the remarkable robustness of surrogate gradient learning and serves as a practical guide to model functional spiking neural networks."}],"external_id":{"isi":["000663433900003"],"pmid":["33513328"]},"project":[{"grant_number":"819603","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","call_identifier":"H2020","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."},{"name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","grant_number":"214316/Z/18/Z"}],"date_published":"2021-03-01T00:00:00Z","publisher":"MIT Press","quality_controlled":"1","volume":33,"page":"899-925","department":[{"_id":"TiVo"}],"year":"2021","acknowledgement":"F.Z. was supported by the Wellcome Trust (110124/Z/15/Z) and the Novartis Research Foundation. T.P.V. was supported by a Wellcome Trust Sir Henry Dale Research fellowship (WT100000), a Wellcome Trust Senior Research Fellowship (214316/Z/18/Z), and an ERC Consolidator Grant SYNAPSEEK.","citation":{"ama":"Zenke F, Vogels TP. The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. <i>Neural Computation</i>. 2021;33(4):899-925. doi:<a href=\"https://doi.org/10.1162/neco_a_01367\">10.1162/neco_a_01367</a>","ieee":"F. Zenke and T. P. Vogels, “The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks,” <i>Neural Computation</i>, vol. 33, no. 4. MIT Press, pp. 899–925, 2021.","short":"F. Zenke, T.P. Vogels, Neural Computation 33 (2021) 899–925.","chicago":"Zenke, Friedemann, and Tim P Vogels. “The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks.” <i>Neural Computation</i>. MIT Press, 2021. <a href=\"https://doi.org/10.1162/neco_a_01367\">https://doi.org/10.1162/neco_a_01367</a>.","mla":"Zenke, Friedemann, and Tim P. Vogels. “The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks.” <i>Neural Computation</i>, vol. 33, no. 4, MIT Press, 2021, pp. 899–925, doi:<a href=\"https://doi.org/10.1162/neco_a_01367\">10.1162/neco_a_01367</a>.","ista":"Zenke F, Vogels TP. 2021. The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. Neural Computation. 33(4), 899–925.","apa":"Zenke, F., &#38; Vogels, T. P. (2021). The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. <i>Neural Computation</i>. MIT Press. <a href=\"https://doi.org/10.1162/neco_a_01367\">https://doi.org/10.1162/neco_a_01367</a>"},"date_created":"2020-08-12T12:08:24Z","ec_funded":1,"article_type":"original","_id":"8253","file_date_updated":"2022-04-08T06:05:39Z","doi":"10.1162/neco_a_01367","author":[{"orcid":"0000-0003-1883-644X","last_name":"Zenke","full_name":"Zenke, Friedemann","first_name":"Friedemann"},{"orcid":"0000-0003-3295-6181","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","first_name":"Tim P","full_name":"Vogels, Tim P"}],"month":"03","publication_identifier":{"eissn":["1530-888X"],"issn":["0899-7667"]},"file":[{"date_updated":"2022-04-08T06:05:39Z","date_created":"2022-04-08T06:05:39Z","file_name":"2021_NeuralComputation_Zenke.pdf","file_id":"11131","checksum":"eac5a51c24c8989ae7cf9ae32ec3bc95","success":1,"access_level":"open_access","content_type":"application/pdf","relation":"main_file","file_size":1611614,"creator":"dernst"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","day":"01","oa_version":"Published Version","type":"journal_article","status":"public","pmid":1,"article_processing_charge":"No","issue":"4","date_updated":"2023-08-04T10:53:14Z","oa":1,"title":"The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks","isi":1,"scopus_import":"1","has_accepted_license":"1","intvolume":"        33","language":[{"iso":"eng"}],"publication":"Neural Computation"},{"ec_funded":1,"related_material":{"record":[{"status":"public","id":"15077","relation":"earlier_version"}],"link":[{"relation":"earlier_version","url":"https://doi.org/10.4230/LIPIcs.ICALP.2020.7"}]},"date_created":"2020-08-24T06:24:04Z","citation":{"ista":"Alistarh D-A, Nadiradze G, Sabour A. 2021. Dynamic averaging load balancing on cycles. Algorithmica.","chicago":"Alistarh, Dan-Adrian, Giorgi Nadiradze, and Amirmojtaba Sabour. “Dynamic Averaging Load Balancing on Cycles.” <i>Algorithmica</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/s00453-021-00905-9\">https://doi.org/10.1007/s00453-021-00905-9</a>.","mla":"Alistarh, Dan-Adrian, et al. “Dynamic Averaging Load Balancing on Cycles.” <i>Algorithmica</i>, Springer Nature, 2021, doi:<a href=\"https://doi.org/10.1007/s00453-021-00905-9\">10.1007/s00453-021-00905-9</a>.","ieee":"D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” <i>Algorithmica</i>. Springer Nature, 2021.","ama":"Alistarh D-A, Nadiradze G, Sabour A. Dynamic averaging load balancing on cycles. <i>Algorithmica</i>. 2021. doi:<a href=\"https://doi.org/10.1007/s00453-021-00905-9\">10.1007/s00453-021-00905-9</a>","short":"D.-A. Alistarh, G. Nadiradze, A. Sabour, Algorithmica (2021).","apa":"Alistarh, D.-A., Nadiradze, G., &#38; Sabour, A. (2021). Dynamic averaging load balancing on cycles. <i>Algorithmica</i>. Virtual, Online; Germany: Springer Nature. <a href=\"https://doi.org/10.1007/s00453-021-00905-9\">https://doi.org/10.1007/s00453-021-00905-9</a>"},"acknowledgement":"The authors sincerely thank Thomas Sauerwald and George Giakkoupis for insightful discussions, and Mohsen Ghaffari, Yuval Peres, and Udi Wieder for feedback on earlier versions of this draft. We also thank the ICALP anonymous reviewers for their very useful comments. Open access funding provided by Institute of Science and Technology (IST Austria). Funding was provided by European Research Council (Grant No. PR1042ERC01).","year":"2021","doi":"10.1007/s00453-021-00905-9","file_date_updated":"2021-12-27T10:36:40Z","article_type":"original","_id":"8286","external_id":{"arxiv":["2003.09297"],"isi":["000734004600001"]},"abstract":[{"lang":"eng","text":"We consider the following dynamic load-balancing process: given an underlying graph G with n nodes, in each step t≥ 0, one unit of load is created, and placed at a randomly chosen graph node. In the same step, the chosen node picks a random neighbor, and the two nodes balance their loads by averaging them. We are interested