[{"intvolume":"        33","volume":33,"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_created":"2021-07-04T22:01:26Z","project":[{"call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning","grant_number":"805223","_id":"268A44D6-B435-11E9-9278-68D0E5697425"}],"arxiv":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."}],"publication_status":"published","oa":1,"ec_funded":1,"publication":"Advances in Neural Information Processing Systems","citation":{"short":"V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, 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.","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.","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.","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.","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."},"department":[{"_id":"DaAl"}],"day":"06","scopus_import":"1","language":[{"iso":"eng"}],"quality_controlled":"1","date_published":"2020-12-06T00:00:00Z","external_id":{"arxiv":["2002.11505"]},"date_updated":"2023-02-23T14:03:03Z","page":"22361-22372","publication_identifier":{"issn":["10495258"],"isbn":["9781713829546"]},"_id":"9631","article_processing_charge":"No","month":"12","oa_version":"Published Version","title":"Scalable belief propagation via relaxed scheduling","year":"2020","status":"public","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).","publisher":"Curran Associates","main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/fdb2c3bab9d0701c4a050a4d8d782c7f-Abstract.html","open_access":"1"}],"author":[{"full_name":"Aksenov, Vitaly","last_name":"Aksenov","first_name":"Vitaly"},{"orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian","last_name":"Alistarh"},{"full_name":"Korhonen, Janne","first_name":"Janne","last_name":"Korhonen","id":"C5402D42-15BC-11E9-A202-CA2BE6697425"}],"type":"conference","conference":{"location":"Vancouver, Canada","start_date":"2020-12-06","end_date":"2020-12-12","name":"NeurIPS: Conference on Neural Information Processing Systems"}},{"oa_version":"Published Version","year":"2020","status":"public","title":"WoodFisher: Efficient second-order approximation for neural network compression","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.","publisher":"Curran Associates","main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html"}],"author":[{"full_name":"Singh, Sidak Pal","last_name":"Singh","first_name":"Sidak Pal","id":"DD138E24-D89D-11E9-9DC0-DEF6E5697425"},{"orcid":"0000-0003-3650-940X","last_name":"Alistarh","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"}],"type":"conference","conference":{"end_date":"2020-12-12","name":"NeurIPS: Conference on Neural Information Processing Systems","start_date":"2020-12-06","location":"Vancouver, Canada"},"language":[{"iso":"eng"}],"quality_controlled":"1","date_published":"2020-12-06T00:00:00Z","external_id":{"arxiv":["2004.14340"]},"date_updated":"2023-02-23T14:03:06Z","page":"18098-18109","publication_identifier":{"isbn":["9781713829546"],"issn":["10495258"]},"_id":"9632","article_processing_charge":"No","month":"12","ec_funded":1,"oa":1,"publication":"Advances in Neural Information Processing Systems","citation":{"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.","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.","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.","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.","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.","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."},"department":[{"_id":"DaAl"},{"_id":"ToHe"}],"day":"06","scopus_import":"1","volume":33,"intvolume":"        33","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_created":"2021-07-04T22:01:26Z","project":[{"name":"Elastic Coordination for Scalable Machine Learning","_id":"268A44D6-B435-11E9-9278-68D0E5697425","grant_number":"805223","call_identifier":"H2020"}],"arxiv":1,"abstract":[{"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.","lang":"eng"}],"publication_status":"published"},{"publication_identifier":{"issn":["1049-5258"]},"_id":"9633","article_processing_charge":"No","month":"12","language":[{"iso":"eng"}],"date_published":"2020-12-06T00:00:00Z","quality_controlled":"1","date_updated":"2023-10-18T09:20:55Z","page":"16398-16408","main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/bdbd5ebfde4934142c8a88e7a3796cd5-Abstract.html","open_access":"1"}],"author":[{"id":"C7610134-B532-11EA-BD9F-F5753DDC885E","first_name":"Basile J","last_name":"Confavreux","full_name":"Confavreux, Basile J"},{"first_name":"Friedemann","last_name":"Zenke","full_name":"Zenke, Friedemann"},{"full_name":"Agnes, Everton J.","first_name":"Everton J.","last_name":"Agnes"},{"last_name":"Lillicrap","first_name":"Timothy","full_name":"Lillicrap, Timothy"},{"id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","last_name":"Vogels","first_name":"Tim P","orcid":"0000-0003-3295-6181"}],"type":"conference","conference":{"location":"Vancouver, Canada","start_date":"2020-12-06","end_date":"2020-12-12","name":"NeurIPS: Conference on Neural Information Processing Systems"},"oa_version":"Published Version","status":"public","year":"2020","title":"A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network","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.","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","_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning."}],"abstract":[{"lang":"eng","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."}],"publication_status":"published","volume":33,"intvolume":"        33","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_created":"2021-07-04T22:01:27Z","related_material":{"link":[{"url":"https://doi.org/10.1101/2020.10.24.353409","relation":"is_continued_by"}],"record":[{"relation":"dissertation_contains","status":"public","id":"14422"}]},"scopus_import":"1","day":"06","oa":1,"ec_funded":1,"publication":"Advances in Neural Information Processing Systems","department":[{"_id":"TiVo"}],"citation":{"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.","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.","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.","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.","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.","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.","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."}},{"pmid":1,"extern":"1","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","intvolume":"       152","volume":152,"date_created":"2021-07-15T07:22:24Z","publication_status":"published","abstract":[{"lang":"eng","text":"Macroscopic models of nucleation provide powerful tools for understanding activated phase transition processes. These models do not provide atomistic insights and can thus sometimes lack material-specific descriptions. Here, we provide a comprehensive framework for constructing a continuum picture from an atomistic simulation of homogeneous nucleation. We use this framework to determine the equilibrium shape of the solid nucleus that forms inside bulk liquid for a Lennard-Jones potential. From this shape, we then extract the anisotropy of the solid-liquid interfacial free energy, by performing a reverse Wulff construction in the space of spherical harmonic expansions. We find that the shape of the nucleus is nearly spherical and that its anisotropy can be perfectly described using classical models."}],"article_number":"044103","arxiv":1,"publication":"The Journal of Chemical Physics","citation":{"ista":"Cheng B, Ceriotti M, Tribello GA. 2020. Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification. The Journal of Chemical Physics. 152(4), 044103.","ama":"Cheng B, Ceriotti M, Tribello GA. Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification. <i>The Journal of Chemical Physics</i>. 2020;152(4). doi:<a href=\"https://doi.org/10.1063/1.5134461\">10.1063/1.5134461</a>","short":"B. Cheng, M. Ceriotti, G.A. Tribello, The Journal of Chemical Physics 152 (2020).","chicago":"Cheng, Bingqing, Michele Ceriotti, and Gareth A. Tribello. “Classical Nucleation Theory Predicts the Shape of the Nucleus in Homogeneous Solidification.” <i>The Journal of Chemical Physics</i>. AIP Publishing, 2020. <a href=\"https://doi.org/10.1063/1.5134461\">https://doi.org/10.1063/1.5134461</a>.","mla":"Cheng, Bingqing, et al. “Classical Nucleation Theory Predicts the Shape of the Nucleus in Homogeneous Solidification.” <i>The Journal of Chemical Physics</i>, vol. 152, no. 4, 044103, AIP Publishing, 2020, doi:<a href=\"https://doi.org/10.1063/1.5134461\">10.1063/1.5134461</a>.","apa":"Cheng, B., Ceriotti, M., &#38; Tribello, G. A. (2020). Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification. <i>The Journal of Chemical Physics</i>. AIP Publishing. <a href=\"https://doi.org/10.1063/1.5134461\">https://doi.org/10.1063/1.5134461</a>","ieee":"B. Cheng, M. Ceriotti, and G. A. Tribello, “Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification,” <i>The Journal of Chemical Physics</i>, vol. 152, no. 4. AIP Publishing, 2020."},"oa":1,"day":"31","scopus_import":"1","article_type":"original","date_updated":"2023-02-23T14:03:55Z","external_id":{"arxiv":["1910.13481"],"pmid":["32007057"]},"issue":"4","language":[{"iso":"eng"}],"date_published":"2020-01-31T00:00:00Z","quality_controlled":"1","_id":"9658","month":"01","article_processing_charge":"No","publication_identifier":{"eissn":["1089-7690"],"issn":["0021-9606"]},"status":"public","year":"2020","title":"Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification","doi":"10.1063/1.5134461","publisher":"AIP Publishing","oa_version":"Submitted Version","type":"journal_article","main_file_link":[{"url":"https://pure.qub.ac.uk/en/publications/classical-nucleation-theory-predicts-the-shape-of-the-nucleus-in-homogeneous-solidification(56af848b-eee8-4e9b-93cf-667373e4a49b).html","open_access":"1"}],"author":[{"orcid":"0000-0002-3584-9632","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","full_name":"Cheng, Bingqing","last_name":"Cheng","first_name":"Bingqing"},{"first_name":"Michele","last_name":"Ceriotti","full_name":"Ceriotti, Michele"},{"first_name":"Gareth A.","last_name":"Tribello","full_name":"Tribello, Gareth A."}]},{"pmid":1,"extern":"1","date_created":"2021-07-15T12:15:14Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","intvolume":"       125","volume":125,"abstract":[{"lang":"eng","text":"Equilibrium molecular dynamics simulations, in combination with the Green-Kubo (GK) method, have been extensively used to compute the thermal conductivity of liquids. However, the GK method relies on an ambiguous definition of the microscopic heat flux, which depends on how one chooses to distribute energies over atoms. This ambiguity makes it problematic to employ the GK method for systems with nonpairwise interactions. In this work, we show that the hydrodynamic description of thermally driven density fluctuations can be used to obtain the thermal conductivity of a bulk fluid unambiguously, thereby bypassing the need to define the heat flux. We verify that, for a model fluid with only pairwise interactions, our method yields estimates of thermal conductivity consistent with the GK approach. We apply our approach to compute the thermal conductivity of a nonpairwise additive water model at supercritical conditions, and of a liquid hydrogen system described by a machine-learning interatomic potential, at 33 GPa and 2000 K."}],"publication_status":"published","article_number":"130602","arxiv":1,"citation":{"mla":"Cheng, Bingqing, and Daan Frenkel. “Computing the Heat Conductivity of Fluids from Density Fluctuations.” <i>Physical Review Letters</i>, vol. 125, no. 13, 130602, American Physical Society, 2020, doi:<a href=\"https://doi.org/10.1103/physrevlett.125.130602\">10.1103/physrevlett.125.130602</a>.","chicago":"Cheng, Bingqing, and Daan Frenkel. “Computing the Heat Conductivity of Fluids from Density Fluctuations.” <i>Physical Review Letters</i>. American Physical Society, 2020. <a href=\"https://doi.org/10.1103/physrevlett.125.130602\">https://doi.org/10.1103/physrevlett.125.130602</a>.","apa":"Cheng, B., &#38; Frenkel, D. (2020). Computing the heat conductivity of fluids from density fluctuations. <i>Physical Review Letters</i>. American Physical Society. <a href=\"https://doi.org/10.1103/physrevlett.125.130602\">https://doi.org/10.1103/physrevlett.125.130602</a>","ieee":"B. Cheng and D. Frenkel, “Computing the heat conductivity of fluids from density fluctuations,” <i>Physical Review Letters</i>, vol. 125, no. 13. American Physical Society, 2020.","short":"B. Cheng, D. Frenkel, Physical Review Letters 125 (2020).","ista":"Cheng B, Frenkel D. 2020. Computing the heat conductivity of fluids from density fluctuations. Physical Review Letters. 125(13), 130602.","ama":"Cheng B, Frenkel D. Computing the heat conductivity of fluids from density fluctuations. <i>Physical Review Letters</i>. 2020;125(13). doi:<a href=\"https://doi.org/10.1103/physrevlett.125.130602\">10.1103/physrevlett.125.130602</a>"},"publication":"Physical Review Letters","oa":1,"day":"25","scopus_import":"1","issue":"13","article_type":"original","external_id":{"arxiv":["2005.07562"],"pmid":["33034481"]},"date_updated":"2021-08-09T12:35:58Z","date_published":"2020-09-25T00:00:00Z","quality_controlled":"1","language":[{"iso":"eng"}],"month":"09","article_processing_charge":"No","_id":"9664","publication_identifier":{"issn":["0031-9007"],"eissn":["1079-7114"]},"doi":"10.1103/physrevlett.125.130602","publisher":"American Physical Society","status":"public","year":"2020","title":"Computing the heat conductivity of fluids from density fluctuations","oa_version":"Preprint","type":"journal_article","author":[{"full_name":"Cheng, Bingqing","first_name":"Bingqing","last_name":"Cheng","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","orcid":"0000-0002-3584-9632"},{"last_name":"Frenkel","first_name":"Daan","full_name":"Frenkel, Daan"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2005.07562"}]},{"file_date_updated":"2021-07-15T12:43:51Z","author":[{"last_name":"Reinhardt","first_name":"Aleks","full_name":"Reinhardt, Aleks"},{"first_name":"Chris J.","last_name":"Pickard","full_name":"Pickard, Chris J."},{"last_name":"Cheng","first_name":"Bingqing","full_name":"Cheng, Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","orcid":"0000-0002-3584-9632"}],"type":"journal_article","has_accepted_license":"1","oa_version":"Published Version","title":"Predicting the phase diagram of titanium dioxide with random search and pattern recognition","status":"public","year":"2020","doi":"10.1039/d0cp02513e","ddc":["530"],"publisher":"Royal Society of Chemistry","publication_identifier":{"issn":["1463-9076"],"eissn":["1463-9084"]},"_id":"9666","month":"06","article_processing_charge":"No","tmp":{"name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)","image":"/images/cc_by.png","short":"CC BY (3.0)","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode"},"language":[{"iso":"eng"}],"date_published":"2020-06-14T00:00:00Z","quality_controlled":"1","article_type":"original","date_updated":"2023-02-23T14:04:16Z","external_id":{"pmid":["32459228"],"arxiv":["1909.08934"]},"issue":"22","page":"12697-12705","file":[{"file_name":"202_PhysicalChemistryChemicalPhysics_Reinhardt.pdf","relation":"main_file","access_level":"open_access","checksum":"0a6872972b1b2e60f9095d39b01753fa","content_type":"application/pdf","file_size":3151206,"date_updated":"2021-07-15T12:43:51Z","date_created":"2021-07-15T12:43:51Z","creator":"asandaue","file_id":"9667","success":1}],"day":"14","scopus_import":"1","license":"https://creativecommons.org/licenses/by/3.0/","oa":1,"publication":"Physical Chemistry Chemical Physics","citation":{"ista":"Reinhardt A, Pickard CJ, Cheng B. 2020. Predicting the phase diagram of titanium dioxide with random search and pattern recognition. Physical Chemistry Chemical Physics. 