[{"language":[{"iso":"eng"}],"article_type":"original","article_processing_charge":"No","department":[{"_id":"UlWa"}],"author":[{"full_name":"Avvakumov, Sergey","last_name":"Avvakumov","id":"3827DAC8-F248-11E8-B48F-1D18A9856A87","first_name":"Sergey"},{"orcid":"0000-0002-1494-0568","last_name":"Wagner","first_name":"Uli","id":"36690CA2-F248-11E8-B48F-1D18A9856A87","full_name":"Wagner, Uli"},{"full_name":"Mabillard, Isaac","last_name":"Mabillard","first_name":"Isaac","id":"32BF9DAA-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Skopenkov, A. B.","first_name":"A. B.","last_name":"Skopenkov"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","title":"Eliminating higher-multiplicity intersections, III. Codimension 2","publisher":"IOP Publishing","publication_status":"published","_id":"9308","quality_controlled":"1","citation":{"apa":"Avvakumov, S., Wagner, U., Mabillard, I., &#38; Skopenkov, A. B. (2020). Eliminating higher-multiplicity intersections, III. Codimension 2. <i>Russian Mathematical Surveys</i>. IOP Publishing. <a href=\"https://doi.org/10.1070/RM9943\">https://doi.org/10.1070/RM9943</a>","mla":"Avvakumov, Sergey, et al. “Eliminating Higher-Multiplicity Intersections, III. Codimension 2.” <i>Russian Mathematical Surveys</i>, vol. 75, no. 6, IOP Publishing, 2020, pp. 1156–58, doi:<a href=\"https://doi.org/10.1070/RM9943\">10.1070/RM9943</a>.","ista":"Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. 2020. Eliminating higher-multiplicity intersections, III. Codimension 2. Russian Mathematical Surveys. 75(6), 1156–1158.","short":"S. Avvakumov, U. Wagner, I. Mabillard, A.B. Skopenkov, Russian Mathematical Surveys 75 (2020) 1156–1158.","ieee":"S. Avvakumov, U. Wagner, I. Mabillard, and A. B. Skopenkov, “Eliminating higher-multiplicity intersections, III. Codimension 2,” <i>Russian Mathematical Surveys</i>, vol. 75, no. 6. IOP Publishing, pp. 1156–1158, 2020.","ama":"Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. Eliminating higher-multiplicity intersections, III. Codimension 2. <i>Russian Mathematical Surveys</i>. 2020;75(6):1156-1158. doi:<a href=\"https://doi.org/10.1070/RM9943\">10.1070/RM9943</a>","chicago":"Avvakumov, Sergey, Uli Wagner, Isaac Mabillard, and A. B. Skopenkov. “Eliminating Higher-Multiplicity Intersections, III. Codimension 2.” <i>Russian Mathematical Surveys</i>. IOP Publishing, 2020. <a href=\"https://doi.org/10.1070/RM9943\">https://doi.org/10.1070/RM9943</a>."},"publication_identifier":{"issn":["0036-0279"]},"type":"journal_article","arxiv":1,"scopus_import":"1","year":"2020","page":"1156-1158","external_id":{"isi":["000625983100001"],"arxiv":["1511.03501"]},"date_updated":"2023-08-14T11:43:54Z","issue":"6","oa":1,"intvolume":"        75","month":"12","date_published":"2020-12-01T00:00:00Z","acknowledgement":"This research was carried out with the support of the Russian Foundation for Basic Research(grant no. 19-01-00169)","related_material":{"record":[{"id":"8183","status":"public","relation":"earlier_version"},{"status":"public","relation":"later_version","id":"10220"}]},"isi":1,"oa_version":"Preprint","main_file_link":[{"url":"https://arxiv.org/abs/1511.03501","open_access":"1"}],"doi":"10.1070/RM9943","publication":"Russian Mathematical Surveys","date_created":"2021-04-04T22:01:22Z","status":"public","day":"01","volume":75},{"status":"public","day":"20","tmp":{"short":"CC BY-NC (4.0)","name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","image":"/images/cc_by_nc.png"},"date_created":"2021-04-14T12:05:20Z","_id":"9326","abstract":[{"text":"The mitochondrial respiratory chain, formed by five protein complexes, utilizes energy from catabolic processes to synthesize ATP. Complex I, the first and the largest protein complex of the chain, harvests electrons from NADH to reduce quinone, while pumping protons across the mitochondrial membrane. Detailed knowledge of the working principle of such coupled charge-transfer processes remains, however, fragmentary due to bottlenecks in understanding redox-driven conformational transitions and their interplay with the hydrated proton pathways. Complex I from Thermus thermophilus encases 16 subunits with nine iron–sulfur clusters, reduced by electrons from NADH. Here, employing the latest crystal structure of T. thermophilus complex I, we have used microsecond-scale molecular dynamics simulations to study the chemo-mechanical coupling between redox changes of the iron–sulfur clusters and conformational transitions across complex I. First, we identify the redox switches within complex I, which allosterically couple the dynamics of the quinone binding pocket to the site of NADH reduction. Second, our free-energy calculations reveal that the affinity of the quinone, specifically menaquinone, for the binding-site is higher than that of its reduced, menaquinol forma design essential for menaquinol release. Remarkably, the barriers to diffusive menaquinone dynamics are lesser than that of the more ubiquitous ubiquinone, and the naphthoquinone headgroup of the former furnishes stronger binding interactions with the pocket, favoring menaquinone for charge transport in T. thermophilus. Our computations are consistent with experimentally validated mutations and hierarchize the key residues into three functional classes, identifying new mutation targets. Third, long-range hydrogen-bond networks connecting the quinone-binding site to the transmembrane subunits are found to be responsible for proton pumping. Put together, the simulations reveal the molecular design principles linking redox reactions to quinone turnover to proton translocation in complex I.","lang":"eng"}],"type":"research_data_reference","citation":{"chicago":"Gupta, Chitrak, Umesh Khaniya, Chun Chan, Francois Dehez, Mrinal Shekhar, M. R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Charge Transfer and Chemo-Mechanical Coupling in Respiratory Complex I.” American Chemical Society, 2020. <a href=\"https://doi.org/10.1021/jacs.9b13450.s002\">https://doi.org/10.1021/jacs.9b13450.s002</a>.","short":"C. Gupta, U. Khaniya, C. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A. Sazanov, C. Chipot, A. Singharoy, (2020).","ieee":"C. Gupta <i>et al.</i>, “Charge transfer and chemo-mechanical coupling in respiratory complex I.” American Chemical Society, 2020.","ista":"Gupta C, Khaniya U, Chan C, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot C, Singharoy A. 2020. Charge transfer and chemo-mechanical coupling in respiratory complex I, American Chemical Society, <a href=\"https://doi.org/10.1021/jacs.9b13450.s002\">10.1021/jacs.9b13450.s002</a>.","ama":"Gupta C, Khaniya U, Chan C, et al. Charge transfer and chemo-mechanical coupling in respiratory complex I. 2020. doi:<a href=\"https://doi.org/10.1021/jacs.9b13450.s002\">10.1021/jacs.9b13450.s002</a>","mla":"Gupta, Chitrak, et al. <i>Charge Transfer and Chemo-Mechanical Coupling in Respiratory Complex I</i>. American Chemical Society, 2020, doi:<a href=\"https://doi.org/10.1021/jacs.9b13450.s002\">10.1021/jacs.9b13450.s002</a>.","apa":"Gupta, C., Khaniya, U., Chan, C., Dehez, F., Shekhar, M., Gunner, M. R., … Singharoy, A. (2020). Charge transfer and chemo-mechanical coupling in respiratory complex I. American Chemical Society. <a href=\"https://doi.org/10.1021/jacs.9b13450.s002\">https://doi.org/10.1021/jacs.9b13450.s002</a>"},"oa_version":"Published Version","related_material":{"record":[{"id":"8040","relation":"used_in_publication","status":"public"}]},"main_file_link":[{"open_access":"1"}],"doi":"10.1021/jacs.9b13450.s002","title":"Charge transfer and chemo-mechanical coupling in respiratory complex I","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Gupta","first_name":"Chitrak","full_name":"Gupta, Chitrak"},{"full_name":"Khaniya, Umesh","last_name":"Khaniya","first_name":"Umesh"},{"full_name":"Chan, Chun","last_name":"Chan","first_name":"Chun"},{"full_name":"Dehez, Francois","first_name":"Francois","last_name":"Dehez"},{"full_name":"Shekhar, Mrinal","first_name":"Mrinal","last_name":"Shekhar"},{"last_name":"Gunner","first_name":"M. R.","full_name":"Gunner, M. R."},{"orcid":"0000-0002-0977-7989","last_name":"Sazanov","id":"338D39FE-F248-11E8-B48F-1D18A9856A87","first_name":"Leonid A","full_name":"Sazanov, Leonid A"},{"first_name":"Christophe","last_name":"Chipot","full_name":"Chipot, Christophe"},{"first_name":"Abhishek","last_name":"Singharoy","full_name":"Singharoy, Abhishek"}],"oa":1,"date_updated":"2023-08-22T07:49:37Z","publisher":"American Chemical Society","date_published":"2020-05-20T00:00:00Z","month":"05","year":"2020","department":[{"_id":"LeSa"}],"license":"https://creativecommons.org/licenses/by-nc/4.0/","article_processing_charge":"No"},{"month":"07","intvolume":"       119","date_published":"2020-07-12T00:00:00Z","oa":1,"date_updated":"2023-02-23T13:57:24Z","conference":{"location":"Online","name":"ICML: International Conference on Machine Learning","start_date":"2020-07-12","end_date":"2020-07-18"},"page":"5533-5543","year":"2020","volume":119,"day":"12","status":"public","date_created":"2021-05-23T22:01:45Z","ddc":["000"],"publication":"37th International Conference on Machine Learning, ICML 2020","oa_version":"Published Version","has_accepted_license":"1","file":[{"success":1,"file_name":"2020_PMLR_Kurtz.pdf","date_created":"2021-05-25T09:51:36Z","date_updated":"2021-05-25T09:51:36Z","file_id":"9421","content_type":"application/pdf","file_size":741899,"creator":"kschuh","access_level":"open_access","checksum":"2aaaa7d7226e49161311d91627cf783b","relation":"main_file"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","title":"Inducing and exploiting activation sparsity for fast neural network inference","author":[{"full_name":"Kurtz, Mark","first_name":"Mark","last_name":"Kurtz"},{"full_name":"Kopinsky, Justin","first_name":"Justin","last_name":"Kopinsky"},{"last_name":"Gelashvili","first_name":"Rati","full_name":"Gelashvili, Rati"},{"full_name":"Matveev, Alexander","first_name":"Alexander","last_name":"Matveev"},{"last_name":"Carr","first_name":"John","full_name":"Carr, John"},{"full_name":"Goin, Michael","last_name":"Goin","first_name":"Michael"},{"last_name":"Leiserson","first_name":"William","full_name":"Leiserson, William"},{"full_name":"Moore, Sage","last_name":"Moore","first_name":"Sage"},{"full_name":"Nell, Bill","last_name":"Nell","first_name":"Bill"},{"last_name":"Shavit","first_name":"Nir","full_name":"Shavit, Nir"},{"full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian"}],"department":[{"_id":"DaAl"}],"article_processing_charge":"No","language":[{"iso":"eng"}],"file_date_updated":"2021-05-25T09:51:36Z","type":"conference","scopus_import":"1","abstract":[{"lang":"eng","text":"Optimizing convolutional neural networks for fast inference has recently become an extremely active area of research. One of the go-to solutions in this context is weight pruning, which aims to reduce computational and memory footprint by removing large subsets of the connections in a neural network. Surprisingly, much less attention has been given to exploiting sparsity in the activation maps, which tend to be naturally sparse in many settings thanks to the structure of rectified linear (ReLU) activation functions. In this paper, we present an in-depth analysis of methods for maximizing the sparsity of the activations in a trained neural network, and show that, when coupled with an efficient sparse-input convolution algorithm, we can leverage this sparsity for significant performance gains. To induce highly sparse activation maps without accuracy loss, we introduce a new regularization technique, coupled with a new threshold-based sparsification method based on a parameterized activation function called Forced-Activation-Threshold Rectified Linear Unit (FATReLU). We examine the impact of our methods on popular image classification models, showing that most architectures can adapt to significantly sparser activation maps without any accuracy loss. Our second contribution is showing that these these compression gains can be translated into inference speedups: we provide a new algorithm to enable fast convolution operations over networks with sparse activations, and show that it can enable significant speedups for end-to-end inference on a range of popular models on the large-scale ImageNet image classification task on modern Intel CPUs, with little or no retraining cost. "}],"citation":{"apa":"Kurtz, M., Kopinsky, J., Gelashvili, R., Matveev, A., Carr, J., Goin, M., … Alistarh, D.