in the expected gap between the minimum and maximum loads at nodes as the process progresses, and its dependence on n and on the graph structure. Variants of the above graphical balanced allocation process have been studied previously by Peres, Talwar, and Wieder [Peres et al., 2015], and by Sauerwald and Sun [Sauerwald and Sun, 2015]. These authors left as open the question of characterizing the gap in the case of cycle graphs in the dynamic case, where weights are created during the algorithm’s execution. For this case, the only known upper bound is of 𝒪(n log n), following from a majorization argument due to [Peres et al., 2015], which analyzes a related graphical allocation process. In this paper, we provide an upper bound of 𝒪 (√n log n) on the expected gap of the above process for cycles of length n. We introduce a new potential analysis technique, which enables us to bound the difference in load between k-hop neighbors on the cycle, for any k ≤ n/2. We complement this with a \"gap covering\" argument, which bounds the maximum value of the gap by bounding its value across all possible subsets of a certain structure, and recursively bounding the gaps within each subset. We provide analytical and experimental evidence that our upper bound on the gap is tight up to a logarithmic factor. "}],"ddc":["000"],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publication_status":"published","department":[{"_id":"DaAl"}],"quality_controlled":"1","publisher":"Springer Nature","date_published":"2021-12-24T00:00:00Z","project":[{"call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning","grant_number":"805223","_id":"268A44D6-B435-11E9-9278-68D0E5697425"},{"_id":"B67AFEDC-15C9-11EA-A837-991A96BB2854","name":"IST Austria Open Access Fund"}],"scopus_import":"1","has_accepted_license":"1","isi":1,"title":"Dynamic averaging load balancing on cycles","date_updated":"2024-03-05T07:35:53Z","oa":1,"article_processing_charge":"Yes (via OA deal)","publication":"Algorithmica","language":[{"iso":"eng"}],"oa_version":"Published Version","day":"24","file":[{"checksum":"21169b25b0c8e17b21e12af22bff9870","file_name":"2021_Algorithmica_Alistarh.pdf","file_id":"10577","success":1,"date_updated":"2021-12-27T10:36:40Z","date_created":"2021-12-27T10:36:40Z","creator":"cchlebak","content_type":"application/pdf","access_level":"open_access","relation":"main_file","file_size":525950}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","conference":{"location":"Virtual, Online; Germany","end_date":"2020-07-11","name":"ICALP: International Colloquium on Automata, Languages, and Programming ","start_date":"2020-07-08"},"publication_identifier":{"eissn":["1432-0541"],"issn":["0178-4617"]},"month":"12","author":[{"first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0001-5634-0731","id":"3279A00C-F248-11E8-B48F-1D18A9856A87","last_name":"Nadiradze","full_name":"Nadiradze, Giorgi","first_name":"Giorgi"},{"full_name":"Sabour, Amirmojtaba","first_name":"Amirmojtaba","last_name":"Sabour","id":"bcc145fd-e77f-11ea-ae8b-80d661dbff67"}],"arxiv":1,"type":"journal_article","status":"public"},{"arxiv":1,"author":[{"first_name":"Oswin","full_name":"Aichholzer, Oswin","last_name":"Aichholzer"},{"full_name":"Akitaya, Hugo A.","first_name":"Hugo A.","last_name":"Akitaya"},{"last_name":"Cheung","first_name":"Kenneth C.","full_name":"Cheung, Kenneth C."},{"last_name":"Demaine","first_name":"Erik D.","full_name":"Demaine, Erik D."},{"first_name":"Martin L.","full_name":"Demaine, Martin L.","last_name":"Demaine"},{"full_name":"Fekete, Sándor P.","first_name":"Sándor P.","last_name":"Fekete"},{"last_name":"Kleist","first_name":"Linda","full_name":"Kleist, Linda"},{"last_name":"Kostitsyna","full_name":"Kostitsyna, Irina","first_name":"Irina"},{"full_name":"Löffler, Maarten","first_name":"Maarten","last_name":"Löffler"},{"first_name":"Zuzana","full_name":"Masárová, Zuzana","last_name":"Masárová","id":"45CFE238-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6660-1322"},{"first_name":"Klara","full_name":"Mundilova, Klara","last_name":"Mundilova"},{"last_name":"Schmidt","full_name":"Schmidt, Christiane","first_name":"Christiane"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1910.09917v3"}],"oa_version":"Preprint","day":"01","month":"02","publication_identifier":{"issn":["09257721"]},"type":"journal_article","status":"public","title":"Folding polyominoes with holes into a cube","article_processing_charge":"No","oa":1,"date_updated":"2023-08-04T10:57:42Z","intvolume":"        93","scopus_import":"1","isi":1,"language":[{"iso":"eng"}],"publication":"Computational Geometry: Theory and Applications","publication_status":"published","abstract":[{"lang":"eng","text":"When can a polyomino piece of paper be folded into a unit cube? Prior work studied tree-like polyominoes, but polyominoes with holes remain an intriguing open problem. We present sufficient conditions for a polyomino with one or several holes to fold into a cube, and conditions under which cube folding is impossible. In particular, we show that all but five special “basic” holes guarantee foldability."}],"external_id":{"arxiv":["1910.09917"],"isi":["000579185100004"]},"project":[{"name":"The Wittgenstein Prize","call_identifier":"FWF","grant_number":"Z00342","_id":"268116B8-B435-11E9-9278-68D0E5697425"}],"volume":93,"quality_controlled":"1","department":[{"_id":"HeEd"}],"date_published":"2021-02-01T00:00:00Z","publisher":"Elsevier","date_created":"2020-08-30T22:01:09Z","citation":{"short":"O. Aichholzer, H.A. Akitaya, K.C. Cheung, E.D. Demaine, M.L. Demaine, S.P. Fekete, L. Kleist, I. Kostitsyna, M. Löffler, Z. Masárová, K. Mundilova, C. Schmidt, Computational Geometry: Theory and Applications 93 (2021).","ama":"Aichholzer O, Akitaya HA, Cheung KC, et al. Folding polyominoes with holes into a cube. <i>Computational Geometry: Theory and Applications</i>. 2021;93. doi:<a href=\"https://doi.org/10.1016/j.comgeo.2020.101700\">10.1016/j.comgeo.2020.101700</a>","ieee":"O. Aichholzer <i>et al.