22(22), 12697–12705.","ama":"Reinhardt A, Pickard CJ, Cheng B. Predicting the phase diagram of titanium dioxide with random search and pattern recognition. <i>Physical Chemistry Chemical Physics</i>. 2020;22(22):12697-12705. doi:<a href=\"https://doi.org/10.1039/d0cp02513e\">10.1039/d0cp02513e</a>","short":"A. Reinhardt, C.J. Pickard, B. Cheng, Physical Chemistry Chemical Physics 22 (2020) 12697–12705.","apa":"Reinhardt, A., Pickard, C. J., &#38; Cheng, B. (2020). Predicting the phase diagram of titanium dioxide with random search and pattern recognition. <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/d0cp02513e\">https://doi.org/10.1039/d0cp02513e</a>","ieee":"A. Reinhardt, C. J. Pickard, and B. Cheng, “Predicting the phase diagram of titanium dioxide with random search and pattern recognition,” <i>Physical Chemistry Chemical Physics</i>, vol. 22, no. 22. Royal Society of Chemistry, pp. 12697–12705, 2020.","chicago":"Reinhardt, Aleks, Chris J. Pickard, and Bingqing Cheng. “Predicting the Phase Diagram of Titanium Dioxide with Random Search and Pattern Recognition.” <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry, 2020. <a href=\"https://doi.org/10.1039/d0cp02513e\">https://doi.org/10.1039/d0cp02513e</a>.","mla":"Reinhardt, Aleks, et al. “Predicting the Phase Diagram of Titanium Dioxide with Random Search and Pattern Recognition.” <i>Physical Chemistry Chemical Physics</i>, vol. 22, no. 22, Royal Society of Chemistry, 2020, pp. 12697–705, doi:<a href=\"https://doi.org/10.1039/d0cp02513e\">10.1039/d0cp02513e</a>."},"arxiv":1,"abstract":[{"text":"Predicting phase stabilities of crystal polymorphs is central to computational materials science and chemistry. Such predictions are challenging because they first require searching for potential energy minima and then performing arduous free-energy calculations to account for entropic effects at finite temperatures. Here, we develop a framework that facilitates such predictions by exploiting all the information obtained from random searches of crystal structures. This framework combines automated clustering, classification and visualisation of crystal structures with machine-learning estimation of their enthalpy and entropy. We demonstrate the framework on the technologically important system of TiO2, which has many polymorphs, without relying on prior knowledge of known phases. We find a number of new phases and predict the phase diagram and metastabilities of crystal polymorphs at 1600 K, benchmarking the results against full free-energy calculations.","lang":"eng"}],"publication_status":"published","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","volume":22,"intvolume":"        22","date_created":"2021-07-15T12:37:27Z","extern":"1","pmid":1},{"type":"journal_article","author":[{"first_name":"Bartomeu","last_name":"Monserrat","full_name":"Monserrat, Bartomeu"},{"full_name":"Brandenburg, Jan Gerit","last_name":"Brandenburg","first_name":"Jan Gerit"},{"full_name":"Engel, Edgar A.","first_name":"Edgar A.","last_name":"Engel"},{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","full_name":"Cheng, Bingqing","first_name":"Bingqing","last_name":"Cheng","orcid":"0000-0002-3584-9632"}],"file_date_updated":"2021-07-15T14:05:45Z","doi":"10.1038/s41467-020-19606-y","publisher":"Springer Nature","ddc":["530","540"],"year":"2020","title":"Liquid water contains the building blocks of diverse ice phases","status":"public","has_accepted_license":"1","oa_version":"Published Version","month":"11","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"article_processing_charge":"No","_id":"9671","publication_identifier":{"eissn":["2041-1723"]},"issue":"1","file":[{"date_updated":"2021-07-15T14:05:45Z","file_size":1385954,"file_id":"9672","creator":"asandaue","success":1,"date_created":"2021-07-15T14:05:45Z","relation":"main_file","access_level":"open_access","checksum":"1edd9b6d8fa791f8094d87bd6453955b","file_name":"2020_NatureCommunications_Monserrat.pdf","content_type":"application/pdf"}],"article_type":"original","date_updated":"2023-02-23T14:04:25Z","date_published":"2020-11-13T00:00:00Z","quality_controlled":"1","language":[{"iso":"eng"}],"scopus_import":"1","day":"13","citation":{"apa":"Monserrat, B., Brandenburg, J. G., Engel, E. A., &#38; Cheng, B. (2020). Liquid water contains the building blocks of diverse ice phases. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-020-19606-y\">https://doi.org/10.1038/s41467-020-19606-y</a>","ieee":"B. Monserrat, J. G. Brandenburg, E. A. Engel, and B. Cheng, “Liquid water contains the building blocks of diverse ice phases,” <i>Nature Communications</i>, vol. 11, no. 1. Springer Nature, 2020.","chicago":"Monserrat, Bartomeu, Jan Gerit Brandenburg, Edgar A. Engel, and Bingqing Cheng. “Liquid Water Contains the Building Blocks of Diverse Ice Phases.” <i>Nature Communications</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41467-020-19606-y\">https://doi.org/10.1038/s41467-020-19606-y</a>.","mla":"Monserrat, Bartomeu, et al. “Liquid Water Contains the Building Blocks of Diverse Ice Phases.” <i>Nature Communications</i>, vol. 11, no. 1, 5757, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1038/s41467-020-19606-y\">10.1038/s41467-020-19606-y</a>.","ista":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. 2020. Liquid water contains the building blocks of diverse ice phases. Nature Communications. 11(1), 5757.","ama":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. Liquid water contains the building blocks of diverse ice phases. <i>Nature Communications</i>. 2020;11(1). doi:<a href=\"https://doi.org/10.1038/s41467-020-19606-y\">10.1038/s41467-020-19606-y</a>","short":"B. Monserrat, J.G. Brandenburg, E.A. Engel, B. Cheng, Nature Communications 11 (2020)."},"publication":"Nature Communications","oa":1,"abstract":[{"text":"Water molecules can arrange into a liquid with complex hydrogen-bond networks and at least 17 experimentally confirmed ice phases with enormous structural diversity. It remains a puzzle how or whether this multitude of arrangements in different phases of water are related. Here we investigate the structural similarities between liquid water and a comprehensive set of 54 ice phases in simulations, by directly comparing their local environments using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, lattice energies, and vibrational properties of the ices. The finding that the local environments characterising the different ice phases are found in water sheds light on the phase behavior of water, and rationalizes the transferability of water models between different phases.","lang":"eng"}],"publication_status":"published","article_number":"5757","extern":"1","date_created":"2021-07-15T14:01:35Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","intvolume":"        11","volume":11},{"oa_version":"None","status":"public","title":"Mapping materials and molecules","year":"2020","doi":"10.1021/acs.accounts.0c00403","publisher":"American Chemical Society","author":[{"first_name":"Bingqing","last_name":"Cheng","full_name":"Cheng, Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","orcid":"0000-0002-3584-9632"},{"last_name":"Griffiths","first_name":"Ryan-Rhys","full_name":"Griffiths, Ryan-Rhys"},{"full_name":"Wengert, Simon","last_name":"Wengert","first_name":"Simon"},{"last_name":"Kunkel","first_name":"Christian","full_name":"Kunkel, Christian"},{"last_name":"Stenczel","first_name":"Tamas","full_name":"Stenczel, Tamas"},{"full_name":"Zhu, Bonan","first_name":"Bonan","last_name":"Zhu"},{"full_name":"Deringer, Volker L.","last_name":"Deringer","first_name":"Volker L."