-A. (2020). Inducing and exploiting activation sparsity for fast neural network inference. In <i>37th International Conference on Machine Learning, ICML 2020</i> (Vol. 119, pp. 5533–5543). Online.","mla":"Kurtz, Mark, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” <i>37th International Conference on Machine Learning, ICML 2020</i>, vol. 119, 2020, pp. 5533–43.","ama":"Kurtz M, Kopinsky J, Gelashvili R, et al. Inducing and exploiting activation sparsity for fast neural network inference. In: <i>37th International Conference on Machine Learning, ICML 2020</i>. Vol 119. ; 2020:5533-5543.","ieee":"M. Kurtz <i>et al.</i>, “Inducing and exploiting activation sparsity for fast neural network inference,” in <i>37th International Conference on Machine Learning, ICML 2020</i>, Online, 2020, vol. 119, pp. 5533–5543.","ista":"Kurtz M, Kopinsky J, Gelashvili R, Matveev A, Carr J, Goin M, Leiserson W, Moore S, Nell B, Shavit N, Alistarh D-A. 2020. Inducing and exploiting activation sparsity for fast neural network inference. 37th International Conference on Machine Learning, ICML 2020. ICML: International Conference on Machine Learning vol. 119, 5533–5543.","short":"M. Kurtz, J. Kopinsky, R. Gelashvili, A. Matveev, J. Carr, M. Goin, W. Leiserson, S. Moore, B. Nell, N. Shavit, D.-A. Alistarh, in:, 37th International Conference on Machine Learning, ICML 2020, 2020, pp. 5533–5543.","chicago":"Kurtz, Mark, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” In <i>37th International Conference on Machine Learning, ICML 2020</i>, 119:5533–43, 2020."},"publication_identifier":{"issn":["2640-3498"]},"quality_controlled":"1","_id":"9415"},{"date_updated":"2021-12-14T07:51:15Z","issue":"2","oa":1,"month":"01","intvolume":"        77","date_published":"2020-01-16T00:00:00Z","year":"2020","external_id":{"pmid":["31732458"]},"page":"310-323.e7","date_created":"2021-06-08T06:37:09Z","status":"public","day":"16","pmid":1,"volume":77,"oa_version":"Published Version","doi":"10.1016/j.molcel.2019.10.011","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.molcel.2019.10.011"}],"publication":"Molecular Cell","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","title":"DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts","author":[{"first_name":"Jaemyung","last_name":"Choi","full_name":"Choi, Jaemyung"},{"first_name":"David B.","last_name":"Lyons","full_name":"Lyons, David B."},{"full_name":"Kim, M. Yvonne","last_name":"Kim","first_name":"M. Yvonne"},{"last_name":"Moore","first_name":"Jonathan D.","full_name":"Moore, Jonathan D."},{"full_name":"Zilberman, Daniel","last_name":"Zilberman","orcid":"0000-0002-0123-8649","first_name":"Daniel","id":"6973db13-dd5f-11ea-814e-b3e5455e9ed1"}],"publisher":"Elsevier","language":[{"iso":"eng"}],"article_type":"original","article_processing_charge":"No","department":[{"_id":"DaZi"}],"_id":"9526","quality_controlled":"1","citation":{"short":"J. Choi, D.B. Lyons, M.Y. Kim, J.D. Moore, D. Zilberman, Molecular Cell 77 (2020) 310–323.e7.","ista":"Choi J, Lyons DB, Kim MY, Moore JD, Zilberman D. 2020. DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts. Molecular Cell. 77(2), 310–323.e7.","ieee":"J. Choi, D. B. Lyons, M. Y. Kim, J. D. Moore, and D. Zilberman, “DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts,” <i>Molecular Cell</i>, vol. 77, no. 2. Elsevier, p. 310–323.e7, 2020.","ama":"Choi J, Lyons DB, Kim MY, Moore JD, Zilberman D. DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts. <i>Molecular Cell</i>. 2020;77(2):310-323.e7. doi:<a href=\"https://doi.org/10.1016/j.molcel.2019.10.011\">10.1016/j.molcel.2019.10.011</a>","chicago":"Choi, Jaemyung, David B. Lyons, M. Yvonne Kim, Jonathan D. Moore, and Daniel Zilberman. “DNA Methylation and Histone H1 Jointly Repress Transposable Elements and Aberrant Intragenic Transcripts.” <i>Molecular Cell</i>. Elsevier, 2020. <a href=\"https://doi.org/10.1016/j.molcel.2019.10.011\">https://doi.org/10.1016/j.molcel.2019.10.011</a>.","apa":"Choi, J., Lyons, D. B., Kim, M. Y., Moore, J. D., &#38; Zilberman, D. (2020). DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts. <i>Molecular Cell</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.molcel.2019.10.011\">https://doi.org/10.1016/j.molcel.2019.10.011</a>","mla":"Choi, Jaemyung, et al. “DNA Methylation and Histone H1 Jointly Repress Transposable Elements and Aberrant Intragenic Transcripts.” <i>Molecular Cell</i>, vol. 77, no. 2, Elsevier, 2020, p. 310–323.e7, doi:<a href=\"https://doi.org/10.1016/j.molcel.2019.10.011\">10.1016/j.molcel.2019.10.011</a>."},"extern":"1","publication_identifier":{"issn":["1097-2765"],"eissn":["1097-4164"]},"type":"journal_article","abstract":[{"text":"DNA methylation and histone H1 mediate transcriptional silencing of genes and transposable elements, but how they interact is unclear. In plants and animals with mosaic genomic methylation, functionally mysterious methylation is also common within constitutively active housekeeping genes. Here, we show that H1 is enriched in methylated sequences, including genes, of Arabidopsis thaliana, yet this enrichment is independent of DNA methylation. Loss of H1 disperses heterochromatin, globally alters nucleosome organization, and activates H1-bound genes, but only weakly de-represses transposable elements. However, H1 loss strongly activates transposable elements hypomethylated through mutation of DNA methyltransferase MET1. Hypomethylation of genes also activates antisense transcription, which is modestly enhanced by H1 loss. Our results demonstrate that H1 and DNA methylation jointly maintain transcriptional homeostasis by silencing transposable elements and aberrant intragenic transcripts. Such functionality plausibly explains why DNA methylation, a well-known mutagen, has been maintained within coding sequences of crucial plant and animal genes.","lang":"eng"}],"scopus_import":"1","publication_status":"published"},{"date_updated":"2021-08-11T12:26:34Z","oa":1,"issue":"2","date_published":"2020-12-14T00:00:00Z","intvolume":"        11","month":"12","acknowledgement":"This research is partially supported by the Office of Naval Research, through grant no. N62909-18-1-2038, and the DFG Collaborative Research Center TRR 109, ‘Discretization in Geometry and Dynamics’, through grant no. I02979-N35 of the Austrian Science Fund (FWF).","year":"2020","page":"162-182","license":"https://creativecommons.org/licenses/by/3.0/","ddc":["510","000"],"tmp":{"short":"CC BY (3.0)","name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode","image":"/images/cc_by.png"},"date_created":"2021-07-04T22:01:26Z","status":"public","day":"14","volume":11,"oa_version":"Published Version","publication":"Journal of Computational Geometry","doi":"10.20382/jocg.v11i2a7","title":"Topological data analysis in information space","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","author":[{"full_name":"Edelsbrunner, Herbert","first_name":"Herbert","id":"3FB178DA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-9823-6833","last_name":"Edelsbrunner"},{"first_name":"Ziga","id":"2E36B656-F248-11E8-B48F-1D18A9856A87","last_name":"Virk","full_name":"Virk, Ziga"},{"first_name":"Hubert","id":"379CA8B8-F248-11E8-B48F-1D18A9856A87","last_name":"Wagner","full_name":"Wagner, Hubert"}],"file":[{"success":1,"file_name":"2020_JournalOfComputationalGeometry_Edelsbrunner.pdf","date_created":"2021-08-11T11:55:11Z","date_updated":"2021-08-11T11:55:11Z","file_id":"9882","content_type":"application/pdf","file_size":1449234,"creator":"asandaue","relation":"main_file","access_level":"open_access","checksum":"f02d0b2b3838e7891a6c417fc34ffdcd"}],"has_accepted_license":"1","publisher":"Carleton University","language":[{"iso":"eng"}],"article_type":"original","article_processing_charge":"Yes","department":[{"_id":"HeEd"}],"_id":"9630","quality_controlled":"1","citation":{"mla":"Edelsbrunner, Herbert, et al. “Topological Data Analysis in Information Space.” <i>Journal of Computational Geometry</i>, vol. 11, no. 2, Carleton University, 2020, pp. 162–82, doi:<a href=\"https://doi.org/10.20382/jocg.v11i2a7\">10.20382/jocg.v11i2a7</a>.","apa":"Edelsbrunner, H., Virk, Z., &#38; Wagner, H. (2020). Topological data analysis in information space. <i>Journal of Computational Geometry</i>. Carleton University. <a href=\"https://doi.org/10.20382/jocg.v11i2a7\">https://doi.org/10.20382/jocg.v11i2a7</a>","chicago":"Edelsbrunner, Herbert, Ziga Virk, and Hubert Wagner. “Topological Data Analysis in Information Space.” <i>Journal of Computational Geometry</i>. Carleton University, 2020. <a href=\"https://doi.org/10.20382/jocg.v11i2a7\">https://doi.org/10.20382/jocg.v11i2a7</a>.","ama":"Edelsbrunner H, Virk Z, Wagner H. Topological data analysis in information space. <i>Journal of Computational Geometry</i>. 2020;11(2):162-182. doi:<a href=\"https://doi.org/10.20382/jocg.v11i2a7\">10.20382/jocg.v11i2a7</a>","ieee":"H. Edelsbrunner, Z. Virk, and H. Wagner, “Topological data analysis in information space,” <i>Journal of Computational Geometry</i>, vol. 11, no. 2. Carleton University, pp. 162–182, 2020.","ista":"Edelsbrunner H, Virk Z, Wagner H. 2020. Topological data analysis in information space. Journal of Computational Geometry. 11(2), 162–182.","short":"H. Edelsbrunner, Z. Virk, H. Wagner, Journal of Computational Geometry 11 (2020) 162–182."},"publication_identifier":{"eissn":["1920180X"]},"abstract":[{"text":"Various kinds of data are routinely represented as discrete probability distributions. Examples include text documents summarized by histograms of word occurrences and images represented as histograms of oriented gradients. Viewing a discrete probability distribution as a point in the standard simplex of the appropriate dimension, we can understand collections of such objects in geometric and topological terms.  