</i>, “Folding polyominoes with holes into a cube,” <i>Computational Geometry: Theory and Applications</i>, vol. 93. Elsevier, 2021.","ista":"Aichholzer O, Akitaya HA, Cheung KC, Demaine ED, Demaine ML, Fekete SP, Kleist L, Kostitsyna I, Löffler M, Masárová Z, Mundilova K, Schmidt C. 2021. Folding polyominoes with holes into a cube. Computational Geometry: Theory and Applications. 93, 101700.","mla":"Aichholzer, Oswin, et al. “Folding Polyominoes with Holes into a Cube.” <i>Computational Geometry: Theory and Applications</i>, vol. 93, 101700, Elsevier, 2021, doi:<a href=\"https://doi.org/10.1016/j.comgeo.2020.101700\">10.1016/j.comgeo.2020.101700</a>.","chicago":"Aichholzer, Oswin, Hugo A. Akitaya, Kenneth C. Cheung, Erik D. Demaine, Martin L. Demaine, Sándor P. Fekete, Linda Kleist, et al. “Folding Polyominoes with Holes into a Cube.” <i>Computational Geometry: Theory and Applications</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.comgeo.2020.101700\">https://doi.org/10.1016/j.comgeo.2020.101700</a>.","apa":"Aichholzer, O., Akitaya, H. A., Cheung, K. C., Demaine, E. D., Demaine, M. L., Fekete, S. P., … Schmidt, C. (2021). Folding polyominoes with holes into a cube. <i>Computational Geometry: Theory and Applications</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.comgeo.2020.101700\">https://doi.org/10.1016/j.comgeo.2020.101700</a>"},"year":"2021","acknowledgement":"This research was performed in part at the 33rd Bellairs Winter Workshop on Computational Geometry. We thank all other participants for a fruitful atmosphere. H. Akitaya was supported by NSF CCF-1422311 & 1423615. Z. Masárová was partially funded by Wittgenstein Prize, Austrian Science Fund (FWF), grant no. Z 342-N31.","article_number":"101700","related_material":{"record":[{"status":"public","relation":"shorter_version","id":"6989"}]},"_id":"8317","article_type":"original","doi":"10.1016/j.comgeo.2020.101700"},{"isi":1,"scopus_import":"1","intvolume":"        66","article_processing_charge":"No","oa":1,"date_updated":"2024-03-07T14:51:11Z","title":"On mutually diagonal nets on (confocal) quadrics and 3-dimensional webs","publication":"Discrete and Computational Geometry","language":[{"iso":"eng"}],"publication_identifier":{"eissn":["1432-0444"],"issn":["0179-5376"]},"month":"10","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","main_file_link":[{"url":"https://arxiv.org/abs/1908.00856","open_access":"1"}],"day":"01","arxiv":1,"author":[{"last_name":"Akopyan","id":"430D2C90-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2548-617X","first_name":"Arseniy","full_name":"Akopyan, Arseniy"},{"first_name":"Alexander I.","full_name":"Bobenko, Alexander I.","last_name":"Bobenko"},{"last_name":"Schief","first_name":"Wolfgang K.","full_name":"Schief, Wolfgang K."},{"first_name":"Jan","full_name":"Techter, Jan","last_name":"Techter"}],"type":"journal_article","status":"public","ec_funded":1,"year":"2021","acknowledgement":"This research was supported by the DFG Collaborative Research Center TRR 109 “Discretization in Geometry and Dynamics”. W.K.S. was also supported by the Australian Research Council (DP1401000851). A.V.A. was also supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 78818 Alpha).","date_created":"2020-09-06T22:01:13Z","citation":{"short":"A. Akopyan, A.I. Bobenko, W.K. Schief, J. Techter, Discrete and Computational Geometry 66 (2021) 938–976.","ama":"Akopyan A, Bobenko AI, Schief WK, Techter J. On mutually diagonal nets on (confocal) quadrics and 3-dimensional webs. <i>Discrete and Computational Geometry</i>. 2021;66:938-976. doi:<a href=\"https://doi.org/10.1007/s00454-020-00240-w\">10.1007/s00454-020-00240-w</a>","ieee":"A. Akopyan, A. I. Bobenko, W. K. Schief, and J. Techter, “On mutually diagonal nets on (confocal) quadrics and 3-dimensional webs,” <i>Discrete and Computational Geometry</i>, vol. 66. Springer Nature, pp. 938–976, 2021.","chicago":"Akopyan, Arseniy, Alexander I. Bobenko, Wolfgang K. Schief, and Jan Techter. “On Mutually Diagonal Nets on (Confocal) Quadrics and 3-Dimensional Webs.” <i>Discrete and Computational Geometry</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1007/s00454-020-00240-w\">https://doi.org/10.1007/s00454-020-00240-w</a>.","ista":"Akopyan A, Bobenko AI, Schief WK, Techter J. 2021. On mutually diagonal nets on (confocal) quadrics and 3-dimensional webs. Discrete and Computational Geometry. 66, 938–976.","mla":"Akopyan, Arseniy, et al. “On Mutually Diagonal Nets on (Confocal) Quadrics and 3-Dimensional Webs.” <i>Discrete and Computational Geometry</i>, vol. 66, Springer Nature, 2021, pp. 938–76, doi:<a href=\"https://doi.org/10.1007/s00454-020-00240-w\">10.1007/s00454-020-00240-w</a>.","apa":"Akopyan, A., Bobenko, A. I., Schief, W. K., &#38; Techter, J. (2021). On mutually diagonal nets on (confocal) quadrics and 3-dimensional webs. <i>Discrete and Computational Geometry</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s00454-020-00240-w\">https://doi.org/10.1007/s00454-020-00240-w</a>"},"doi":"10.1007/s00454-020-00240-w","_id":"8338","article_type":"original","abstract":[{"lang":"eng","text":"Canonical parametrisations of classical confocal coordinate systems are introduced and exploited to construct non-planar analogues of incircular (IC) nets on individual quadrics and systems of confocal quadrics. Intimate connections with classical deformations of quadrics that are isometric along asymptotic lines and circular cross-sections of quadrics are revealed. The existence of octahedral webs of surfaces of Blaschke type generated by asymptotic and characteristic lines that are diagonally related to lines of curvature is proved theoretically and established constructively. Appropriate samplings (grids) of these webs lead to three-dimensional extensions of non-planar IC nets. Three-dimensional octahedral grids composed of planes and spatially extending (checkerboard) IC-nets are shown to arise in connection with systems of confocal quadrics in Minkowski space. In this context, the Laguerre geometric notion of conical octahedral grids of planes is introduced. The latter generalise the octahedral grids derived from systems of confocal quadrics in Minkowski space. An explicit construction of conical octahedral grids is presented. The results are accompanied by various illustrations which are based on the explicit formulae provided by the theory."