},{"full_name":"Bernstein, Noam","last_name":"Bernstein","first_name":"Noam"},{"full_name":"Margraf, Johannes T.","first_name":"Johannes T.","last_name":"Margraf"},{"full_name":"Reuter, Karsten","first_name":"Karsten","last_name":"Reuter"},{"full_name":"Csanyi, Gabor","last_name":"Csanyi","first_name":"Gabor"}],"type":"journal_article","language":[{"iso":"eng"}],"quality_controlled":"1","date_published":"2020-08-14T00:00:00Z","article_type":"original","date_updated":"2021-11-24T15:54:41Z","external_id":{"pmid":["32794697"]},"issue":"9","page":"1981-1991","publication_identifier":{"issn":["0001-4842"],"eissn":["1520-4898"]},"_id":"9675","month":"08","article_processing_charge":"No","publication":"Accounts of Chemical Research","citation":{"ieee":"B. Cheng <i>et al.</i>, “Mapping materials and molecules,” <i>Accounts of Chemical Research</i>, vol. 53, no. 9. American Chemical Society, pp. 1981–1991, 2020.","apa":"Cheng, B., Griffiths, R.-R., Wengert, S., Kunkel, C., Stenczel, T., Zhu, B., … Csanyi, G. (2020). Mapping materials and molecules. <i>Accounts of Chemical Research</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acs.accounts.0c00403\">https://doi.org/10.1021/acs.accounts.0c00403</a>","chicago":"Cheng, Bingqing, Ryan-Rhys Griffiths, Simon Wengert, Christian Kunkel, Tamas Stenczel, Bonan Zhu, Volker L. Deringer, et al. “Mapping Materials and Molecules.” <i>Accounts of Chemical Research</i>. American Chemical Society, 2020. <a href=\"https://doi.org/10.1021/acs.accounts.0c00403\">https://doi.org/10.1021/acs.accounts.0c00403</a>.","mla":"Cheng, Bingqing, et al. “Mapping Materials and Molecules.” <i>Accounts of Chemical Research</i>, vol. 53, no. 9, American Chemical Society, 2020, pp. 1981–91, doi:<a href=\"https://doi.org/10.1021/acs.accounts.0c00403\">10.1021/acs.accounts.0c00403</a>.","ama":"Cheng B, Griffiths R-R, Wengert S, et al. Mapping materials and molecules. <i>Accounts of Chemical Research</i>. 2020;53(9):1981-1991. doi:<a href=\"https://doi.org/10.1021/acs.accounts.0c00403\">10.1021/acs.accounts.0c00403</a>","ista":"Cheng B, Griffiths R-R, Wengert S, Kunkel C, Stenczel T, Zhu B, Deringer VL, Bernstein N, Margraf JT, Reuter K, Csanyi G. 2020. Mapping materials and molecules. Accounts of Chemical Research. 53(9), 1981–1991.","short":"B. Cheng, R.-R. Griffiths, S. Wengert, C. Kunkel, T. Stenczel, B. Zhu, V.L. Deringer, N. Bernstein, J.T. Margraf, K. Reuter, G. Csanyi, Accounts of Chemical Research 53 (2020) 1981–1991."},"scopus_import":"1","day":"14","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","volume":53,"intvolume":"        53","date_created":"2021-07-16T06:25:53Z","extern":"1","pmid":1,"publication_status":"published","abstract":[{"lang":"eng","text":"The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the \"big data\" revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them.This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields."}]},{"_id":"9685","month":"09","article_processing_charge":"No","publication_identifier":{"eissn":["1476-4687"],"issn":["0028-0836"]},"article_type":"original","date_updated":"2021-08-09T12:38:01Z","external_id":{"arxiv":["1906.03341"],"pmid":["32908269"]},"issue":"7824","page":"217-220","language":[{"iso":"eng"}],"quality_controlled":"1","date_published":"2020-09-10T00:00:00Z","type":"journal_article","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1906.03341"}],"author":[{"orcid":"0000-0002-3584-9632","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","last_name":"Cheng","full_name":"Cheng, Bingqing"},{"full_name":"Mazzola, Guglielmo","first_name":"Guglielmo","last_name":"Mazzola"},{"full_name":"Pickard, Chris J.","last_name":"Pickard","first_name":"Chris J."},{"last_name":"Ceriotti","first_name":"Michele","full_name":"Ceriotti, Michele"}],"title":"Evidence for supercritical behaviour of high-pressure liquid hydrogen","status":"public","year":"2020","doi":"10.1038/s41586-020-2677-y","publisher":"Springer Nature","oa_version":"Preprint","publication_status":"published","abstract":[{"lang":"eng","text":"Hydrogen, the simplest and most abundant element in the Universe, develops a remarkably complex behaviour upon compression^1. Since Wigner predicted the dissociation and metallization of solid hydrogen at megabar pressures almost a century ago^2, several efforts have been made to explain the many unusual properties of dense hydrogen, including a rich and poorly understood solid polymorphism^1,3-5, an anomalous melting line6 and the possible transition to a superconducting state^7. Experiments at such extreme conditions are challenging and often lead to hard-to-interpret and controversial observations, whereas theoretical investigations are constrained by the huge computational cost of sufficiently accurate quantum mechanical calculations. Here we present a theoretical study of the phase diagram of dense hydrogen that uses machine learning to 'learn' potential-energy surfaces and interatomic forces from reference calculations and then predict them at low computational cost, overcoming length- and timescale limitations. We reproduce both the re-entrant melting behaviour and the polymorphism of the solid phase. Simulations using our machine-learning-based potentials provide evidence for a continuous molecular-to-atomic transition in the liquid, with no first-order transition observed above the melting line. This suggests a smooth transition between insulating and metallic layers in giant gas planets, and reconciles existing discrepancies between experiments as a manifestation of supercritical behaviour."}],"arxiv":1,"pmid":1,"extern":"1","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","intvolume":"       585","volume":585,"date_created":"2021-07-19T09:17:49Z","scopus_import":"1","day":"10","publication":"Nature","citation":{"ama":"Cheng B, Mazzola G, Pickard CJ, Ceriotti M. Evidence for supercritical behaviour of high-pressure liquid hydrogen. <i>Nature</i>. 2020;585(7824):217-220. doi:<a href=\"https://doi.org/10.1038/s41586-020-2677-y\">10.1038/s41586-020-2677-y</a>","ista":"Cheng B, Mazzola G, Pickard CJ, Ceriotti M. 2020. Evidence for supercritical behaviour of high-pressure liquid hydrogen. Nature. 