Importantly, instead of using the standard Euclidean distance, we look into dissimilarity measures with information-theoretic justification, and we develop the theory needed for applying topological data analysis in this setting. In doing so, we emphasize constructions that enable the usage of existing computational topology software in this context.","lang":"eng"}],"scopus_import":"1","file_date_updated":"2021-08-11T11:55:11Z","type":"journal_article","project":[{"grant_number":"I4887","name":"Discretization in Geometry and Dynamics","_id":"0aa4bc98-070f-11eb-9043-e6fff9c6a316"}],"publication_status":"published"},{"publication_status":"published","project":[{"grant_number":"805223","call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning","_id":"268A44D6-B435-11E9-9278-68D0E5697425"}],"arxiv":1,"scopus_import":"1","abstract":[{"lang":"eng","text":"The ability to leverage large-scale hardware parallelism has been one of the key enablers of the accelerated recent progress in machine learning. Consequently, there has been considerable effort invested into developing efficient parallel variants of classic machine learning algorithms. However, despite the wealth of knowledge on parallelization, some classic machine learning algorithms often prove hard to parallelize efficiently while maintaining convergence. In this paper, we focus on efficient parallel algorithms for the key machine learning task of inference on graphical models, in particular on the fundamental belief propagation algorithm. We address the challenge of efficiently parallelizing this classic paradigm by showing how to leverage scalable relaxed schedulers in this context. We present an extensive empirical study, showing that our approach outperforms previous parallel belief propagation implementations both in terms of scalability and in terms of wall-clock convergence time, on a range of practical applications."}],"type":"conference","publication_identifier":{"isbn":["9781713829546"],"issn":["10495258"]},"citation":{"short":"V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 22361–22372.","ieee":"V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation via relaxed scheduling,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2020, vol. 33, pp. 22361–22372.","ista":"Aksenov V, Alistarh D-A, Korhonen J. 2020. Scalable belief propagation via relaxed scheduling. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 22361–22372.","ama":"Aksenov V, Alistarh D-A, Korhonen J. Scalable belief propagation via relaxed scheduling. In: <i>Advances in Neural Information Processing Systems</i>. Vol 33. Curran Associates; 2020:22361-22372.","chicago":"Aksenov, Vitaly, Dan-Adrian Alistarh, and Janne Korhonen. “Scalable Belief Propagation via Relaxed Scheduling.” In <i>Advances in Neural Information Processing Systems</i>, 33:22361–72. Curran Associates, 2020.","apa":"Aksenov, V., Alistarh, D.-A., &#38; Korhonen, J. (2020). Scalable belief propagation via relaxed scheduling. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 22361–22372). Vancouver, Canada: Curran Associates.","mla":"Aksenov, Vitaly, et al. “Scalable Belief Propagation via Relaxed Scheduling.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, Curran Associates, 2020, pp. 22361–72."},"quality_controlled":"1","_id":"9631","department":[{"_id":"DaAl"}],"article_processing_charge":"No","language":[{"iso":"eng"}],"publisher":"Curran Associates","title":"Scalable belief propagation via relaxed scheduling","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","author":[{"last_name":"Aksenov","first_name":"Vitaly","full_name":"Aksenov, Vitaly"},{"first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian"},{"id":"C5402D42-15BC-11E9-A202-CA2BE6697425","first_name":"Janne","last_name":"Korhonen","full_name":"Korhonen, Janne"}],"publication":"Advances in Neural Information Processing Systems","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/fdb2c3bab9d0701c4a050a4d8d782c7f-Abstract.html"}],"oa_version":"Published Version","ec_funded":1,"volume":33,"day":"06","status":"public","date_created":"2021-07-04T22:01:26Z","conference":{"end_date":"2020-12-12","start_date":"2020-12-06","name":"NeurIPS: Conference on Neural Information Processing Systems","location":"Vancouver, Canada"},"page":"22361-22372","external_id":{"arxiv":["2002.11505"]},"year":"2020","acknowledgement":"We thank Marco Mondelli for discussions related to LDPC decoding, and Giorgi Nadiradze for discussions on analysis of relaxed schedulers. This project has received funding from the European Research Council (ERC) under the European\r\nUnion’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML).","date_published":"2020-12-06T00:00:00Z","intvolume":"        33","month":"12","oa":1,"date_updated":"2023-02-23T14:03:03Z"},{"conference":{"location":"Vancouver, Canada","name":"NeurIPS: Conference on Neural Information Processing Systems","start_date":"2020-12-06","end_date":"2020-12-12"},"external_id":{"arxiv":["2004.14340"]},"page":"18098-18109","year":"2020","acknowledgement":"This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). Also, we would like to thank Alexander Shevchenko, Alexandra Peste, and other members of the group for fruitful discussions.","month":"12","intvolume":"        33","date_published":"2020-12-06T00:00:00Z","oa":1,"date_updated":"2023-02-23T14:03:06Z","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html"}],"publication":"Advances in Neural Information Processing Systems","ec_funded":1,"oa_version":"Published Version","volume":33,"day":"06","status":"public","date_created":"2021-07-04T22:01:26Z","department":[{"_id":"DaAl"},{"_id":"ToHe"}],"article_processing_charge":"No","language":[{"iso":"eng"}],"publisher":"Curran Associates","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","author":[{"full_name":"Singh, Sidak Pal","last_name":"Singh","id":"DD138E24-D89D-11E9-9DC0-DEF6E5697425","first_name":"Sidak Pal"},{"orcid":"0000-0003-3650-940X","last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian"}],"title":"WoodFisher: Efficient second-order approximation for neural network compression","publication_status":"published","project":[{"_id":"268A44D6-B435-11E9-9278-68D0E5697425","name":"Elastic Coordination for Scalable Machine Learning","call_identifier":"H2020","grant_number":"805223"}],"type":"conference","arxiv":1,"scopus_import":"1","abstract":[{"lang":"eng","text":"Second-order information, in the form of Hessian- or Inverse-Hessian-vector products, is a fundamental tool for solving optimization problems. Recently, there has been significant interest in utilizing this information in the context of deep\r\nneural networks; however, relatively little is known about the quality of existing approximations in this context. Our work examines this question, identifies issues with existing approaches, and proposes a method called WoodFisher to compute a faithful and efficient estimate of the inverse Hessian. Our main application is to neural network compression, where we build on the classic Optimal Brain Damage/Surgeon framework. We demonstrate that WoodFisher significantly outperforms popular state-of-the-art methods for oneshot pruning. Further, even when iterative, gradual pruning is allowed, our method results in a gain in test accuracy over the state-of-the-art approaches, for standard image classification datasets such as ImageNet ILSVRC. We examine how our method can be extended to take into account first-order information, as well as\r\nillustrate its ability to automatically set layer-wise pruning thresholds and perform compression in the limited-data regime. The code is available at the following link, https://github.com/IST-DASLab/WoodFisher."}],"citation":{"apa":"Singh, S. P., &#38; Alistarh, D.-A. (2020). WoodFisher: Efficient second-order approximation for neural network compression. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 18098–18109). Vancouver, Canada: Curran Associates.","mla":"Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, Curran Associates, 2020, pp. 18098–109.","ama":"Singh SP, Alistarh D-A. WoodFisher: Efficient second-order approximation for neural network compression. In: <i>Advances in Neural Information Processing Systems</i>. Vol 33. Curran Associates; 2020:18098-18109.","ista":"Singh SP, Alistarh D-A. 2020. WoodFisher: Efficient second-order approximation for neural network compression. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 18098–18109.","short":"S.P. Singh, D.-A. Alistarh, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 18098–18109.","ieee":"S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.","chicago":"Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.” In <i>Advances in Neural Information Processing Systems</i>, 33:18098–109. Curran Associates, 2020."},"publication_identifier":{"issn":["10495258"],"isbn":["9781713829546"]},"quality_controlled":"1","_id":"9632"},{"language":[{"iso":"eng"}],"department":[{"_id":"TiVo"}],"article_processing_charge":"No","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","title":"A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network","author":[{"full_name":"Confavreux, Basile J","last_name":"Confavreux","id":"C7610134-B532-11EA-BD9F-F5753DDC885E","first_name":"Basile J"},{"full_name":"Zenke, Friedemann","first_name":"Friedemann","last_name":"Zenke"},{"full_name":"Agnes, Everton J.","last_name":"Agnes","first_name":"Everton J."},{"last_name":"Lillicrap","first_name":"Timothy","full_name":"Lillicrap, Timothy"},{"orcid":"0000-0003-3295-6181","last_name":"Vogels","first_name":"Tim P","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P"}],"publication_status":"published","project":[{"_id":"c084a126-5a5b-11eb-8a69-d75314a70a87","name":"What’s in a memory? Spatiotemporal dynamics in strongly coupled recurrent neuronal networks.","grant_number":"214316/Z/18/Z"},{"call_identifier":"H2020","grant_number":"819603","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"quality_controlled":"1","_id":"9633","type":"conference","abstract":[{"text":"The search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by – and fitted to – experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce. Briefly, we parameterize synaptic plasticity rules by a Volterra expansion and then use supervised learning methods (gradient descent or evolutionary strategies) to minimize a problem-dependent loss function that quantifies how effectively a candidate plasticity rule transforms an initially random network into one with the desired function. We first validate our approach by re-discovering previously described plasticity rules, starting at the single-neuron level and “Oja’s rule”, a simple Hebbian plasticity rule that captures the direction of most variability of inputs to a neuron (i.e., the first principal component). We expand the problem to the network level and ask the framework to find Oja’s rule together with an anti-Hebbian rule such that an initially random two-layer firing-rate network will recover several principal components of the input space after learning. Next, we move to networks of integrate-and-fire neurons with plastic inhibitory afferents. We train for rules that achieve a target firing rate by countering tuned excitation. Our algorithm discovers a specific subset of the manifold of rules that can solve this task. Our work is a proof of principle of an automated and unbiased approach to unveil synaptic plasticity rules that obey biological constraints and can solve complex functions.","lang":"eng"}],"scopus_import":"1","citation":{"chicago":"Confavreux, Basile J, Friedemann Zenke, Everton J. Agnes, Timothy Lillicrap, and Tim P Vogels. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” In <i>Advances in Neural Information Processing Systems</i>, 33:16398–408, 2020.","ama":"Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In: <i>Advances in Neural Information Processing Systems</i>. Vol 33. ; 2020:16398-16408.","short":"B.J. Confavreux, F. Zenke, E.J. Agnes, T. Lillicrap, T.P. Vogels, in:, Advances in Neural Information Processing Systems, 2020, pp. 16398–16408.","ieee":"B. J. Confavreux, F. Zenke, E. J. Agnes, T. Lillicrap, and T. P. Vogels, “A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network,” in <i>Advances in Neural Information Processing Systems</i>, Vancouver, Canada, 2020, vol. 33, pp. 16398–16408.","ista":"Confavreux BJ, Zenke F, Agnes EJ, Lillicrap T, Vogels TP. 2020. A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 16398–16408.","mla":"Confavreux, Basile J., et al. “A Meta-Learning Approach to (Re)Discover Plasticity Rules That Carve a Desired Function into a Neural Network.” <i>Advances in Neural Information Processing Systems</i>, vol. 33, 2020, pp. 16398–408.","apa":"Confavreux, B. J., Zenke, F., Agnes, E. J., Lillicrap, T., &#38; Vogels, T. P. (2020). A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. In <i>Advances in Neural Information Processing Systems</i> (Vol. 33, pp. 16398–16408). Vancouver, Canada."},"publication_identifier":{"issn":["1049-5258"]},"year":"2020","conference":{"location":"Vancouver, Canada","start_date":"2020-12-06","end_date":"2020-12-12","name":"NeurIPS: Conference on Neural Information Processing Systems"},"page":"16398-16408","oa":1,"date_updated":"2023-10-18T09:20:55Z","acknowledgement":"We would like to thank Chaitanya Chintaluri, Georgia Christodoulou, Bill Podlaski and Merima Šabanovic for useful discussions and comments. This work was supported by a Wellcome Trust ´ Senior Research Fellowship (214316/Z/18/Z), a BBSRC grant (BB/N019512/1), an ERC consolidator Grant (SYNAPSEEK), a Leverhulme Trust Project Grant (RPG-2016-446), and funding from École Polytechnique, Paris.","month":"12","intvolume":"        33","date_published":"2020-12-06T00:00:00Z","ec_funded":1,"oa_version":"Published Version","related_material":{"record":[{"id":"14422","relation":"dissertation_contains","status":"public"}],"link":[{"relation":"is_continued_by","url":"https://doi.org/10.1101/2020.10.24.353409"}]},"main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html","open_access":"1"}],"publication":"Advances in Neural Information Processing Systems","day":"06","status":"public","date_created":"2021-07-04T22:01:27Z","volume":33},{"day":"11","status":"public","date_created":"2021-09-13T12:17:11Z","_id":"10012","abstract":[{"lang":"eng","text":"We prove that in the absence of topological changes, the notion of BV solutions to planar multiphase mean curvature flow does not allow for a mechanism for (unphysical) non-uniqueness. Our approach is based on the local structure of the energy landscape near a classical evolution by mean curvature. Mean curvature flow being the gradient flow of the surface energy functional, we develop a gradient-flow analogue of the notion of calibrations. Just like the existence of a calibration guarantees that one has reached a global minimum in the energy landscape, the existence of a \"gradient flow calibration\" ensures that the route of steepest descent in the energy landscape is unique and stable."}],"arxiv":1,"type":"preprint","citation":{"apa":"Fischer, J. L., Hensel, S., Laux, T., &#38; Simon, T. (n.d.). The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions. <i>arXiv</i>.","mla":"Fischer, Julian L., et al. “The Local Structure of the Energy Landscape in Multiphase Mean Curvature Flow: Weak-Strong Uniqueness and Stability of Evolutions.” <i>ArXiv</i>, 2003.05478.","ama":"Fischer JL, Hensel S, Laux T, Simon T. The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions. <i>arXiv</i>.","ieee":"J. L. Fischer, S. Hensel, T. Laux, and T. Simon, “The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions,” <i>arXiv</i>. .","short":"J.L. Fischer, S. Hensel, T. Laux, T. Simon, ArXiv (n.d.).","ista":"Fischer JL, Hensel S, Laux T, Simon T. The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions. arXiv, 2003.05478.","chicago":"Fischer, Julian L, Sebastian Hensel, Tim Laux, and Thilo Simon. “The Local Structure of the Energy Landscape in Multiphase Mean Curvature Flow: Weak-Strong Uniqueness and Stability of Evolutions.” <i>ArXiv</i>, n.d."},"ec_funded":1,"oa_version":"Preprint","related_material":{"record":[{"id":"10007","status":"public","relation":"dissertation_contains"}]},"publication":"arXiv","publication_status":"submitted","main_file_link":[{"url":"https://arxiv.org/abs/2003.05478","open_access":"1"}],"project":[{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program","call_identifier":"H2020","grant_number":"665385"}],"author":[{"id":"2C12A0B0-F248-11E8-B48F-1D18A9856A87","first_name":"Julian L","last_name":"Fischer","orcid":"0000-0002-0479-558X","full_name":"Fischer, Julian L"},{"id":"4D23B7DA-F248-11E8-B48F-1D18A9856A87","first_name":"Sebastian","last_name":"Hensel","orcid":"0000-0001-7252-8072","full_name":"Hensel, Sebastian"},{"full_name":"Laux, Tim","last_name":"Laux","first_name":"Tim"},{"full_name":"Simon, Thilo","first_name":"Thilo","last_name":"Simon"}],"oa":1,"title":"The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_updated":"2023-09-07T13:30:45Z","article_number":"2003.05478","acknowledgement":"Parts of the paper were written during the visit of the authors to the Hausdorff Research Institute for Mathematics (HIM), University of Bonn, in the framework of the trimester program “Evolution of Interfaces”. The support and the hospitality of HIM are gratefully acknowledged. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 665385.","date_published":"2020-03-11T00:00:00Z","month":"03","year":"2020","language":[{"iso":"eng"}],"department":[{"_id":"JuFi"}],"article_processing_charge":"No","external_id":{"arxiv":["2003.05478"]}},{"article_number":"2008.10962","acknowledgement":"This work is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 716117) and by the Austrian Science Fund (FWF), grants No F65 and W1245.","date_published":"2020-08-25T00:00:00Z","month":"08","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","oa":1,"title":"Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions","author":[{"full_name":"Forkert, Dominik L","first_name":"Dominik L","id":"35C79D68-F248-11E8-B48F-1D18A9856A87","last_name":"Forkert"},{"first_name":"Jan","id":"4C5696CE-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0845-1338","last_name":"Maas","full_name":"Maas, Jan"},{"last_name":"Portinale","id":"30AD2CBC-F248-11E8-B48F-1D18A9856A87","first_name":"Lorenzo","full_name":"Portinale, Lorenzo"}],"date_updated":"2023-09-07T13:31:05Z","department":[{"_id":"JaMa"}],"external_id":{"arxiv":["2008.10962"]},"article_processing_charge":"No","page":"33","year":"2020","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"We consider finite-volume approximations of Fokker-Planck equations on bounded convex domains in R^d and study the corresponding gradient flow structures. We reprove the convergence of the discrete to continuous Fokker-Planck equation via the method of Evolutionary Γ-convergence, i.e., we pass to the limit at the level of the gradient flow structures, generalising the one-dimensional result obtained by Disser and Liero. The proof is of variational nature and relies on a Mosco convergence result for functionals in the discrete-to-continuum limit that is of independent interest. Our results apply to arbitrary regular meshes, even though the associated discrete transport distances may fail to converge to the Wasserstein distance in this generality."}],"arxiv":1,"type":"preprint","citation":{"chicago":"Forkert, Dominik L, Jan Maas, and Lorenzo Portinale. “Evolutionary Γ-Convergence of Entropic Gradient Flow Structures for Fokker-Planck Equations in Multiple Dimensions.” <i>ArXiv</i>, n.d.","short":"D.L. Forkert, J. Maas, L. Portinale, ArXiv (n.d.).","ista":"Forkert DL, Maas J, Portinale L. Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions. arXiv, 2008.10962.","ieee":"D. L. Forkert, J. Maas, and L. Portinale, “Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions,” <i>arXiv</i>. .","ama":"Forkert DL, Maas J, Portinale L. Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions. <i>arXiv</i>.","mla":"Forkert, Dominik L., et al. “Evolutionary Γ-Convergence of Entropic Gradient Flow Structures for Fokker-Planck Equations in Multiple Dimensions.” <i>ArXiv</i>, 2008.10962.","apa":"Forkert, D. L., Maas, J., &#38; Portinale, L. (n.d.). Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions. <i>arXiv</i>."},"status":"public","day":"25","date_created":"2021-09-17T10:57:27Z","_id":"10022","publication":"arXiv","publication_status":"submitted","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2008.10962"}],"project":[{"name":"Optimal Transport and Stochastic Dynamics","_id":"256E75B8-B435-11E9-9278-68D0E5697425","grant_number":"716117","call_identifier":"H2020"},{"grant_number":"F6504","name":"Taming Complexity in Partial Differential Systems","_id":"fc31cba2-9c52-11eb-aca3-ff467d239cd2"}],"ec_funded":1,"oa_version":"Preprint","related_material":{"record":[{"id":"11739","status":"public","relation":"later_version"},{"id":"10030","relation":"dissertation_contains","status":"public"}]}},{"publication":"OSA Quantum 2.0 Conference","publication_status":"published","doi":"10.1364/QUANTUM.2020.QTu8A.1","oa_version":"None","publication_identifier":{"isbn":["9-781-5575-2820-9"]},"citation":{"chicago":"Lambert, Nicholas J., Sonia Mobassem, Alfredo R Rueda Sanchez, and Harald G.L. Schwefel. “New Designs and Noise Channels in Electro-Optic Microwave to Optical up-Conversion.” In <i>OSA Quantum 2.0 Conference</i>. Optica Publishing Group, 2020. <a href=\"https://doi.org/10.1364/QUANTUM.2020.QTu8A.1\">https://doi.org/10.1364/QUANTUM.2020.QTu8A.1</a>.","ama":"Lambert NJ, Mobassem S, Rueda Sanchez AR, Schwefel HGL. New designs and noise channels in electro-optic microwave to optical up-conversion. In: <i>OSA Quantum 2.0 Conference</i>. Optica Publishing Group; 2020. doi:<a href=\"https://doi.org/10.1364/QUANTUM.2020.QTu8A.1\">10.1364/QUANTUM.2020.QTu8A.1</a>","ista":"Lambert NJ, Mobassem S, Rueda Sanchez AR, Schwefel HGL. 2020. New designs and noise channels in electro-optic microwave to optical up-conversion. OSA Quantum 2.0 Conference. OSA: Optical Society of America, OSA Technical Digest, , QTu8A.1.","short":"N.J. Lambert, S. Mobassem, A.R. Rueda Sanchez, H.G.L. Schwefel, in:, OSA Quantum 2.0 Conference, Optica Publishing Group, 2020.","ieee":"N. J. Lambert, S. Mobassem, A. R. Rueda Sanchez, and H. G. L. Schwefel, “New designs and noise channels in electro-optic microwave to optical up-conversion,” in <i>OSA Quantum 2.0 Conference</i>, Washington, DC, United States, 2020.","mla":"Lambert, Nicholas J., et al. “New Designs and Noise Channels in Electro-Optic Microwave to Optical up-Conversion.” <i>OSA Quantum 2.0 Conference</i>, QTu8A.1, Optica Publishing Group, 2020, doi:<a href=\"https://doi.org/10.1364/QUANTUM.2020.QTu8A.1\">10.1364/QUANTUM.2020.QTu8A.1</a>.","apa":"Lambert, N. J., Mobassem, S., Rueda Sanchez, A. R., &#38; Schwefel, H. G. L. (2020). New designs and noise channels in electro-optic microwave to optical up-conversion. In <i>OSA Quantum 2.0 Conference</i>. Washington, DC, United States: Optica Publishing Group. <a href=\"https://doi.org/10.1364/QUANTUM.2020.QTu8A.1\">https://doi.org/10.1364/QUANTUM.2020.QTu8A.1</a>"},"scopus_import":"1","abstract":[{"lang":"eng","text":"We discus noise channels in coherent electro-optic up-conversion between microwave and optical fields, in particular due to optical heating. We also report on a novel configuration, which promises to be flexible and highly efficient."