}],"external_id":{"isi":["000564488500002"],"arxiv":["1908.00856"]},"publication_status":"published","date_published":"2021-10-01T00:00:00Z","publisher":"Springer Nature","volume":66,"quality_controlled":"1","page":"938-976","department":[{"_id":"HeEd"}],"project":[{"grant_number":"788183","_id":"266A2E9E-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"Alpha Shape Theory Extended"}]},{"arxiv":1,"author":[{"last_name":"Pitrik","full_name":"Pitrik, József","first_name":"József"},{"first_name":"Daniel","full_name":"Virosztek, Daniel","orcid":"0000-0003-1109-5511","id":"48DB45DA-F248-11E8-B48F-1D18A9856A87","last_name":"Virosztek"}],"publication_identifier":{"issn":["0024-3795"]},"month":"01","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2002.11678"}],"day":"15","oa_version":"Preprint","status":"public","type":"journal_article","article_processing_charge":"No","oa":1,"date_updated":"2023-08-04T10:58:14Z","title":"A divergence center interpretation of general symmetric Kubo-Ando means, and related weighted multivariate operator means","isi":1,"intvolume":"       609","language":[{"iso":"eng"}],"publication":"Linear Algebra and its Applications","publication_status":"published","abstract":[{"lang":"eng","text":"It is well known that special Kubo-Ando operator means admit divergence center interpretations, moreover, they are also mean squared error estimators for certain metrics on positive definite operators. In this paper we give a divergence center interpretation for every symmetric Kubo-Ando mean. This characterization of the symmetric means naturally leads to a definition of weighted and multivariate versions of a large class of symmetric Kubo-Ando means. We study elementary properties of these weighted multivariate means, and note in particular that in the special case of the geometric mean we recover the weighted A#H-mean introduced by Kim, Lawson, and Lim."}],"external_id":{"isi":["000581730500011"],"arxiv":["2002.11678"]},"project":[{"call_identifier":"H2020","name":"Geometric study of Wasserstein spaces and free probability","_id":"26A455A6-B435-11E9-9278-68D0E5697425","grant_number":"846294"},{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"date_published":"2021-01-15T00:00:00Z","publisher":"Elsevier","quality_controlled":"1","volume":609,"department":[{"_id":"LaEr"}],"page":"203-217","year":"2021","acknowledgement":"The authors are grateful to Milán Mosonyi for fruitful discussions on the topic, and to the anonymous referee for his/her comments and suggestions.\r\nJ. Pitrik was supported by the Hungarian Academy of Sciences Lendület-Momentum Grant for Quantum Information Theory, No. 96 141, and by Hungarian National Research, Development and Innovation Office (NKFIH) via grants no. K119442, no. K124152, and no. KH129601. D. Virosztek was supported by the ISTFELLOW program of the Institute of Science and Technology Austria (project code IC1027FELL01), by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 846294, and partially supported by the Hungarian National Research, Development and Innovation Office (NKFIH) via grants no. K124152, and no. KH129601.","keyword":["Kubo-Ando mean","weighted multivariate mean","barycenter"],"citation":{"apa":"Pitrik, J., &#38; Virosztek, D. (2021). A divergence center interpretation of general symmetric Kubo-Ando means, and related weighted multivariate operator means. <i>Linear Algebra and Its Applications</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.laa.2020.09.007\">https://doi.org/10.1016/j.laa.2020.09.007</a>","mla":"Pitrik, József, and Daniel Virosztek. “A Divergence Center Interpretation of General Symmetric Kubo-Ando Means, and Related Weighted Multivariate Operator Means.” <i>Linear Algebra and Its Applications</i>, vol. 609, Elsevier, 2021, pp. 203–17, doi:<a href=\"https://doi.org/10.1016/j.laa.2020.09.007\">10.1016/j.laa.2020.09.007</a>.","ista":"Pitrik J, Virosztek D. 2021. A divergence center interpretation of general symmetric Kubo-Ando means, and related weighted multivariate operator means. Linear Algebra and its Applications. 609, 203–217.","chicago":"Pitrik, József, and Daniel Virosztek. “A Divergence Center Interpretation of General Symmetric Kubo-Ando Means, and Related Weighted Multivariate Operator Means.” <i>Linear Algebra and Its Applications</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.laa.2020.09.007\">https://doi.org/10.1016/j.laa.2020.09.007</a>.","short":"J. Pitrik, D. Virosztek, Linear Algebra and Its Applications 609 (2021) 203–217.","ama":"Pitrik J, Virosztek D. A divergence center interpretation of general symmetric Kubo-Ando means, and related weighted multivariate operator means. <i>Linear Algebra and its Applications</i>. 2021;609:203-217. doi:<a href=\"https://doi.org/10.1016/j.laa.2020.09.007\">10.1016/j.laa.2020.09.007</a>","ieee":"J. Pitrik and D. Virosztek, “A divergence center interpretation of general symmetric Kubo-Ando means, and related weighted multivariate operator means,” <i>Linear Algebra and its Applications</i>, vol. 609. Elsevier, pp. 203–217, 2021."