585(7824), 217–220.","short":"B. Cheng, G. Mazzola, C.J. Pickard, M. Ceriotti, Nature 585 (2020) 217–220.","ieee":"B. Cheng, G. Mazzola, C. J. Pickard, and M. Ceriotti, “Evidence for supercritical behaviour of high-pressure liquid hydrogen,” <i>Nature</i>, vol. 585, no. 7824. Springer Nature, pp. 217–220, 2020.","apa":"Cheng, B., Mazzola, G., Pickard, C. J., &#38; Ceriotti, M. (2020). Evidence for supercritical behaviour of high-pressure liquid hydrogen. <i>Nature</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41586-020-2677-y\">https://doi.org/10.1038/s41586-020-2677-y</a>","chicago":"Cheng, Bingqing, Guglielmo Mazzola, Chris J. Pickard, and Michele Ceriotti. “Evidence for Supercritical Behaviour of High-Pressure Liquid Hydrogen.” <i>Nature</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41586-020-2677-y\">https://doi.org/10.1038/s41586-020-2677-y</a>.","mla":"Cheng, Bingqing, et al. “Evidence for Supercritical Behaviour of High-Pressure Liquid Hydrogen.” <i>Nature</i>, vol. 585, no. 7824, Springer Nature, 2020, pp. 217–20, doi:<a href=\"https://doi.org/10.1038/s41586-020-2677-y\">10.1038/s41586-020-2677-y</a>."},"oa":1},{"type":"preprint","day":"11","author":[{"first_name":"Julian L","last_name":"Fischer","full_name":"Fischer, Julian L","id":"2C12A0B0-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0479-558X"},{"full_name":"Hensel, Sebastian","first_name":"Sebastian","last_name":"Hensel","id":"4D23B7DA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-7252-8072"},{"first_name":"Tim","last_name":"Laux","full_name":"Laux, Tim"},{"first_name":"Thilo","last_name":"Simon","full_name":"Simon, Thilo"}],"related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"10007"}]},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2003.05478"}],"department":[{"_id":"JuFi"}],"citation":{"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.","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.","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>.","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>. .","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.","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>.","short":"J.L. Fischer, S. Hensel, T. Laux, T. Simon, ArXiv (n.d.)."},"title":"The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions","year":"2020","status":"public","publication":"arXiv","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.","ec_funded":1,"oa_version":"Preprint","oa":1,"article_processing_charge":"No","publication_status":"submitted","month":"03","abstract":[{"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.","lang":"eng"}],"_id":"10012","arxiv":1,"article_number":"2003.05478","project":[{"call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","grant_number":"665385","name":"International IST Doctoral Program"}],"external_id":{"arxiv":["2003.05478"]},"date_updated":"2023-09-07T13:30:45Z","date_published":"2020-03-11T00:00:00Z","date_created":"2021-09-13T12:17:11Z","language":[{"iso":"eng"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9"},{"article_number":"2008.10962","arxiv":1,"project":[{"call_identifier":"H2020","name":"Optimal Transport and Stochastic Dynamics","grant_number":"716117","_id":"256E75B8-B435-11E9-9278-68D0E5697425"},{"grant_number":"F6504","_id":"fc31cba2-9c52-11eb-aca3-ff467d239cd2","name":"Taming Complexity in Partial Differential Systems"}],"article_processing_charge":"No","publication_status":"submitted","abstract":[{"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.","lang":"eng"}],"month":"08","_id":"10022","date_published":"2020-08-25T00:00:00Z","date_created":"2021-09-17T10:57:27Z","language":[{"iso":"eng"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","page":"33","external_id":{"arxiv":["2008.10962"]},"date_updated":"2023-09-07T13:31:05Z","author":[{"last_name":"Forkert","first_name":"Dominik L","full_name":"Forkert, Dominik L","id":"35C79D68-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0002-0845-1338","id":"4C5696CE-F248-11E8-B48F-1D18A9856A87","first_name":"Jan","last_name":"Maas","full_name":"Maas, Jan"},{"id":"30AD2CBC-F248-11E8-B48F-1D18A9856A87","last_name":"Portinale","first_name":"Lorenzo","full_name":"Portinale, Lorenzo"}],"related_material":{"record":[{"relation":"later_version","status":"public","id":"11739"},{"id":"10030","relation":"dissertation_contains","status":"public"}]},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2008.10962"}],"type":"preprint","day":"25","oa":1,"ec_funded":1,"oa_version":"Preprint","department":[{"_id":"JaMa"}],"citation":{"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>. .","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>.","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.","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.","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>.","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.","short":"D.L. Forkert, J. Maas, L. Portinale, ArXiv (n.d.)."},"status":"public","year":"2020","publication":"arXiv","title":"Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions","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."},{"publisher":"Optica Publishing Group","doi":"10.1364/QUANTUM.2020.QTu8A.1","citation":{"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.","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>","short":"N.J. Lambert, S. Mobassem, A.R. Rueda Sanchez, H.G.L. Schwefel, in:, OSA Quantum 2.0 Conference, Optica Publishing Group, 2020.","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>.","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>","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."},"department":[{"_id":"JoFi"}],"title":"New designs and noise channels in electro-optic microwave to optical up-conversion","year":"2020","publication":"OSA Quantum 2.0 Conference","status":"public","oa_version":"None","type":"conference","conference":{"end_date":"2020-09-17","name":"OSA: Optical Society of America","start_date":"2020-09-14","location":"Washington, DC, United States"},"alternative_title":["OSA Technical Digest"],"scopus_import":"1","day":"01","author":[{"full_name":"Lambert, Nicholas J.","first_name":"Nicholas J.","last_name":"Lambert"},{"first_name":"Sonia","last_name":"Mobassem","full_name":"Mobassem, Sonia"},{"last_name":"Rueda Sanchez","first_name":"Alfredo R","full_name":"Rueda Sanchez, Alfredo R","id":"3B82B0F8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6249-5860"},{"last_name":"Schwefel","first_name":"Harald G.L.","full_name":"Schwefel, Harald G.L."}],"date_updated":"2023-10-18T08:32:34Z","quality_controlled":"1","date_published":"2020-01-01T00:00:00Z","date_created":"2021-11-21T23:01:31Z","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","publication_status":"published","month":"01","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."}],"_id":"10328","article_number":"QTu8A.1","publication_identifier":{"isbn":["9-781-5575-2820-9"]}},{"day":"16","scopus_import":"1","oa":1,"publication":"Proceedings of the National Academy of Sciences","citation":{"short":"J. Krausser, T.P.J. Knowles, A. Šarić, Proceedings of the National Academy of Sciences 117 (2020) 33090–33098.","ama":"Krausser J, Knowles TPJ, Šarić A. Physical mechanisms of amyloid nucleation on fluid membranes. <i>Proceedings of the National Academy of Sciences</i>. 2020;117(52):33090-33098. doi:<a href=\"https://doi.org/10.1073/pnas.2007694117\">10.1073/pnas.2007694117</a>","ista":"Krausser J, Knowles TPJ, Šarić A. 2020. Physical mechanisms of amyloid nucleation on fluid membranes. Proceedings of the National Academy of Sciences. 