}],"type":"conference","_id":"10328","date_created":"2021-11-21T23:01:31Z","day":"01","quality_controlled":"1","status":"public","article_processing_charge":"No","department":[{"_id":"JoFi"}],"conference":{"name":"OSA: Optical Society of America","end_date":"2020-09-17","start_date":"2020-09-14","location":"Washington, DC, United States"},"language":[{"iso":"eng"}],"year":"2020","date_published":"2020-01-01T00:00:00Z","alternative_title":["OSA Technical Digest"],"month":"01","publisher":"Optica Publishing Group","article_number":"QTu8A.1","date_updated":"2023-10-18T08:32:34Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Lambert","first_name":"Nicholas J.","full_name":"Lambert, Nicholas J."},{"full_name":"Mobassem, Sonia","first_name":"Sonia","last_name":"Mobassem"},{"orcid":"0000-0001-6249-5860","last_name":"Rueda Sanchez","id":"3B82B0F8-F248-11E8-B48F-1D18A9856A87","first_name":"Alfredo R","full_name":"Rueda Sanchez, Alfredo R"},{"full_name":"Schwefel, Harald G.L.","first_name":"Harald G.L.","last_name":"Schwefel"}],"title":"New designs and noise channels in electro-optic microwave to optical up-conversion"},{"_id":"10556","quality_controlled":"1","citation":{"mla":"Kokoris Kogias, Eleftherios, et al. “Asynchronous Distributed Key Generation for Computationally-Secure Randomness, Consensus, and Threshold Signatures.” <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i>, Association for Computing Machinery, 2020, pp. 1751–1767, doi:<a href=\"https://doi.org/10.1145/3372297.3423364\">10.1145/3372297.3423364</a>.","apa":"Kokoris Kogias, E., Malkhi, D., &#38; Spiegelman, A. (2020). Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures. In <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i> (pp. 1751–1767). Virtual, United States: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3372297.3423364\">https://doi.org/10.1145/3372297.3423364</a>","chicago":"Kokoris Kogias, Eleftherios, Dahlia Malkhi, and Alexander Spiegelman. “Asynchronous Distributed Key Generation for Computationally-Secure Randomness, Consensus, and Threshold Signatures.” In <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i>, 1751–1767. Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3372297.3423364\">https://doi.org/10.1145/3372297.3423364</a>.","ieee":"E. Kokoris Kogias, D. Malkhi, and A. Spiegelman, “Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures,” in <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i>, Virtual, United States, 2020, pp. 1751–1767.","short":"E. Kokoris Kogias, D. Malkhi, A. Spiegelman, in:, Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, Association for Computing Machinery, 2020, pp. 1751–1767.","ista":"Kokoris Kogias E, Malkhi D, Spiegelman A. 2020. Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures. Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. CCS: Computer and Communications Security, 1751–1767.","ama":"Kokoris Kogias E, Malkhi D, Spiegelman A. Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures. In: <i>Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security</i>. Association for Computing Machinery; 2020:1751–1767. doi:<a href=\"https://doi.org/10.1145/3372297.3423364\">10.1145/3372297.3423364</a>"},"publication_identifier":{"isbn":["978-1-4503-7089-9"]},"scopus_import":"1","abstract":[{"lang":"eng","text":"In this paper, we present the first Asynchronous Distributed Key Generation (ADKG) algorithm which is also the first distributed key generation algorithm that can generate cryptographic keys with a dual (f,2f+1)-threshold (where f is the number of faulty parties). As a result, using our ADKG we remove the trusted setup assumption that the most scalable consensus algorithms make. In order to create a DKG with a dual (f,2f+1)- threshold we first answer in the affirmative the open question posed by Cachin et al. [7] on how to create an Asynchronous Verifiable Secret Sharing (AVSS) protocol with a reconstruction threshold of f+1<k łe 2f+1, which is of independent interest. Our High-threshold-AVSS (HAVSS) uses an asymmetric bivariate polynomial to encode the secret. This enables the reconstruction of the secret only if a set of k nodes contribute while allowing an honest node that did not participate in the sharing phase to recover his share with the help of f+1 honest parties. Once we have HAVSS we can use it to bootstrap scalable partially synchronous consensus protocols, but the question on how to get a DKG in asynchrony remains as we need a way to produce common randomness. The solution comes from a novel Eventually Perfect Common Coin (EPCC) abstraction that enables the generation of a common coin from n concurrent HAVSS invocations. EPCC's key property is that it is eventually reliable, as it might fail to agree at most f times (even if invoked a polynomial number of times). Using EPCC we implement an Eventually Efficient Asynchronous Binary Agreement (EEABA) which is optimal when the EPCC agrees and protects safety when EPCC fails. Finally, using EEABA we construct the first ADKG which has the same overhead and expected runtime as the best partially-synchronous DKG (O(n4) words, O(f) rounds). As a corollary of our ADKG, we can also create the first Validated Asynchronous Byzantine Agreement (VABA) that does not need a trusted dealer to setup threshold signatures of degree n-f. Our VABA has an overhead of expected O(n2) words and O(1) time per instance, after an initial O(n4) words and O(f) time bootstrap via ADKG."}],"type":"conference","publication_status":"published","author":[{"last_name":"Kokoris Kogias","first_name":"Eleftherios","id":"f5983044-d7ef-11ea-ac6d-fd1430a26d30","full_name":"Kokoris Kogias, Eleftherios"},{"first_name":"Dahlia","last_name":"Malkhi","full_name":"Malkhi, Dahlia"},{"full_name":"Spiegelman, Alexander","last_name":"Spiegelman","first_name":"Alexander"}],"title":"Asynchronous distributed key generation for computationally-secure randomness, consensus, and threshold signatures","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publisher":"Association for Computing Machinery","language":[{"iso":"eng"}],"article_processing_charge":"No","department":[{"_id":"ElKo"}],"date_created":"2021-12-16T13:23:27Z","day":"30","status":"public","oa_version":"Preprint","isi":1,"publication":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","main_file_link":[{"open_access":"1","url":"https://eprint.iacr.org/2019/1015"}],"doi":"10.1145/3372297.3423364","date_updated":"2024-02-22T13:10:45Z","oa":1,"date_published":"2020-10-30T00:00:00Z","month":"10","acknowledgement":"We would like to thank Ittai Abraham for the discussions and guidance during the initial conception of the project, especially for HAVSS. Furthermore, we would like to thank the anonymous reviewers for pointing out the relevance of this work to MPC protocols.","year":"2020","page":"1751–1767","external_id":{"isi":["000768470400104"]},"conference":{"name":"CCS: Computer and Communications Security","end_date":"2020-11-13","start_date":"2020-11-09","location":"Virtual, United States"}},{"year":"2020","article_processing_charge":"No","department":[{"_id":"ElKo"}],"ipc":" H04L9/3247 ; G06Q20/29 ; G06Q20/382 ; H04L9/3236","date_updated":"2021-12-21T10:04:50Z","title":"Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods","oa":1,"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","application_date":"2017-06-09","author":[{"full_name":"Ford, Bryan","first_name":"Bryan","last_name":"Ford"},{"first_name":"Linus","last_name":"Gasse","full_name":"Gasse, Linus"},{"first_name":"Eleftherios","id":"f5983044-d7ef-11ea-ac6d-fd1430a26d30","last_name":"Kokoris Kogias","full_name":"Kokoris Kogias, Eleftherios"},{"first_name":"Philipp","last_name":"Jovanovic","full_name":"Jovanovic, Philipp"}],"publication_date":"2020-03-03","date_published":"2020-03-03T00:00:00Z","month":"03","related_material":{"link":[{"relation":"earlier_version","url":"https://patents.google.com/patent/US20180359096A1/en"}]},"oa_version":"Published Version","applicant":["Ecole Polytechnique Federale de Lausanne"],"ipn":"10581613","main_file_link":[{"url":"https://patents.google.com/patent/US10581613B2/en","open_access":"1"}],"date_created":"2021-12-16T13:28:59Z","_id":"10557","status":"public","day":"03","extern":"1","citation":{"ista":"Ford B, Gasse L, Kokoris Kogias E, Jovanovic P. 2020. Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods.","short":"B. Ford, L. Gasse, E. Kokoris Kogias, P. Jovanovic, (2020).","ieee":"B. Ford, L. Gasse, E. Kokoris Kogias, and P. Jovanovic, “Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods.” 2020.","ama":"Ford B, Gasse L, Kokoris Kogias E, Jovanovic P. Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods. 2020.","chicago":"Ford, Bryan, Linus Gasse, Eleftherios Kokoris Kogias, and Philipp Jovanovic. “Cryptographically Verifiable Data Structure Having Multi-Hop Forward and Backwards Links and Associated Systems and Methods,” 2020.","apa":"Ford, B., Gasse, L., Kokoris Kogias, E., &#38; Jovanovic, P. (2020). Cryptographically verifiable data structure having multi-hop forward and backwards links and associated systems and methods.","mla":"Ford, Bryan, et al. <i>Cryptographically Verifiable Data Structure Having Multi-Hop Forward and Backwards Links and Associated Systems and Methods</i>. 2020."},"abstract":[{"text":"Data storage and retrieval systems, methods, and computer-readable media utilize a cryptographically verifiable data structure that facilitates verification of a transaction in a decentralized peer-to-peer environment using multi-hop backwards and forwards links. Backward links are cryptographic hashes of past records. Forward links are cryptographic signatures of future records that are added retroactively to records once the target block has been appended to the data structure.","lang":"eng"}],"type":"patent"},{"keyword":["Multidisciplinary"],"date_updated":"2023-05-08T10:53:55Z","oa":1,"issue":"28","date_published":"2020-05-22T00:00:00Z","month":"05","intvolume":"       117","acknowledgement":"We would like to thank Scott Berry for help with ICU-GFP nuclear localization microscopy, Hao Yu and Lisha Shen for assistance with 6mA DNA methylation analysis, Donna Gibson for graphic design assistance, and members of the C.D. and Howard laboratories for helpful discussions. This work was funded by the European Research Council grants to “MEXTIM” (to C.D.) and “SexMeth” (to X. Feng), by the Biotechnological and Biological Sciences Research Council (BBSRC) Institute Strategic Programmes GRO (BB/J004588/1), GEN (BB/P013511/1), BBSRC grant (to X. Feng) (BB/S009620/1), and the Marie Sklodowska–Curie Postdoctoral Fellowships “UNRAVEL” (to R.H.B.) and \"WISDOM\" (to X. Fang). Additional funding via the Wellcome Trust through a Senior Research Fellowship (to J.R.) (103139) and a multiuser equipment grant (108504). The Wellcome Centre for Cell Biology is supported by core funding from the Wellcome Trust (203149).","year":"2020","page":"16660-16666","external_id":{"pmid":["32601198"]},"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"ddc":["580"],"date_created":"2023-01-16T09:15:44Z","day":"22","status":"public","volume":117,"pmid":1,"oa_version":"Published Version","publication":"Proceedings of the National Academy of Sciences","main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7368280/","open_access":"1"}],"doi":"10.1073/pnas.1920621117","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"The  Arabidopsis epigenetic regulator ICU11 as an accessory protein of polycomb repressive complex 2","author":[{"first_name":"Rebecca H.","last_name":"Bloomer","full_name":"Bloomer, Rebecca H."