},"date_created":"2020-09-11T08:35:50Z","ec_funded":1,"article_type":"original","_id":"8373","doi":"10.1016/j.laa.2020.09.007"},{"title":"Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits","issue":"1","article_processing_charge":"No","oa":1,"date_updated":"2023-09-26T10:36:14Z","scopus_import":"1","intvolume":"        12","has_accepted_license":"1","isi":1,"language":[{"iso":"eng"}],"publication":"Nature Communications","author":[{"full_name":"Patxot, Marion","first_name":"Marion","last_name":"Patxot"},{"first_name":"Daniel","full_name":"Trejo Banos, Daniel","last_name":"Trejo Banos"},{"full_name":"Kousathanas, Athanasios","first_name":"Athanasios","last_name":"Kousathanas"},{"last_name":"Orliac","full_name":"Orliac, Etienne J","first_name":"Etienne J"},{"last_name":"Ojavee","first_name":"Sven E","full_name":"Ojavee, Sven E"},{"last_name":"Moser","full_name":"Moser, Gerhard","first_name":"Gerhard"},{"first_name":"Julia","full_name":"Sidorenko, Julia","last_name":"Sidorenko"},{"first_name":"Zoltan","full_name":"Kutalik, Zoltan","last_name":"Kutalik"},{"last_name":"Magi","full_name":"Magi, Reedik","first_name":"Reedik"},{"first_name":"Peter M","full_name":"Visscher, Peter M","last_name":"Visscher"},{"full_name":"Ronnegard, Lars","first_name":"Lars","last_name":"Ronnegard"},{"first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson","orcid":"0000-0001-8982-8813"}],"file":[{"checksum":"384681be17aff902c149a48f52d13d4f","file_name":"2021_NatComm_Paxtot.pdf","file_id":"10419","success":1,"date_updated":"2021-12-06T07:47:11Z","date_created":"2021-12-06T07:47:11Z","creator":"cchlebak","file_size":6519771,"relation":"main_file","content_type":"application/pdf","access_level":"open_access"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","day":"30","oa_version":"Published Version","month":"11","publication_identifier":{"eissn":["2041-1723"]},"type":"journal_article","status":"public","date_created":"2020-09-17T10:52:38Z","citation":{"short":"M. Patxot, D. Trejo Banos, A. Kousathanas, E.J. Orliac, S.E. Ojavee, G. Moser, J. Sidorenko, Z. Kutalik, R. Magi, P.M. Visscher, L. Ronnegard, M.R. Robinson, Nature Communications 12 (2021).","ieee":"M. Patxot <i>et al.</i>, “Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits,” <i>Nature Communications</i>, vol. 12, no. 1. Springer Nature, 2021.","ama":"Patxot M, Trejo Banos D, Kousathanas A, et al. Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. <i>Nature Communications</i>. 2021;12(1). doi:<a href=\"https://doi.org/10.1038/s41467-021-27258-9\">10.1038/s41467-021-27258-9</a>","mla":"Patxot, Marion, et al. “Probabilistic Inference of the Genetic Architecture Underlying Functional Enrichment of Complex Traits.” <i>Nature Communications</i>, vol. 12, no. 1, 6972, Springer Nature, 2021, doi:<a href=\"https://doi.org/10.1038/s41467-021-27258-9\">10.1038/s41467-021-27258-9</a>.","chicago":"Patxot, Marion, Daniel Trejo Banos, Athanasios Kousathanas, Etienne J Orliac, Sven E Ojavee, Gerhard Moser, Julia Sidorenko, et al. “Probabilistic Inference of the Genetic Architecture Underlying Functional Enrichment of Complex Traits.” <i>Nature Communications</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1038/s41467-021-27258-9\">https://doi.org/10.1038/s41467-021-27258-9</a>.","ista":"Patxot M, Trejo Banos D, Kousathanas A, Orliac EJ, Ojavee SE, Moser G, Sidorenko J, Kutalik Z, Magi R, Visscher PM, Ronnegard L, Robinson MR. 2021. Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. Nature Communications. 12(1), 6972.","apa":"Patxot, M., Trejo Banos, D., Kousathanas, A., Orliac, E. J., Ojavee, S. E., Moser, G., … Robinson, M. R. (2021). Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-021-27258-9\">https://doi.org/10.1038/s41467-021-27258-9</a>"},"year":"2021","acknowledgement":"This project 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 thank the participants of the cohort studies, and the Ecole Polytechnique Federal Lausanne (EPFL) SCITAS for their excellent compute resources, their generosity with their time and the kindness of their support. P.M.V. acknowledges funding from the Australian National Health and Medical Research Council (1113400) and the Australian Research Council (FL180100072). L.R. acknowledges funding from the Kjell & Märta Beijer Foundation (Stockholm, Sweden). We also would like to acknowledge Simone Rubinacci, Oliver Delanau, Alexander Terenin, Eleonora Porcu, and Mike Goddard for their useful comments and suggestions.","article_number":"6972","related_material":{"record":[{"relation":"research_data","id":"13063","status":"public"}]},"_id":"8429","article_type":"original","file_date_updated":"2021-12-06T07:47:11Z","doi":"10.1038/s41467-021-27258-9","publication_status":"published","abstract":[{"lang":"eng","text":"We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32–44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data."}],"external_id":{"isi":["000724450600023"]},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"ddc":["610"],"volume":12,"quality_controlled":"1","department":[{"_id":"MaRo"}],"date_published":"2021-11-30T00:00:00Z","publisher":"Springer Nature"},{"title":"Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis","oa":1,"date_updated":"2023-08-04T11:00:17Z","issue":"1","article_processing_charge":"No","has_accepted_license":"1","scopus_import":"1","intvolume":"        12","isi":1,"language":[{"iso":"eng"}],"publication":"Nature Communications","author":[{"last_name":"Ojavee","first_name":"Sven E","full_name":"Ojavee, Sven E"},{"first_name":"Athanasios","full_name":"Kousathanas, Athanasios","last_name":"Kousathanas"},{"last_name":"Trejo Banos","first_name":"Daniel","full_name":"Trejo Banos, Daniel"},{"full_name":"Orliac, Etienne J","first_name":"Etienne J","last_name":"Orliac"},{"last_name":"Patxot","first_name":"Marion","full_name":"Patxot, Marion"},{"first_name":"Kristi","full_name":"Lall, Kristi","last_name":"Lall"},{"full_name":"Magi, Reedik","first_name":"Reedik","last_name":"Magi"},{"full_name":"Fischer, Krista","first_name":"Krista","last_name":"Fischer"},{"first_name":"Zoltan","full_name":"Kutalik, Zoltan","last_name":"Kutalik"},{"first_name":"Matthew Richard","full_name":"Robinson, Matthew Richard","orcid":"0000-0001-8982-8813","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","last_name":"Robinson"}],"day":"20","oa_version":"Published Version","file":[{"file_name":"2021_nature_communications_Ojavee.pdf","file_id":"9372","checksum":"eca8b9ae713835c5b785211dd08d8a2e","success":1,"date_updated":"2021-05-04T15:07:50Z","date_created":"2021-05-04T15:07:50Z","creator":"kschuh","file_size":6474239,"relation":"main_file","content_type":"application/pdf","access_level":"open_access"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publication_identifier":{"eissn":["20411723"]},"month":"04","type":"journal_article","status":"public","citation":{"chicago":"Ojavee, Sven E, Athanasios Kousathanas, Daniel Trejo Banos, Etienne J Orliac, Marion Patxot, Kristi Lall, Reedik Magi, Krista Fischer, Zoltan Kutalik, and Matthew Richard Robinson. “Genomic Architecture and Prediction of Censored Time-to-Event Phenotypes with a Bayesian Genome-Wide Analysis.” <i>Nature Communications</i>. Nature Research, 2021. <a href=\"https://doi.org/10.1038/s41467-021-22538-w\">https://doi.org/10.1038/s41467-021-22538-w</a>.","ista":"Ojavee SE, Kousathanas A, Trejo Banos D, Orliac EJ, Patxot M, Lall K, Magi R, Fischer K, Kutalik Z, Robinson MR. 2021. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. Nature Communications. 12(1), 2337.","mla":"Ojavee, Sven E., et al. “Genomic Architecture and Prediction of Censored Time-to-Event Phenotypes with a Bayesian Genome-Wide Analysis.” <i>Nature Communications</i>, vol. 12, no. 1, 2337, Nature Research, 2021, doi:<a href=\"https://doi.org/10.1038/s41467-021-22538-w\">10.1038/s41467-021-22538-w</a>.","ama":"Ojavee SE, Kousathanas A, Trejo Banos D, et al. Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. <i>Nature Communications</i>. 2021;12(1). doi:<a href=\"https://doi.org/10.1038/s41467-021-22538-w\">10.1038/s41467-021-22538-w</a>","ieee":"S. E. Ojavee <i>et al.</i>, “Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis,” <i>Nature Communications</i>, vol. 12, no. 1. Nature Research, 2021.","short":"S.E. Ojavee, A. Kousathanas, D. Trejo Banos, E.J. Orliac, M. Patxot, K. Lall, R. Magi, K. Fischer, Z. Kutalik, M.R. Robinson, Nature Communications 12 (2021).","apa":"Ojavee, S. E., Kousathanas, A., Trejo Banos, D., Orliac, E. J., Patxot, M., Lall, K., … Robinson, M. R. (2021). Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis. <i>Nature Communications</i>. Nature Research. <a href=\"https://doi.org/10.1038/s41467-021-22538-w\">https://doi.org/10.1038/s41467-021-22538-w</a>"},"date_created":"2020-09-17T10:53:00Z","acknowledgement":"This project was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science and Technology Austria and the University of Lausanne; the work of KF was supported by the grant PUT1665 by the Estonian Research Council. We would like to thank Mike Goddard for comments which greatly improved the work, the participants of the cohort studies, and the Ecole Polytechnique Federal Lausanne (EPFL) SCITAS for their excellent compute resources, their generosity with their time and the kindness of their support.","year":"2021","related_material":{"link":[{"url":"https://ist.ac.at/en/news/predicting-the-onset-of-diseases/","description":"News on IST Homepage","relation":"press_release"}]},"article_number":"2337","file_date_updated":"2021-05-04T15:07:50Z","_id":"8430","doi":"10.1038/s41467-021-22538-w","publication_status":"published","external_id":{"isi":["000642509600006"]},"abstract":[{"text":"While recent advancements in computation and modelling have improved the analysis of complex traits, our understanding of the genetic basis of the time at symptom onset remains limited. Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-onset phenotypes in a sampling scheme that facilitates biobank-scale time-to-event analyses. We show in extensive simulation work the benefits BayesW provides in terms of number of discoveries, model performance and genomic prediction. In the UK Biobank, we find many thousands of common genomic regions underlying the age-at-onset of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of onset reflecting the underlying genetic liability to disease. Age-at-menopause and age-at-menarche are also highly polygenic, but with higher variance contributed by low frequency variants. Genomic prediction into the Estonian Biobank data shows that BayesW gives higher prediction accuracy than other approaches.","lang":"eng"}],"ddc":["570"],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"project":[{"name":"Improving estimation and prediction of common complex disease risk","grant_number":"PCEGP3_181181","_id":"9B8D11D6-BA93-11EA-9121-9846C619BF3A"}],"department":[{"_id":"MaRo"}],"volume":12,"quality_controlled":"1","publisher":"Nature Research","date_published":"2021-04-20T00:00:00Z"},{"doi":"10.1016/j.neuron.2020.11.028","_id":"8544","article_type":"original","ec_funded":1,"citation":{"ieee":"Y. H. Takeo <i>et al.</i>, “GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells,” <i>Neuron</i>, vol. 109, no. 4. Elsevier, p. P629–644.E8, 2021.","ama":"Takeo YH, Shuster SA, Jiang L, et al. GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells. <i>Neuron</i>. 2021;109(4):P629-644.E8. doi:<a href=\"https://doi.org/10.1016/j.neuron.2020.11.028\">10.1016/j.neuron.2020.11.028</a>","short":"Y.H. Takeo, S.A. Shuster, L. Jiang, M. Hu, D.J. Luginbuhl, T. Rülicke, X. Contreras, S. Hippenmeyer, M.J. Wagner, S. Ganguli, L. Luo, Neuron 109 (2021) P629–644.E8.","ista":"Takeo YH, Shuster SA, Jiang L, Hu M, Luginbuhl DJ, Rülicke T, Contreras X, Hippenmeyer S, Wagner MJ, Ganguli S, Luo L. 2021. GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells. Neuron. 