117(52), 33090–33098.","apa":"Krausser, J., Knowles, T. P. J., &#38; Šarić, A. (2020). Physical mechanisms of amyloid nucleation on fluid membranes. <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2007694117\">https://doi.org/10.1073/pnas.2007694117</a>","ieee":"J. Krausser, T. P. J. Knowles, and A. Šarić, “Physical mechanisms of amyloid nucleation on fluid membranes,” <i>Proceedings of the National Academy of Sciences</i>, vol. 117, no. 52. National Academy of Sciences, pp. 33090–33098, 2020.","mla":"Krausser, Johannes, et al. “Physical Mechanisms of Amyloid Nucleation on Fluid Membranes.” <i>Proceedings of the National Academy of Sciences</i>, vol. 117, no. 52, National Academy of Sciences, 2020, pp. 33090–98, doi:<a href=\"https://doi.org/10.1073/pnas.2007694117\">10.1073/pnas.2007694117</a>.","chicago":"Krausser, Johannes, Tuomas P. J. Knowles, and Anđela Šarić. “Physical Mechanisms of Amyloid Nucleation on Fluid Membranes.” <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences, 2020. <a href=\"https://doi.org/10.1073/pnas.2007694117\">https://doi.org/10.1073/pnas.2007694117</a>."},"abstract":[{"lang":"eng","text":"Biological membranes can dramatically accelerate the aggregation of normally soluble protein molecules into amyloid fibrils and alter the fibril morphologies, yet the molecular mechanisms through which this accelerated nucleation takes place are not yet understood. Here, we develop a coarse-grained model to systematically explore the effect that the structural properties of the lipid membrane and the nature of protein–membrane interactions have on the nucleation rates of amyloid fibrils. We identify two physically distinct nucleation pathways—protein-rich and lipid-rich—and quantify how the membrane fluidity and protein–membrane affinity control the relative importance of those molecular pathways. We find that the membrane’s susceptibility to reshaping and being incorporated into the fibrillar aggregates is a key determinant of its ability to promote protein aggregation. We then characterize the rates and the free-energy profile associated with this heterogeneous nucleation process, in which the surface itself participates in the aggregate structure. Finally, we compare quantitatively our data to experiments on membrane-catalyzed amyloid aggregation of α-synuclein, a protein implicated in Parkinson’s disease that predominately nucleates on membranes. More generally, our results provide a framework for understanding macromolecular aggregation on lipid membranes in a broad biological and biotechnological context."}],"publication_status":"published","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","volume":117,"intvolume":"       117","date_created":"2021-11-25T15:07:09Z","extern":"1","pmid":1,"main_file_link":[{"url":"https://www.biorxiv.org/content/10.1101/2019.12.22.886267v2","open_access":"1"}],"author":[{"full_name":"Krausser, Johannes","last_name":"Krausser","first_name":"Johannes"},{"full_name":"Knowles, Tuomas P. J.","first_name":"Tuomas P. J.","last_name":"Knowles"},{"orcid":"0000-0002-7854-2139","id":"bf63d406-f056-11eb-b41d-f263a6566d8b","first_name":"Anđela","last_name":"Šarić","full_name":"Šarić, Anđela"}],"type":"journal_article","oa_version":"Published Version","acknowledgement":"We thank T. C. T. Michaels for reading the manuscript. This work was supported by the Academy of Medical Science (J.K. and A.Š.), the Cambridge Center for Misfolding Diseases (T.P.J.K.), the Biotechnology and Biological Sciences Research Council (T.P.J.K.), the Frances and Augustus Newman Foundation (T.P.J.K.), the European Research Council Grant PhysProt Agreement 337969, the Wellcome Trust (A.Š. and T.P.J.K.), the Royal Society (A.Š.), the Medical Research Council (J.K. and A.Š.), and the UK Materials and Molecular Modeling Hub for computational resources, which is partially funded by Engineering and Physical Sciences Research Council Grant EP/P020194/1.","status":"public","year":"2020","title":"Physical mechanisms of amyloid nucleation on fluid membranes","doi":"10.1073/pnas.2007694117","publisher":"National Academy of Sciences","publication_identifier":{"eissn":["1091-6490"],"issn":["0027-8424"]},"_id":"10336","month":"12","article_processing_charge":"No","language":[{"iso":"eng"}],"date_published":"2020-12-16T00:00:00Z","quality_controlled":"1","article_type":"original","date_updated":"2021-11-25T15:35:58Z","external_id":{"pmid":["33328273"]},"issue":"52","page":"33090-33098"},{"language":[{"iso":"eng"}],"quality_controlled":"1","date_published":"2020-10-06T00:00:00Z","article_type":"original","external_id":{"pmid":["33084724"]},"date_updated":"2021-11-26T07:00:33Z","issue":"47","page":"10628-10639","publication_identifier":{"issn":["1744-683X","1744-6848"]},"_id":"10341","month":"10","article_processing_charge":"No","oa_version":"Published Version","acknowledgement":"We thank Jessica McQuade for her input at the start of the project. We acknowledge support from the ERASMUS Placement Programme (V. E. D.), the UCL Institute for the Physics of Living Systems (V. E. D. and A. Š.), the UCL Global Engagement Fund (L. M. C. J.), and the Royal Society (A. Š.).","title":"Characterising the diffusion of biological nanoparticles on fluid and cross-linked membranes","year":"2020","status":"public","doi":"10.1039/d0sm00712a","publisher":"Royal Society of Chemistry","main_file_link":[{"url":"https://www.biorxiv.org/content/10.1101/2020.05.01.071761v1","open_access":"1"}],"author":[{"full_name":"Debets, V. E.","first_name":"V. E.","last_name":"Debets"},{"last_name":"Janssen","first_name":"L. M. C.","full_name":"Janssen, L. M. C."},{"orcid":"0000-0002-7854-2139","full_name":"Šarić, Anđela","last_name":"Šarić","first_name":"Anđela","id":"bf63d406-f056-11eb-b41d-f263a6566d8b"}],"type":"journal_article","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","volume":16,"intvolume":"        16","date_created":"2021-11-26T06:29:41Z","extern":"1","pmid":1,"publication_status":"published","abstract":[{"text":"Tracing the motion of macromolecules, viruses, and nanoparticles adsorbed onto cell membranes is currently the most direct way of probing the complex dynamic interactions behind vital biological processes, including cell signalling, trafficking, and viral infection. The resulting trajectories are usually consistent with some type of anomalous diffusion, but the molecular origins behind the observed anomalous behaviour are usually not obvious. Here we use coarse-grained molecular dynamics simulations to help identify the physical mechanisms that can give rise to experimentally observed trajectories of nanoscopic objects moving on biological membranes. We find that diffusion on membranes of high fluidities typically results in normal diffusion of the adsorbed nanoparticle, irrespective of the concentration of receptors, receptor clustering, or multivalent interactions between the particle and membrane receptors. Gel-like membranes on the other hand result in anomalous diffusion of the particle, which becomes more pronounced at higher receptor concentrations. This anomalous diffusion is characterised by local particle trapping in the regions of high receptor concentrations and fast hopping between such regions. The normal diffusion is recovered in the limit where the gel membrane is saturated with receptors. We conclude that hindered receptor diffusivity can be a common reason behind the observed anomalous diffusion of viruses, vesicles, and nanoparticles adsorbed on cell and model membranes. Our results enable direct comparison with experiments and offer a new route for interpreting motility experiments on cell membranes.","lang":"eng"}],"oa":1,"publication":"Soft Matter","citation":{"mla":"Debets, V. E., et al. “Characterising the Diffusion of Biological Nanoparticles on Fluid and Cross-Linked Membranes.” <i>Soft Matter</i>, vol. 16, no. 47, Royal Society of Chemistry, 2020, pp. 10628–39, doi:<a href=\"https://doi.org/10.1039/d0sm00712a\">10.1039/d0sm00712a</a>.","chicago":"Debets, V. E., L. M. C. Janssen, and Anđela Šarić. “Characterising the Diffusion of Biological Nanoparticles on Fluid and Cross-Linked Membranes.” <i>Soft Matter</i>. Royal Society of Chemistry, 2020. <a href=\"https://doi.org/10.1039/d0sm00712a\">https://doi.org/10.1039/d0sm00712a</a>.","apa":"Debets, V. E., Janssen, L. M. C., &#38; Šarić, A. (2020). Characterising the diffusion of biological nanoparticles on fluid and cross-linked membranes. <i>Soft Matter</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/d0sm00712a\">https://doi.org/10.1039/d0sm00712a</a>","ieee":"V. E. Debets, L. M. C. Janssen, and A. Šarić, “Characterising the diffusion of biological nanoparticles on fluid and cross-linked membranes,” <i>Soft Matter</i>, vol. 16, no. 47. Royal Society of Chemistry, pp. 10628–10639, 2020.","short":"V.E. Debets, L.M.C. Janssen, A. Šarić, Soft Matter 16 (2020) 10628–10639.","ista":"Debets VE, Janssen LMC, Šarić A. 2020. Characterising the diffusion of biological nanoparticles on fluid and cross-linked membranes. Soft Matter. 