},{"full_name":"Hutchison, Claire E.","first_name":"Claire E.","last_name":"Hutchison"},{"full_name":"Bäurle, Isabel","last_name":"Bäurle","first_name":"Isabel"},{"first_name":"James","last_name":"Walker","full_name":"Walker, James"},{"full_name":"Fang, Xiaofeng","first_name":"Xiaofeng","last_name":"Fang"},{"full_name":"Perera, Pumi","first_name":"Pumi","last_name":"Perera"},{"first_name":"Christos N.","last_name":"Velanis","full_name":"Velanis, Christos N."},{"first_name":"Serin","last_name":"Gümüs","full_name":"Gümüs, Serin"},{"full_name":"Spanos, Christos","first_name":"Christos","last_name":"Spanos"},{"last_name":"Rappsilber","first_name":"Juri","full_name":"Rappsilber, Juri"},{"first_name":"Xiaoqi","id":"e0164712-22ee-11ed-b12a-d80fcdf35958","last_name":"Feng","orcid":"0000-0002-4008-1234","full_name":"Feng, Xiaoqi"},{"full_name":"Goodrich, Justin","first_name":"Justin","last_name":"Goodrich"},{"last_name":"Dean","first_name":"Caroline","full_name":"Dean, Caroline"}],"file":[{"creator":"alisjak","file_size":1105414,"content_type":"application/pdf","file_id":"12526","relation":"main_file","access_level":"open_access","checksum":"cedee184cb12f454f2fba4158ff47db9","file_name":"2020_PNAS_Bloomer.pdf","success":1,"date_updated":"2023-02-07T11:29:55Z","date_created":"2023-02-07T11:29:55Z"}],"has_accepted_license":"1","publisher":"Proceedings of the National Academy of Sciences","language":[{"iso":"eng"}],"article_type":"original","article_processing_charge":"No","department":[{"_id":"XiFe"}],"_id":"12188","quality_controlled":"1","citation":{"chicago":"Bloomer, Rebecca H., Claire E. Hutchison, Isabel Bäurle, James Walker, Xiaofeng Fang, Pumi Perera, Christos N. Velanis, et al. “The  Arabidopsis Epigenetic Regulator ICU11 as an Accessory Protein of Polycomb Repressive Complex 2.” <i>Proceedings of the National Academy of Sciences</i>. Proceedings of the National Academy of Sciences, 2020. <a href=\"https://doi.org/10.1073/pnas.1920621117\">https://doi.org/10.1073/pnas.1920621117</a>.","ama":"Bloomer RH, Hutchison CE, Bäurle I, et al. The  Arabidopsis epigenetic regulator ICU11 as an accessory protein of polycomb repressive complex 2. <i>Proceedings of the National Academy of Sciences</i>. 2020;117(28):16660-16666. doi:<a href=\"https://doi.org/10.1073/pnas.1920621117\">10.1073/pnas.1920621117</a>","ieee":"R. H. Bloomer <i>et al.</i>, “The  Arabidopsis epigenetic regulator ICU11 as an accessory protein of polycomb repressive complex 2,” <i>Proceedings of the National Academy of Sciences</i>, vol. 117, no. 28. Proceedings of the National Academy of Sciences, pp. 16660–16666, 2020.","ista":"Bloomer RH, Hutchison CE, Bäurle I, Walker J, Fang X, Perera P, Velanis CN, Gümüs S, Spanos C, Rappsilber J, Feng X, Goodrich J, Dean C. 2020. The  Arabidopsis epigenetic regulator ICU11 as an accessory protein of polycomb repressive complex 2. Proceedings of the National Academy of Sciences. 117(28), 16660–16666.","short":"R.H. Bloomer, C.E. Hutchison, I. Bäurle, J. Walker, X. Fang, P. Perera, C.N. Velanis, S. Gümüs, C. Spanos, J. Rappsilber, X. Feng, J. Goodrich, C. Dean, Proceedings of the National Academy of Sciences 117 (2020) 16660–16666.","mla":"Bloomer, Rebecca H., et al. “The  Arabidopsis Epigenetic Regulator ICU11 as an Accessory Protein of Polycomb Repressive Complex 2.” <i>Proceedings of the National Academy of Sciences</i>, vol. 117, no. 28, Proceedings of the National Academy of Sciences, 2020, pp. 16660–66, doi:<a href=\"https://doi.org/10.1073/pnas.1920621117\">10.1073/pnas.1920621117</a>.","apa":"Bloomer, R. H., Hutchison, C. E., Bäurle, I., Walker, J., Fang, X., Perera, P., … Dean, C. (2020). The  Arabidopsis epigenetic regulator ICU11 as an accessory protein of polycomb repressive complex 2. <i>Proceedings of the National Academy of Sciences</i>. Proceedings of the National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1920621117\">https://doi.org/10.1073/pnas.1920621117</a>"},"extern":"1","publication_identifier":{"issn":["0027-8424","1091-6490"]},"abstract":[{"lang":"eng","text":"Molecular mechanisms enabling the switching and maintenance of epigenetic states are not fully understood. Distinct histone modifications are often associated with ON/OFF epigenetic states, but how these states are stably maintained through DNA replication, yet in certain situations switch from one to another remains unclear. Here, we address this problem through identification of Arabidopsis INCURVATA11 (ICU11) as a Polycomb Repressive Complex 2 accessory protein. ICU11 robustly immunoprecipitated in vivo with PRC2 core components and the accessory proteins, EMBRYONIC FLOWER 1 (EMF1), LIKE HETEROCHROMATIN PROTEIN1 (LHP1), and TELOMERE_REPEAT_BINDING FACTORS (TRBs). ICU11 encodes a 2-oxoglutarate-dependent dioxygenase, an activity associated with histone demethylation in other organisms, and mutant plants show defects in multiple aspects of the Arabidopsis epigenome. To investigate its primary molecular function we identified the Arabidopsis FLOWERING LOCUS C (FLC) as a direct target and found icu11 disrupted the cold-induced, Polycomb-mediated silencing underlying vernalization. icu11 prevented reduction in H3K36me3 levels normally seen during the early cold phase, supporting a role for ICU11 in H3K36me3 demethylation. This was coincident with an attenuation of H3K27me3 at the internal nucleation site in FLC, and reduction in H3K27me3 levels across the body of the gene after plants were returned to the warm. Thus, ICU11 is required for the cold-induced epigenetic switching between the mutually exclusive chromatin states at FLC, from the active H3K36me3 state to the silenced H3K27me3 state. These data support the importance of physical coupling of histone modification activities to promote epigenetic switching between opposing chromatin states."}],"scopus_import":"1","type":"journal_article","file_date_updated":"2023-02-07T11:29:55Z","publication_status":"published"},{"language":[{"iso":"eng"}],"article_type":"original","article_processing_charge":"No","department":[{"_id":"XiFe"}],"title":"AXR1 affects DNA methylation independently of its role in regulating meiotic crossover localization","author":[{"first_name":"Nicolas","last_name":"Christophorou","full_name":"Christophorou, Nicolas"},{"first_name":"Wenjing","last_name":"She","full_name":"She, Wenjing"},{"full_name":"Long, Jincheng","first_name":"Jincheng","last_name":"Long"},{"full_name":"Hurel, Aurélie","first_name":"Aurélie","last_name":"Hurel"},{"first_name":"Sébastien","last_name":"Beaubiat","full_name":"Beaubiat, Sébastien"},{"full_name":"Idir, Yassir","last_name":"Idir","first_name":"Yassir"},{"full_name":"Tagliaro-Jahns, Marina","first_name":"Marina","last_name":"Tagliaro-Jahns"},{"first_name":"Aurélie","last_name":"Chambon","full_name":"Chambon, Aurélie"},{"full_name":"Solier, Victor","first_name":"Victor","last_name":"Solier"},{"last_name":"Vezon","first_name":"Daniel","full_name":"Vezon, Daniel"},{"full_name":"Grelon, Mathilde","last_name":"Grelon","first_name":"Mathilde"},{"first_name":"Xiaoqi","id":"e0164712-22ee-11ed-b12a-d80fcdf35958","orcid":"0000-0002-4008-1234","last_name":"Feng","full_name":"Feng, Xiaoqi"},{"last_name":"Bouché","first_name":"Nicolas","full_name":"Bouché, Nicolas"},{"first_name":"Christine","last_name":"Mézard","full_name":"Mézard, Christine"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Public Library of Science (PLoS)","publication_status":"published","_id":"12189","quality_controlled":"1","extern":"1","publication_identifier":{"issn":["1553-7404"]},"citation":{"ieee":"N. Christophorou <i>et al.</i>, “AXR1 affects DNA methylation independently of its role in regulating meiotic crossover localization,” <i>PLOS Genetics</i>, vol. 16, no. 6. Public Library of Science (PLoS), 2020.","short":"N. Christophorou, W. She, J. Long, A. Hurel, S. Beaubiat, Y. Idir, M. Tagliaro-Jahns, A. Chambon, V. Solier, D. Vezon, M. Grelon, X. Feng, N. Bouché, C. Mézard, PLOS Genetics 16 (2020).","ista":"Christophorou N, She W, Long J, Hurel A, Beaubiat S, Idir Y, Tagliaro-Jahns M, Chambon A, Solier V, Vezon D, Grelon M, Feng X, Bouché N, Mézard C. 2020. AXR1 affects DNA methylation independently of its role in regulating meiotic crossover localization. PLOS Genetics. 16(6), e1008894.","ama":"Christophorou N, She W, Long J, et al. AXR1 affects DNA methylation independently of its role in regulating meiotic crossover localization. <i>PLOS Genetics</i>. 2020;16(6). doi:<a href=\"https://doi.org/10.1371/journal.pgen.1008894\">10.1371/journal.pgen.1008894</a>","chicago":"Christophorou, Nicolas, Wenjing She, Jincheng Long, Aurélie Hurel, Sébastien Beaubiat, Yassir Idir, Marina Tagliaro-Jahns, et al. “AXR1 Affects DNA Methylation Independently of Its Role in Regulating Meiotic Crossover Localization.” <i>PLOS Genetics</i>. Public Library of Science (PLoS), 2020. <a href=\"https://doi.org/10.1371/journal.pgen.1008894\">https://doi.org/10.1371/journal.pgen.1008894</a>.","apa":"Christophorou, N., She, W., Long, J., Hurel, A., Beaubiat, S., Idir, Y., … Mézard, C. (2020). AXR1 affects DNA methylation independently of its role in regulating meiotic crossover localization. <i>PLOS Genetics</i>. Public Library of Science (PLoS). <a href=\"https://doi.org/10.1371/journal.pgen.1008894\">https://doi.org/10.1371/journal.pgen.1008894</a>","mla":"Christophorou, Nicolas, et al. “AXR1 Affects DNA Methylation Independently of Its Role in Regulating Meiotic Crossover Localization.” <i>PLOS Genetics</i>, vol. 16, no. 6, e1008894, Public Library of Science (PLoS), 2020, doi:<a href=\"https://doi.org/10.1371/journal.pgen.1008894\">10.1371/journal.pgen.1008894</a>."},"type":"journal_article","abstract":[{"lang":"eng","text":"Meiotic crossovers (COs) are important for reshuffling genetic information between homologous chromosomes and they are essential for their correct segregation. COs are unevenly distributed along chromosomes and the underlying mechanisms controlling CO localization are not well understood. We previously showed that meiotic COs are mis-localized in the absence of AXR1, an enzyme involved in the neddylation/rubylation protein modification pathway in Arabidopsis thaliana. Here, we report that in axr1-/-, male meiocytes show a strong defect in chromosome pairing whereas the formation of the telomere bouquet is not affected. COs are also redistributed towards subtelomeric chromosomal ends where they frequently form clusters, in contrast to large central regions depleted in recombination. The CO suppressed regions correlate with DNA hypermethylation of transposable elements (TEs) in the CHH context in axr1-/- meiocytes. Through examining somatic methylomes, we found axr1-/- affects DNA methylation in a plant, causing hypermethylation in all sequence contexts (CG, CHG and CHH) in TEs. Impairment of the main pathways involved in DNA methylation is epistatic over axr1-/- for DNA methylation in somatic cells but does not restore regular chromosome segregation during meiosis. Collectively, our findings reveal that the neddylation pathway not only regulates hormonal perception and CO distribution but is also, directly or indirectly, a major limiting pathway of TE DNA methylation in somatic cells."