109(4), P629–644.E8.","mla":"Takeo, Yukari H., et al. “GluD2- and Cbln1-Mediated Competitive Synaptogenesis Shapes the Dendritic Arbors of Cerebellar Purkinje Cells.” <i>Neuron</i>, vol. 109, no. 4, Elsevier, 2021, p. P629–644.E8, doi:<a href=\"https://doi.org/10.1016/j.neuron.2020.11.028\">10.1016/j.neuron.2020.11.028</a>.","chicago":"Takeo, Yukari H., S. Andrew Shuster, Linnie Jiang, Miley Hu, David J. Luginbuhl, Thomas Rülicke, Ximena Contreras, et al. “GluD2- and Cbln1-Mediated Competitive Synaptogenesis Shapes the Dendritic Arbors of Cerebellar Purkinje Cells.” <i>Neuron</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.neuron.2020.11.028\">https://doi.org/10.1016/j.neuron.2020.11.028</a>.","apa":"Takeo, Y. H., Shuster, S. A., Jiang, L., Hu, M., Luginbuhl, D. J., Rülicke, T., … Luo, L. (2021). GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells. <i>Neuron</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.neuron.2020.11.028\">https://doi.org/10.1016/j.neuron.2020.11.028</a>"},"date_created":"2020-09-21T11:59:47Z","year":"2021","acknowledgement":"We thank M. Mishina for GluD2fl frozen embryos, T.C. Südhof and J.I. Morgan for Cbln1fl mice, L. Anderson for help in generating the MADM alleles, W. Joo for a previously unpublished construct, M. Yuzaki, K. Shen, J. Ding, and members of the Luo lab, including J.M. Kebschull, H. Li, J. Li, T. Li, C.M. McLaughlin, D. Pederick, J. Ren, D.C. Wang and C. Xu for discussions and critiques of the manuscript, and M. Yuzaki for supporting Y.H.T. during the final phase of this project. Y.H.T. was supported by a JSPS fellowship; S.A.S. was supported by a Stanford Graduate Fellowship and an NSF Predoctoral Fellowship; L.J. is supported by a Stanford Graduate Fellowship and an NSF Predoctoral Fellowship; M.J.W. is supported by a Burroughs Wellcome Fund CASI Award. This work was supported by an NIH grant (R01-NS050538) to L.L.; the European Research Council (ERC) under the European Union's Horizon 2020 research and innovations programme (No. 725780 LinPro) to S.H.; and Simons and James S. McDonnell Foundations and an NSF CAREER award to S.G.; L.L. is an HHMI investigator.","volume":109,"quality_controlled":"1","department":[{"_id":"SiHi"}],"page":"P629-644.E8","date_published":"2021-02-17T00:00:00Z","publisher":"Elsevier","project":[{"_id":"260018B0-B435-11E9-9278-68D0E5697425","grant_number":"725780","name":"Principles of Neural Stem Cell Lineage Progression in Cerebral Cortex Development","call_identifier":"H2020"}],"abstract":[{"lang":"eng","text":"The synaptotrophic hypothesis posits that synapse formation stabilizes dendritic branches, yet this hypothesis has not been causally tested in vivo in the mammalian brain. Presynaptic ligand cerebellin-1 (Cbln1) and postsynaptic receptor GluD2 mediate synaptogenesis between granule cells and Purkinje cells in the molecular layer of the cerebellar cortex. Here we show that sparse but not global knockout of GluD2 causes under-elaboration of Purkinje cell dendrites in the deep molecular layer and overelaboration in the superficial molecular layer. Developmental, overexpression, structure-function, and genetic epistasis analyses indicate that dendrite morphogenesis defects result from competitive synaptogenesis in a Cbln1/GluD2-dependent manner. A generative model of dendritic growth based on competitive synaptogenesis largely recapitulates GluD2 sparse and global knockout phenotypes. Our results support the synaptotrophic hypothesis at initial stages of dendrite development, suggest a second mode in which cumulative synapse formation inhibits further dendrite growth, and highlight the importance of competition in dendrite morphogenesis."}],"publication_status":"published","publication":"Neuron","language":[{"iso":"eng"}],"intvolume":"       109","scopus_import":"1","title":"GluD2- and Cbln1-mediated competitive synaptogenesis shapes the dendritic arbors of cerebellar Purkinje cells","article_processing_charge":"No","issue":"4","oa":1,"date_updated":"2024-03-06T12:12:48Z","status":"public","type":"journal_article","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","main_file_link":[{"url":"https://doi.org/10.1101/2020.06.14.151258","open_access":"1"}],"day":"17","month":"02","publication_identifier":{"eissn":["1097-4199"]},"author":[{"last_name":"Takeo","first_name":"Yukari H.","full_name":"Takeo, Yukari H."},{"full_name":"Shuster, S. Andrew","first_name":"S. Andrew","last_name":"Shuster"},{"first_name":"Linnie","full_name":"Jiang, Linnie","last_name":"Jiang"},{"full_name":"Hu, Miley","first_name":"Miley","last_name":"Hu"},{"last_name":"Luginbuhl","first_name":"David J.","full_name":"Luginbuhl, David J."},{"last_name":"Rülicke","full_name":"Rülicke, Thomas","first_name":"Thomas"},{"full_name":"Contreras, Ximena","first_name":"Ximena","last_name":"Contreras","id":"475990FE-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0003-2279-1061","last_name":"Hippenmeyer","id":"37B36620-F248-11E8-B48F-1D18A9856A87","full_name":"Hippenmeyer, Simon","first_name":"Simon"},{"last_name":"Wagner","first_name":"Mark J.","full_name":"Wagner, Mark J."},{"last_name":"Ganguli","full_name":"Ganguli, Surya","first_name":"Surya"},{"last_name":"Luo","first_name":"Liqun","full_name":"Luo, Liqun"}]},{"article_number":"109208","related_material":{"link":[{"relation":"earlier_version","url":"https://doi.org/10.1101/2020.03.18.997205"}]},"ec_funded":1,"year":"2021","acknowledgement":"This work was supported by the program “Investissements d’avenir” ANR-10-IAIHU-06 , ICM , a Sorbonne Université Emergence grant, an Allen Distinguished Investigator Award , and the Roger De Spoelberch Foundation Prize (to B.A.H.); Armenise-Harvard Foundation , AIRC , and CARITRO (to L.T.); and the European Research Council under the European Union’s Horizon 2020 research and innovation programme grant agreement no. 725780 LinPro (to S.H.). T.Z. and T.L. were supported by doctoral fellowships from the China Scholarship Council and A.H.H. by a doctoral DOC fellowship of the Austrian Academy of Sciences ( 24812 ). All animal work was conducted at the PHENO-ICMice facility. The Core is supported by 2 “Investissements d’avenir” (ANR-10- IAIHU-06 and ANR-11-INBS-0011-NeurATRIS) and the “Fondation pour la Recherche Médicale.” Light microscopy work was carried out at ICM’s imaging core facility, ICM.Quant, and analysis of scRNA-seq data was carried out at ICM’s bioinformatics core facility, iCONICS. We thank Paulina Ejsmont, Natalia Danda, and Nathalie De Geest for technical support. We are grateful to Dr. Shahragim TAJBAKHSH for providing R26Rstop-NICD-nGFP transgenic mice, Dr. Bart De Strooper for Psn1-deficient mice, Dr. Jean-Christophe Marine for Gt(ROSA)26SortdTom reporter mice, and Dr. Martinez Barbera for Sox2CreERT2 mice. We also give thanks to Dr. Mikio Hoshino for providing Atoh1 and Ptf1a antibodies. B.A.H. is an Einstein Visiting Fellow of