16(47), 10628–10639.","ama":"Debets VE, Janssen LMC, Šarić A. Characterising the diffusion of biological nanoparticles on fluid and cross-linked membranes. <i>Soft Matter</i>. 2020;16(47):10628-10639. doi:<a href=\"https://doi.org/10.1039/d0sm00712a\">10.1039/d0sm00712a</a>"},"keyword":["condensed matter physics","general chemistry"],"scopus_import":"1","day":"06"},{"article_number":"eabc4397 ","publication_status":"published","abstract":[{"text":"The blood-brain barrier is made of polarized brain endothelial cells (BECs) phenotypically conditioned by the central nervous system (CNS). Although transport across BECs is of paramount importance for nutrient uptake as well as ridding the brain of waste products, the intracellular sorting mechanisms that regulate successful receptor-mediated transcytosis in BECs remain to be elucidated. Here, we used a synthetic multivalent system with tunable avidity to the low-density lipoprotein receptor–related protein 1 (LRP1) to investigate the mechanisms of transport across BECs. We used a combination of conventional and super-resolution microscopy, both in vivo and in vitro, accompanied with biophysical modeling of transport kinetics and membrane-bound interactions to elucidate the role of membrane-sculpting protein syndapin-2 on fast transport via tubule formation. We show that high-avidity cargo biases the LRP1 toward internalization associated with fast degradation, while mid-avidity augments the formation of syndapin-2 tubular carriers promoting a fast shuttling across.","lang":"eng"}],"volume":6,"intvolume":"         6","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_created":"2021-11-26T06:40:28Z","extern":"1","pmid":1,"day":"27","scopus_import":"1","oa":1,"publication":"Science Advances","keyword":["multidisciplinary"],"citation":{"ieee":"X. Tian <i>et al.</i>, “On the shuttling across the blood-brain barrier via tubule formation: Mechanism and cargo avidity bias,” <i>Science Advances</i>, vol. 6, no. 48. American Association for the Advancement of Science, 2020.","apa":"Tian, X., Leite, D. M., Scarpa, E., Nyberg, S., Fullstone, G., Forth, J., … Battaglia, G. (2020). On the shuttling across the blood-brain barrier via tubule formation: Mechanism and cargo avidity bias. <i>Science Advances</i>. American Association for the Advancement of Science. <a href=\"https://doi.org/10.1126/sciadv.abc4397\">https://doi.org/10.1126/sciadv.abc4397</a>","chicago":"Tian, Xiaohe, Diana M. Leite, Edoardo Scarpa, Sophie Nyberg, Gavin Fullstone, Joe Forth, Diana Matias, et al. “On the Shuttling across the Blood-Brain Barrier via Tubule Formation: Mechanism and Cargo Avidity Bias.” <i>Science Advances</i>. American Association for the Advancement of Science, 2020. <a href=\"https://doi.org/10.1126/sciadv.abc4397\">https://doi.org/10.1126/sciadv.abc4397</a>.","mla":"Tian, Xiaohe, et al. “On the Shuttling across the Blood-Brain Barrier via Tubule Formation: Mechanism and Cargo Avidity Bias.” <i>Science Advances</i>, vol. 6, no. 48, eabc4397, American Association for the Advancement of Science, 2020, doi:<a href=\"https://doi.org/10.1126/sciadv.abc4397\">10.1126/sciadv.abc4397</a>.","ama":"Tian X, Leite DM, Scarpa E, et al. On the shuttling across the blood-brain barrier via tubule formation: Mechanism and cargo avidity bias. <i>Science Advances</i>. 2020;6(48). doi:<a href=\"https://doi.org/10.1126/sciadv.abc4397\">10.1126/sciadv.abc4397</a>","ista":"Tian X, Leite DM, Scarpa E, Nyberg S, Fullstone G, Forth J, Matias D, Apriceno A, Poma A, Duro-Castano A, Vuyyuru M, Harker-Kirschneck L, Šarić A, Zhang Z, Xiang P, Fang B, Tian Y, Luo L, Rizzello L, Battaglia G. 2020. On the shuttling across the blood-brain barrier via tubule formation: Mechanism and cargo avidity bias. Science Advances. 6(48), eabc4397.","short":"X. Tian, D.M. Leite, E. Scarpa, S. Nyberg, G. Fullstone, J. Forth, D. Matias, A. Apriceno, A. Poma, A. Duro-Castano, M. Vuyyuru, L. Harker-Kirschneck, A. Šarić, Z. Zhang, P. Xiang, B. Fang, Y. Tian, L. Luo, L. Rizzello, G. Battaglia, Science Advances 6 (2020)."},"publication_identifier":{"issn":["2375-2548"]},"_id":"10342","article_processing_charge":"No","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"month":"11","language":[{"iso":"eng"}],"date_published":"2020-11-27T00:00:00Z","quality_controlled":"1","external_id":{"pmid":["33246953"]},"date_updated":"2021-11-26T07:00:24Z","article_type":"original","file":[{"access_level":"open_access","relation":"main_file","checksum":"3ba2eca975930cdb0b1ce1ae876885a7","file_name":"2020_SciAdv_Tian.pdf","content_type":"application/pdf","date_updated":"2021-11-26T06:50:09Z","file_size":10381298,"creator":"cchlebak","file_id":"10343","success":1,"date_created":"2021-11-26T06:50:09Z"}],"issue":"48","file_date_updated":"2021-11-26T06:50:09Z","main_file_link":[{"url":"https://www.biorxiv.org/content/10.1101/2020.04.04.025866v1","open_access":"1"}],"author":[{"full_name":"Tian, Xiaohe","last_name":"Tian","first_name":"Xiaohe"},{"last_name":"Leite","first_name":"Diana M.","full_name":"Leite, Diana M."},{"full_name":"Scarpa, Edoardo","last_name":"Scarpa","first_name":"Edoardo"},{"last_name":"Nyberg","first_name":"Sophie","full_name":"Nyberg, Sophie"},{"last_name":"Fullstone","first_name":"Gavin","full_name":"Fullstone, Gavin"},{"first_name":"Joe","last_name":"Forth","full_name":"Forth, Joe"},{"full_name":"Matias, Diana","last_name":"Matias","first_name":"Diana"},{"full_name":"Apriceno, Azzurra","first_name":"Azzurra","last_name":"Apriceno"},{"full_name":"Poma, Alessandro","first_name":"Alessandro","last_name":"Poma"},{"first_name":"Aroa","last_name":"Duro-Castano","full_name":"Duro-Castano, Aroa"},{"first_name":"Manish","last_name":"Vuyyuru","full_name":"Vuyyuru, Manish"},{"full_name":"Harker-Kirschneck, Lena","last_name":"Harker-Kirschneck","first_name":"Lena"},{"orcid":"0000-0002-7854-2139","id":"bf63d406-f056-11eb-b41d-f263a6566d8b","last_name":"Šarić","first_name":"Anđela","full_name":"Šarić, Anđela"},{"first_name":"Zhongping","last_name":"Zhang","full_name":"Zhang, Zhongping"},{"first_name":"Pan","last_name":"Xiang","full_name":"Xiang, Pan"},{"first_name":"Bin","last_name":"Fang","full_name":"Fang, Bin"},{"full_name":"Tian, Yupeng","first_name":"Yupeng","last_name":"Tian"},{"full_name":"Luo, Lei","first_name":"Lei","last_name":"Luo"},{"full_name":"Rizzello, Loris","first_name":"Loris","last_name":"Rizzello"},{"full_name":"Battaglia, Giuseppe","first_name":"Giuseppe","last_name":"Battaglia"}],"type":"journal_article","has_accepted_license":"1","oa_version":"Published Version","title":"On the shuttling across the blood-brain barrier via tubule formation: Mechanism and cargo avidity bias","status":"public","year":"2020","acknowledgement":"Funding: G.B. thanks the ERC for the starting grant (MEViC 278793) and consolidator award (CheSSTaG 769798), EPSRC/BTG Healthcare Partnership (EP/I001697/1), EPSRC Established Career Fellowship (EP/N026322/1), EPSRC/SomaNautix Healthcare Partnership EP/R024723/1, and Children with Cancer UK for the research project (16-227). X.T. and G.B. thank that Anhui 100 Talent program for facilitating data sharing and research visits. A.D.