}],"scopus_import":"1","year":"2020","external_id":{"pmid":["32598340"]},"date_updated":"2023-05-08T10:54:39Z","keyword":["Cancer Research","Genetics (clinical)","Genetics","Molecular Biology","Ecology","Evolution","Behavior and Systematics"],"issue":"6","oa":1,"intvolume":"        16","month":"06","date_published":"2020-06-29T00:00:00Z","acknowledgement":"The authors wish to thank Cécile Raynaud, Eric Jenczewski, Rajeev Kumar, Raphaël Mercier and Jean Molinier for critical reading of the manuscript.","article_number":"e1008894","oa_version":"Published Version","doi":"10.1371/journal.pgen.1008894","main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351236/","open_access":"1"}],"publication":"PLOS Genetics","date_created":"2023-01-16T09:16:10Z","day":"29","status":"public","pmid":1,"volume":16},{"date_updated":"2023-08-22T07:55:36Z","other_data_license":"CC0 + CC BY (4.0)","title":"Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults","oa":1,"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","author":[{"full_name":"Hillary, Robert F.","first_name":"Robert F.","last_name":"Hillary"},{"last_name":"Trejo-Banos","first_name":"Daniel","full_name":"Trejo-Banos, Daniel"},{"first_name":"Athanasios","last_name":"Kousathanas","full_name":"Kousathanas, Athanasios"},{"full_name":"McCartney, Daniel L.","first_name":"Daniel L.","last_name":"McCartney"},{"full_name":"Harris, Sarah E.","first_name":"Sarah E.","last_name":"Harris"},{"last_name":"Stevenson","first_name":"Anna J.","full_name":"Stevenson, Anna J."},{"first_name":"Marion","last_name":"Patxot","full_name":"Patxot, Marion"},{"last_name":"Ojavee","first_name":"Sven Erik","full_name":"Ojavee, Sven Erik"},{"full_name":"Zhang, Qian","last_name":"Zhang","first_name":"Qian"},{"full_name":"Liewald, David C.","last_name":"Liewald","first_name":"David C."},{"full_name":"Ritchie, Craig W.","first_name":"Craig W.","last_name":"Ritchie"},{"last_name":"Evans","first_name":"Kathryn L.","full_name":"Evans, Kathryn L."},{"last_name":"Tucker-Drob","first_name":"Elliot M.","full_name":"Tucker-Drob, Elliot M."},{"full_name":"Wray, Naomi R.","last_name":"Wray","first_name":"Naomi R."},{"first_name":"Allan F. ","last_name":"McRae","full_name":"McRae, Allan F. "},{"last_name":"Visscher","first_name":"Peter M.","full_name":"Visscher, Peter M."},{"full_name":"Deary, Ian J.","first_name":"Ian J.","last_name":"Deary"},{"full_name":"Robinson, Matthew Richard","id":"E5D42276-F5DA-11E9-8E24-6303E6697425","first_name":"Matthew Richard","orcid":"0000-0001-8982-8813","last_name":"Robinson"},{"full_name":"Marioni, Riccardo E. ","first_name":"Riccardo E. ","last_name":"Marioni"}],"date_published":"2020-07-09T00:00:00Z","has_accepted_license":"1","month":"07","publisher":"Springer Nature","year":"2020","article_processing_charge":"No","department":[{"_id":"MaRo"}],"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"date_created":"2021-07-23T08:59:15Z","_id":"9706","status":"public","day":"09","citation":{"chicago":"Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel L. McCartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.” Springer Nature, 2020. <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">https://doi.org/10.6084/m9.figshare.12629697.v1</a>.","ama":"Hillary RF, Trejo-Banos D, Kousathanas A, et al. Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. 2020. doi:<a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>","ista":"Hillary RF, Trejo-Banos D, Kousathanas A, McCartney DL, Harris SE, Stevenson AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults, Springer Nature, <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>.","ieee":"R. F. Hillary <i>et al.</i>, “Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults.” Springer Nature, 2020.","short":"R.F. Hillary, D. Trejo-Banos, A. Kousathanas, D.L. McCartney, S.E. Harris, A.J. Stevenson, M. Patxot, S.E. Ojavee, Q. Zhang, D.C. Liewald, C.W. Ritchie, K.L. Evans, E.M. Tucker-Drob, N.R. Wray, A.F. McRae, P.M. Visscher, I.J. Deary, M.R. Robinson, R.E. Marioni, (2020).","mla":"Hillary, Robert F., et al. <i>Additional File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults</i>. Springer Nature, 2020, doi:<a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">10.6084/m9.figshare.12629697.v1</a>.","apa":"Hillary, R. F., Trejo-Banos, D., Kousathanas, A., McCartney, D. L., Harris, S. E., Stevenson, A. J., … Marioni, R. E. (2020). Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults. Springer Nature. <a href=\"https://doi.org/10.6084/m9.figshare.12629697.v1\">https://doi.org/10.6084/m9.figshare.12629697.v1</a>"},"abstract":[{"text":"Additional file 2: Supplementary Tables. The association of pre-adjusted protein levels with biological and technical covariates. Protein levels were adjusted for age, sex, array plate and four genetic principal components (population structure) prior to analyses. Significant associations are emboldened. (Table S1). pQTLs associated with inflammatory biomarker levels from Bayesian penalised regression model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S3). Summary of lambda values relating to ordinary least squares GWAS and EWAS performed on inflammatory protein levels (n = 70) in Lothian Birth Cohort 1936 study. (Table S4). Conditionally significant pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S5). Comparison of variance explained by ordinary least squares and Bayesian penalised regression models for concordantly identified SNPs. (Table S6). Estimate of heritability for blood protein levels as well as proportion of variance explained attributable to different prior mixtures. (Table S7). Comparison of heritability estimates from Ahsan et al. (maximum likelihood) and Hillary et al. (Bayesian penalised regression). (Table S8). List of concordant SNPs identified by linear model and Bayesian penalised regression and whether they have been previously identified as eQTLs. (Table S9). Bayesian tests of colocalisation for cis pQTLs and cis eQTLs. (Table S10). Sherlock algorithm: Genes whose expression are putatively associated with circulating inflammatory proteins that harbour pQTLs. (Table S11). CpGs associated with inflammatory protein biomarkers as identified by Bayesian model (Bayesian model; Posterior Inclusion Probability > 95%). (Table S12). CpGs associated with inflammatory protein biomarkers as identified by linear model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10. (Table S14). Estimate of variance explained for blood protein levels by DNA methylation as well as proportion of explained attributable to different prior mixtures - BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker levels explained by common genetic and methylation data (joint and conditional estimates from BayesR+). Ordered by combined variance explained by genetic and epigenetic data - smallest to largest. Significant results from t-tests comparing distributions for variance explained by methylation or genetics alone versus combined estimate are emboldened. (Table S17). Genetic and epigenetic factors identified by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian Randomisation analyses to assess whether proteins with concordantly identified genetic signals are causally associated with Alzheimer’s disease risk. (Table S19).","lang":"eng"}],"type":"research_data_reference","related_material":{"record":[{"id":"8133","status":"public","relation":"used_in_publication"}]},"oa_version":"Published Version","doi":"10.6084/m9.figshare.12629697.v1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.6084/m9.figshare.12629697.v1"}]},{"day":"29","status":"public","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"_id":"9708","date_created":"2021-07-23T10:00:35Z","type":"research_data_reference","abstract":[{"lang":"eng","text":"This research data supports 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'. A Readme file for plotting each figure is provided."}],"citation":{"apa":"Hartstein, M., Hsu, Y.-T., Modic, K. A., Porras, J., Loew, T., Le Tacon, M., … Harrison, N. (2020). Accompanying dataset for “Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.” Apollo - University of Cambridge. <a href=\"https://doi.org/10.17863/cam.50169\">https://doi.org/10.17863/cam.50169</a>","mla":"Hartstein, Mate, et al. <i>Accompanying Dataset for “Hard Antinodal Gap Revealed by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc Superconductors.”</i> Apollo - University of Cambridge, 2020, doi:<a href=\"https://doi.org/10.17863/cam.50169\">10.17863/cam.50169</a>.","ama":"Hartstein M, Hsu Y-T, Modic KA, et al. Accompanying dataset for “Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.” 2020. doi:<a href=\"https://doi.org/10.17863/cam.50169\">10.17863/cam.50169</a>","ieee":"M. Hartstein <i>et al.</i>, “Accompanying dataset for ‘Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.’” Apollo - University of Cambridge, 2020.","short":"M. Hartstein, Y.-T. Hsu, K.A. Modic, J. Porras, T. Loew, M. Le Tacon, H. Zuo, J. Wang, Z. Zhu, M. Chan, R. McDonald, G. Lonzarich, B. Keimer, S. Sebastian, N. Harrison, (2020).","ista":"Hartstein M, Hsu Y-T, Modic KA, Porras J, Loew T, Le Tacon M, Zuo H, Wang J, Zhu Z, Chan M, McDonald R, Lonzarich G, Keimer B, Sebastian S, Harrison N. 2020. Accompanying dataset for ‘Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors’, Apollo - University of Cambridge, <a href=\"https://doi.org/10.17863/cam.50169\">10.17863/cam.50169</a>.","chicago":"Hartstein, Mate, Yu-Te Hsu, Kimberly A Modic, Juan Porras, Toshinao Loew, Matthieu Le Tacon, Huakun Zuo, et al. “Accompanying Dataset for ‘Hard Antinodal Gap Revealed by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc Superconductors.’” Apollo - University of Cambridge, 2020. <a href=\"https://doi.org/10.17863/cam.50169\">https://doi.org/10.17863/cam.50169</a>."