the Berlin Institute of Health .","date_created":"2020-09-21T12:00:48Z","citation":{"apa":"Zhang, T., Liu, T., Mora, N., Guegan, J., Bertrand, M., Contreras, X., … Hassan, B. A. (2021). Generation of excitatory and inhibitory neurons from common progenitors via Notch signaling in the cerebellum. <i>Cell Reports</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.celrep.2021.109208\">https://doi.org/10.1016/j.celrep.2021.109208</a>","mla":"Zhang, Tingting, et al. “Generation of Excitatory and Inhibitory Neurons from Common Progenitors via Notch Signaling in the Cerebellum.” <i>Cell Reports</i>, vol. 35, no. 10, 109208, Elsevier, 2021, doi:<a href=\"https://doi.org/10.1016/j.celrep.2021.109208\">10.1016/j.celrep.2021.109208</a>.","ista":"Zhang T, Liu T, Mora N, Guegan J, Bertrand M, Contreras X, Hansen AH, Streicher C, Anderle M, Danda N, Tiberi L, Hippenmeyer S, Hassan BA. 2021. Generation of excitatory and inhibitory neurons from common progenitors via Notch signaling in the cerebellum. Cell Reports. 35(10), 109208.","chicago":"Zhang, Tingting, Tengyuan Liu, Natalia Mora, Justine Guegan, Mathilde Bertrand, Ximena Contreras, Andi H Hansen, et al. “Generation of Excitatory and Inhibitory Neurons from Common Progenitors via Notch Signaling in the Cerebellum.” <i>Cell Reports</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.celrep.2021.109208\">https://doi.org/10.1016/j.celrep.2021.109208</a>.","short":"T. Zhang, T. Liu, N. Mora, J. Guegan, M. Bertrand, X. Contreras, A.H. Hansen, C. Streicher, M. Anderle, N. Danda, L. Tiberi, S. Hippenmeyer, B.A. Hassan, Cell Reports 35 (2021).","ama":"Zhang T, Liu T, Mora N, et al. Generation of excitatory and inhibitory neurons from common progenitors via Notch signaling in the cerebellum. <i>Cell Reports</i>. 2021;35(10). doi:<a href=\"https://doi.org/10.1016/j.celrep.2021.109208\">10.1016/j.celrep.2021.109208</a>","ieee":"T. Zhang <i>et al.</i>, “Generation of excitatory and inhibitory neurons from common progenitors via Notch signaling in the cerebellum,” <i>Cell Reports</i>, vol. 35, no. 10. Elsevier, 2021."},"doi":"10.1016/j.celrep.2021.109208","article_type":"original","_id":"8546","file_date_updated":"2021-06-15T14:01:35Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"},"ddc":["570"],"abstract":[{"text":"Brain neurons arise from relatively few progenitors generating an enormous diversity of neuronal types. Nonetheless, a cardinal feature of mammalian brain neurogenesis is thought to be that excitatory and inhibitory neurons derive from separate, spatially segregated progenitors. Whether bi-potential progenitors with an intrinsic capacity to generate both lineages exist and how such a fate decision may be regulated are unknown. Using cerebellar development as a model, we discover that individual progenitors can give rise to both inhibitory and excitatory lineages. Gradations of Notch activity determine the fates of the progenitors and their daughters. Daughters with the highest levels of Notch activity retain the progenitor fate, while intermediate levels of Notch activity generate inhibitory neurons, and daughters with very low levels of Notch signaling adopt the excitatory fate. Therefore, Notch-mediated binary cell fate choice is a mechanism for regulating the ratio of excitatory to inhibitory neurons from common progenitors.","lang":"eng"}],"external_id":{"pmid":["34107249 "],"isi":["000659894300001"]},"publication_status":"published","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","date_published":"2021-06-08T00:00:00Z","publisher":"Elsevier","volume":35,"quality_controlled":"1","department":[{"_id":"SiHi"}],"project":[{"_id":"260018B0-B435-11E9-9278-68D0E5697425","grant_number":"725780","call_identifier":"H2020","name":"Principles of Neural Stem Cell Lineage Progression in Cerebral Cortex Development"},{"_id":"2625A13E-B435-11E9-9278-68D0E5697425","grant_number":"24812","name":"Molecular Mechanisms of Radial Neuronal Migration"}],"isi":1,"intvolume":"        35","scopus_import":"1","has_accepted_license":"1","issue":"10","article_processing_charge":"No","oa":1,"date_updated":"2023-08-04T11:00:48Z","title":"Generation of excitatory and inhibitory neurons from common progenitors via Notch signaling in the cerebellum","publication":"Cell Reports","language":[{"iso":"eng"}],"month":"06","publication_identifier":{"eissn":[" 22111247"]},"file":[{"relation":"main_file","file_size":8900385,"access_level":"open_access","content_type":"application/pdf","creator":"cziletti","date_updated":"2021-06-15T14:01:35Z","date_created":"2021-06-15T14:01:35Z","file_name":"2021_CellReports_Zhang.pdf","file_id":"9554","checksum":"7def3d42ebc8f5675efb6f38819e3e2e","success":1}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa_version":"Published Version","day":"08","author":[{"first_name":"Tingting","full_name":"Zhang, Tingting","last_name":"Zhang"},{"first_name":"Tengyuan","full_name":"Liu, Tengyuan","last_name":"Liu"},{"full_name":"Mora, Natalia","first_name":"Natalia","last_name":"Mora"},{"full_name":"Guegan, Justine","first_name":"Justine","last_name":"Guegan"},{"last_name":"Bertrand","full_name":"Bertrand, Mathilde","first_name":"Mathilde"},{"last_name":"Contreras","id":"475990FE-F248-11E8-B48F-1D18A9856A87","full_name":"Contreras, Ximena","first_name":"Ximena"},{"id":"38853E16-F248-11E8-B48F-1D18A9856A87","last_name":"Hansen","first_name":"Andi H","full_name":"Hansen, Andi H"},{"id":"36BCB99C-F248-11E8-B48F-1D18A9856A87","last_name":"Streicher","full_name":"Streicher, Carmen","first_name":"Carmen"},{"last_name":"Anderle","full_name":"Anderle, Marica","first_name":"Marica"},{"last_name":"Danda","full_name":"Danda, Natasha","first_name":"Natasha"},{"last_name":"Tiberi","full_name":"Tiberi, Luca","first_name":"Luca"},{"orcid":"0000-0003-2279-1061","last_name":"Hippenmeyer","id":"37B36620-F248-11E8-B48F-1D18A9856A87","full_name":"Hippenmeyer, Simon","first_name":"Simon"},{"last_name":"Hassan","full_name":"Hassan, Bassem A.","first_name":"Bassem A."}],"status":"public","type":"journal_article","pmid":1}]