},"oa_version":"Published Version","related_material":{"record":[{"id":"7942","relation":"used_in_publication","status":"public"}]},"doi":"10.17863/cam.50169","main_file_link":[{"open_access":"1","url":"https://doi.org/10.17863/CAM.50169"}],"oa":1,"author":[{"first_name":"Mate","last_name":"Hartstein","full_name":"Hartstein, Mate"},{"last_name":"Hsu","first_name":"Yu-Te","full_name":"Hsu, Yu-Te"},{"full_name":"Modic, Kimberly A","last_name":"Modic","orcid":"0000-0001-9760-3147","first_name":"Kimberly A","id":"13C26AC0-EB69-11E9-87C6-5F3BE6697425"},{"last_name":"Porras","first_name":"Juan","full_name":"Porras, Juan"},{"full_name":"Loew, Toshinao","last_name":"Loew","first_name":"Toshinao"},{"first_name":"Matthieu","last_name":"Le Tacon","full_name":"Le Tacon, Matthieu"},{"full_name":"Zuo, Huakun","first_name":"Huakun","last_name":"Zuo"},{"last_name":"Wang","first_name":"Jinhua","full_name":"Wang, Jinhua"},{"full_name":"Zhu, Zengwei","first_name":"Zengwei","last_name":"Zhu"},{"first_name":"Mun","last_name":"Chan","full_name":"Chan, Mun"},{"first_name":"Ross","last_name":"McDonald","full_name":"McDonald, Ross"},{"full_name":"Lonzarich, Gilbert","first_name":"Gilbert","last_name":"Lonzarich"},{"first_name":"Bernhard","last_name":"Keimer","full_name":"Keimer, Bernhard"},{"last_name":"Sebastian","first_name":"Suchitra","full_name":"Sebastian, Suchitra"},{"full_name":"Harrison, Neil","first_name":"Neil","last_name":"Harrison"}],"title":"Accompanying dataset for 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-08-21T07:06:48Z","publisher":"Apollo - University of Cambridge","has_accepted_license":"1","month":"05","date_published":"2020-05-29T00:00:00Z","year":"2020","department":[{"_id":"KiMo"}],"article_processing_charge":"No"},{"doi":"10.1021/jacs.9b13450.s001","oa_version":"Published Version","related_material":{"record":[{"id":"8040","status":"public","relation":"used_in_publication"}]},"type":"research_data_reference","abstract":[{"lang":"eng","text":"Additional analyses of the trajectories"}],"citation":{"apa":"Gupta, C., Khaniya, U., Chan, C. K., Dehez, F., Shekhar, M., Gunner, M. R., … Singharoy, A. (2020). Supporting information. American Chemical Society . <a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">https://doi.org/10.1021/jacs.9b13450.s001</a>","mla":"Gupta, Chitrak, et al. <i>Supporting Information</i>. American Chemical Society , 2020, doi:<a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">10.1021/jacs.9b13450.s001</a>.","short":"C. Gupta, U. Khaniya, C.K. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A. Sazanov, C. Chipot, A. Singharoy, (2020).","ieee":"C. Gupta <i>et al.</i>, “Supporting information.” American Chemical Society , 2020.","ista":"Gupta C, Khaniya U, Chan CK, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot C, Singharoy A. 2020. Supporting information, American Chemical Society , <a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">10.1021/jacs.9b13450.s001</a>.","ama":"Gupta C, Khaniya U, Chan CK, et al. Supporting information. 2020. doi:<a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">10.1021/jacs.9b13450.s001</a>","chicago":"Gupta, Chitrak, Umesh Khaniya, Chun Kit Chan, Francois Dehez, Mrinal Shekhar, M.R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Supporting Information.” American Chemical Society , 2020. <a href=\"https://doi.org/10.1021/jacs.9b13450.s001\">https://doi.org/10.1021/jacs.9b13450.s001</a>."},"day":"20","status":"public","date_created":"2021-07-23T12:02:39Z","_id":"9713","department":[{"_id":"LeSa"}],"article_processing_charge":"No","year":"2020","publisher":"American Chemical Society ","month":"05","date_published":"2020-05-20T00:00:00Z","title":"Supporting information","author":[{"full_name":"Gupta, Chitrak","last_name":"Gupta","first_name":"Chitrak"},{"last_name":"Khaniya","first_name":"Umesh","full_name":"Khaniya, Umesh"},{"first_name":"Chun Kit","last_name":"Chan","full_name":"Chan, Chun Kit"},{"full_name":"Dehez, Francois","first_name":"Francois","last_name":"Dehez"},{"full_name":"Shekhar, Mrinal","last_name":"Shekhar","first_name":"Mrinal"},{"first_name":"M.R.","last_name":"Gunner","full_name":"Gunner, M.R."},{"id":"338D39FE-F248-11E8-B48F-1D18A9856A87","first_name":"Leonid A","orcid":"0000-0002-0977-7989","last_name":"Sazanov","full_name":"Sazanov, Leonid A"},{"last_name":"Chipot","first_name":"Christophe","full_name":"Chipot, Christophe"},{"full_name":"Singharoy, Abhishek","last_name":"Singharoy","first_name":"Abhishek"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-08-22T07:49:38Z"},{"month":"11","date_published":"2020-11-20T00:00:00Z","publisher":"Cold Spring Harbor Laboratory","acknowledgement":"We would like to thank Edouard Hannezo for discussions, Shayan Shami Pour and Daniel Capek for help with data analysis, Vanessa Barone and other members of the Heisenberg laboratory for thoughtful discussions and comments on the manuscript. We also thank Jack Merrin for preparing the microwells, and the Scientific Service Units at IST Austria, specifically Bioimaging and Electron Microscopy, and the Zebrafish Facility for continuous support. We acknowledge Hitoshi Morita for the kind gift of VinculinB-GFP plasmid. This research was supported by an ERC Advanced Grant (MECSPEC) to C.-P.H, EMBO Long Term grant (ALTF 187-2013) to M.S and IST Fellow Marie-Curie COFUND No. P_IST_EU01 to J.S.","date_updated":"2024-03-25T23:30:10Z","oa":1,"author":[{"last_name":"Slovakova","id":"30F3F2F0-F248-11E8-B48F-1D18A9856A87","first_name":"Jana","full_name":"Slovakova, Jana"},{"full_name":"Sikora, Mateusz K","id":"2F74BCDE-F248-11E8-B48F-1D18A9856A87","first_name":"Mateusz K","last_name":"Sikora"},{"full_name":"Caballero Mancebo, Silvia","last_name":"Caballero Mancebo","orcid":"0000-0002-5223-3346","first_name":"Silvia","id":"2F1E1758-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0003-4761-5996","last_name":"Krens","first_name":"Gabriel","id":"2B819732-F248-11E8-B48F-1D18A9856A87","full_name":"Krens, Gabriel"},{"last_name":"Kaufmann","orcid":"0000-0001-9735-5315","id":"3F99E422-F248-11E8-B48F-1D18A9856A87","first_name":"Walter","full_name":"Kaufmann, Walter"},{"full_name":"Huljev, Karla","last_name":"Huljev","id":"44C6F6A6-F248-11E8-B48F-1D18A9856A87","first_name":"Karla"},{"id":"39427864-F248-11E8-B48F-1D18A9856A87","first_name":"Carl-Philipp J","orcid":"0000-0002-0912-4566","last_name":"Heisenberg","full_name":"Heisenberg, Carl-Philipp J"}],"title":"Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","acknowledged_ssus":[{"_id":"Bio"},{"_id":"EM-Fac"},{"_id":"SSU"}],"page":"41","article_processing_charge":"No","department":[{"_id":"CaHe"},{"_id":"EM-Fac"},{"_id":"Bio"}],"language":[{"iso":"eng"}],"year":"2020","citation":{"short":"J. Slovakova, M.K. Sikora, S. Caballero Mancebo, G. Krens, W. Kaufmann, K. Huljev, C.-P.J. Heisenberg, BioRxiv (2020).","ieee":"J. Slovakova <i>et al.</i>, “Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion,” <i>bioRxiv</i>. Cold Spring Harbor Laboratory, 2020.","ista":"Slovakova J, Sikora MK, Caballero Mancebo S, Krens G, Kaufmann W, Huljev K, Heisenberg C-PJ. 2020. Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion. bioRxiv, <a href=\"https://doi.org/10.1101/2020.11.20.391284\">10.1101/2020.11.20.391284</a>.","ama":"Slovakova J, Sikora MK, Caballero Mancebo S, et al. Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion. <i>bioRxiv</i>. 2020. doi:<a href=\"https://doi.org/10.1101/2020.11.20.391284\">10.1101/2020.11.20.391284</a>","chicago":"Slovakova, Jana, Mateusz K Sikora, Silvia Caballero Mancebo, Gabriel Krens, Walter Kaufmann, Karla Huljev, and Carl-Philipp J Heisenberg. “Tension-Dependent Stabilization of E-Cadherin Limits Cell-Cell Contact Expansion.” <i>BioRxiv</i>. Cold Spring Harbor Laboratory, 2020. <a href=\"https://doi.org/10.1101/2020.11.20.391284\">https://doi.org/10.1101/2020.11.20.391284</a>.","apa":"Slovakova, J., Sikora, M. K., Caballero Mancebo, S., Krens, G., Kaufmann, W., Huljev, K., &#38; Heisenberg, C.-P. J. (2020). Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion. <i>bioRxiv</i>. Cold Spring Harbor Laboratory. <a href=\"https://doi.org/10.1101/2020.11.20.391284\">https://doi.org/10.1101/2020.11.20.391284</a>","mla":"Slovakova, Jana, et al. “Tension-Dependent Stabilization of E-Cadherin Limits Cell-Cell Contact Expansion.” <i>BioRxiv</i>, Cold Spring Harbor Laboratory, 2020, doi:<a href=\"https://doi.org/10.1101/2020.11.20.391284\">10.1101/2020.11.20.391284</a>."},"type":"preprint","abstract":[{"text":"Tension of the actomyosin cell cortex plays a key role in determining cell-cell contact growth and size. The level of cortical tension outside of the cell-cell contact, when pulling at the contact edge, scales with the total size to which a cell-cell contact can grow1,2. Here we show in zebrafish primary germ layer progenitor cells that this monotonic relationship only applies to a narrow range of cortical tension increase, and that above a critical threshold, contact size inversely scales with cortical tension. This switch from cortical tension increasing to decreasing progenitor cell-cell contact size is caused by cortical tension promoting E-cadherin anchoring to the actomyosin cytoskeleton, thereby increasing clustering and stability of E-cadherin at the contact. Once tension-mediated E-cadherin stabilization at the contact exceeds a critical threshold level, the rate by which the contact expands in response to pulling forces from the cortex sharply drops, leading to smaller contacts at physiologically relevant timescales of contact formation. Thus, the activity of cortical tension in expanding cell-cell contact size is limited by tension stabilizing E-cadherin-actin complexes at the contact.","lang":"eng"}],"_id":"9750","date_created":"2021-07-29T11:29:50Z","day":"20","status":"public","project":[{"call_identifier":"FP7","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme"},{"_id":"260F1432-B435-11E9-9278-68D0E5697425","name":"Interaction and feedback between cell mechanics and fate specification in vertebrate gastrulation","call_identifier":"H2020","grant_number":"742573"},{"name":"Modulation of adhesion function in cell-cell contact formation by cortical tension","_id":"2521E28E-B435-11E9-9278-68D0E5697425","grant_number":"187-2013"}],"doi":"10.1101/2020.11.20.391284","publication_status":"published","main_file_link":[{"url":"https://doi.org/10.1101/2020.11.20.391284","open_access":"1"}],"publication":"bioRxiv","related_material":{"record":[{"status":"public","relation":"later_version","id":"10766"},{"id":"9623","status":"public","relation":"dissertation_contains"}]},"oa_version":"Preprint","ec_funded":1},{"day":"25","status":"public","date_created":"2021-08-06T07:15:04Z","_id":"9776","type":"research_data_reference","citation":{"ista":"Grah R, Friedlander T. 2020. Supporting information, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s001\">10.1371/journal.pcbi.1007642.s001</a>.","short":"R. Grah, T. Friedlander, (2020).","ieee":"R. Grah and T. Friedlander, “Supporting information.” Public Library of Science, 2020.","ama":"Grah R, Friedlander T. Supporting information. 2020. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s001\">10.1371/journal.pcbi.1007642.s001</a>","chicago":"Grah, Rok, and Tamar Friedlander. “Supporting Information.” Public Library of Science, 2020. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s001\">https://doi.org/10.1371/journal.pcbi.1007642.s001</a>.","apa":"Grah, R., &#38; Friedlander, T. (2020). Supporting information. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s001\">https://doi.org/10.1371/journal.pcbi.1007642.s001</a>","mla":"Grah, Rok, and Tamar Friedlander. <i>Supporting Information</i>. Public Library of Science, 2020, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007642.s001\">10.1371/journal.pcbi.1007642.s001</a>."},"oa_version":"Published Version","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"7569"}]},"doi":"10.1371/journal.pcbi.1007642.s001","author":[{"last_name":"Grah","orcid":"0000-0003-2539-3560","first_name":"Rok","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","full_name":"Grah, Rok"},{"full_name":"Friedlander, Tamar","first_name":"Tamar","last_name":"Friedlander"}],"title":"Supporting information","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-08-18T06:47:47Z","publisher":"Public Library of Science","date_published":"2020-02-25T00:00:00Z","month":"02","year":"2020","department":[{"_id":"GaTk"}],"article_processing_charge":"No"}]