-C. and L.R. acknowledge the Royal Society for a Newton fellowship and the Marie Skłodowska-Curie Actions for a European Fellowship. Author contributions: X.T. prepared and characterized POs, performed all the fast imaging in both conventional and STED microscopy, set up the initial BBB model, encapsulated the PtA2 in POs, and supervised the PtA2-PO animal work. D.M.L. prepared and characterized POs; performed all the permeability studies, PLA assays, WB and associated data analysis, and part of the colocalization assays; and performed experiments with the shRNA for knockdown of syndapin-2. E.S. prepared and characterized POs and performed part of colocalization assays and Cy7-labeled PO animal experiments. S.N. prepared and characterized POs and performed part of the colocalization and inhibition assays. G.F. designed, performed, and analyzed the agent-based simulations of transcytosis. J.F. designed the image-based algorithm to analyze the PLA data. D.M. prepared and characterized POs and helped with Cy7-labeled PO animal experiments. A.A. performed TEM imaging of the POs. A.P. and A.D.-C. synthesized the dye- and peptide-functionalized and pristine copolymers. M.V., L.H.-K., and A.Š. designed, performed, and analyzed the MD simulations. Z.Z. supervised and supported STED imaging. P.X., B.F., and Y.T. synthesized and characterized the PtA2 compound. L.L. performed some of the animal work. L.R. supported and helped with the BBB characterization. G.B. analyzed all fast imaging and supervised and coordinated the overall work. X.T., D.M.L., E.S., and G.B. wrote the manuscript. Competing interests: The authors declare that part of the work is associated with the UCL spin-out company SomaNautix Ltd. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.","ddc":["611"],"publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.abc4397"},{"publication_status":"published","abstract":[{"text":"In this study, we investigate the role of the surface patterning of nanostructures for cell membrane reshaping. To accomplish this, we combine an evolutionary algorithm with coarse-grained molecular dynamics simulations and explore the solution space of ligand patterns on a nanoparticle that promote efficient and reliable cell uptake. Surprisingly, we find that in the regime of low ligand number the best-performing structures are characterized by ligands arranged into long one-dimensional chains that pattern the surface of the particle. We show that these chains of ligands provide particles with high rotational freedom and they lower the free energy barrier for membrane crossing. Our approach reveals a set of nonintuitive design rules that can be used to inform artificial nanoparticle construction and the search for inhibitors of viral entry.","lang":"eng"}],"article_number":"228101","pmid":1,"extern":"1","date_created":"2021-11-26T07:10:43Z","volume":125,"intvolume":"       125","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","day":"23","scopus_import":"1","citation":{"ama":"Forster JC, Krausser J, Vuyyuru MR, Baum B, Šarić A. Exploring the design rules for efficient membrane-reshaping nanostructures. <i>Physical Review Letters</i>. 2020;125(22). doi:<a href=\"https://doi.org/10.1103/physrevlett.125.228101\">10.1103/physrevlett.125.228101</a>","ista":"Forster JC, Krausser J, Vuyyuru MR, Baum B, Šarić A. 2020. Exploring the design rules for efficient membrane-reshaping nanostructures. Physical Review Letters. 125(22), 228101.","short":"J.C. Forster, J. Krausser, M.R. Vuyyuru, B. Baum, A. Šarić, Physical Review Letters 125 (2020).","ieee":"J. C. Forster, J. Krausser, M. R. Vuyyuru, B. Baum, and A. Šarić, “Exploring the design rules for efficient membrane-reshaping nanostructures,” <i>Physical Review Letters</i>, vol. 125, no. 22. American Physical Society, 2020.","apa":"Forster, J. C., Krausser, J., Vuyyuru, M. R., Baum, B., &#38; Šarić, A. (2020). Exploring the design rules for efficient membrane-reshaping nanostructures. <i>Physical Review Letters</i>. American Physical Society. <a href=\"https://doi.org/10.1103/physrevlett.125.228101\">https://doi.org/10.1103/physrevlett.125.228101</a>","chicago":"Forster, Joel C., Johannes Krausser, Manish R. Vuyyuru, Buzz Baum, and Anđela Šarić. “Exploring the Design Rules for Efficient Membrane-Reshaping Nanostructures.” <i>Physical Review Letters</i>. American Physical Society, 2020. <a href=\"https://doi.org/10.1103/physrevlett.125.228101\">https://doi.org/10.1103/physrevlett.125.228101</a>.","mla":"Forster, Joel C., et al. “Exploring the Design Rules for Efficient Membrane-Reshaping Nanostructures.” <i>Physical Review Letters</i>, vol. 125, no. 22, 228101, American Physical Society, 2020, doi:<a href=\"https://doi.org/10.1103/physrevlett.125.228101\">10.1103/physrevlett.125.228101</a>."},"publication":"Physical Review Letters","oa":1,"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"article_processing_charge":"No","month":"11","_id":"10344","publication_identifier":{"eissn":["1079-7114"],"issn":["0031-9007"]},"file":[{"file_size":844353,"date_updated":"2021-11-26T07:16:49Z","date_created":"2021-11-26T07:16:49Z","file_id":"10345","success":1,"creator":"cchlebak","file_name":"2020_PhysRevLett_Forster.pdf","relation":"main_file","access_level":"open_access","checksum":"fbf2e1415e332d6add90222d60401a1d","content_type":"application/pdf"}],"issue":"22","external_id":{"pmid":["33315453"]},"date_updated":"2021-11-30T08:33:14Z","article_type":"original","quality_controlled":"1","date_published":"2020-11-23T00:00:00Z","language":[{"iso":"eng"}],"type":"journal_article","author":[{"full_name":"Forster, Joel C.","last_name":"Forster","first_name":"Joel C."},{"last_name":"Krausser","first_name":"Johannes","full_name":"Krausser, Johannes"},{"last_name":"Vuyyuru","first_name":"Manish R.","full_name":"Vuyyuru, Manish R."},{"last_name":"Baum","first_name":"Buzz","full_name":"Baum, Buzz"},{"id":"bf63d406-f056-11eb-b41d-f263a6566d8b","full_name":"Šarić, Anđela","first_name":"Anđela","last_name":"Šarić","orcid":"0000-0002-7854-2139"}],"file_date_updated":"2021-11-26T07:16:49Z","main_file_link":[{"url":"https://www.biorxiv.org/content/10.1101/2020.02.27.968149v1","open_access":"1"}],"publisher":"American Physical Society","ddc":["530"],"doi":"10.1103/physrevlett.125.228101","title":"Exploring the design rules for efficient membrane-reshaping nanostructures","status":"public","year":"2020","acknowledgement":"We acknowledge support from EPSRC (J. C. F.), MRC (B. B. and A. Š.), the ERC StG 802960 “NEPA” (J. K. and A. Š.), the Royal Society (A. Š.), and the United Kingdom Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC (EP/P020194/1).","oa_version":"Published Version","has_accepted_license":"1"},{"article_type":"original","external_id":{"pmid":["33049216"]},"date_updated":"2021-11-26T07:45:24Z","page":"1791-1799","issue":"9","language":[{"iso":"eng"}],"date_published":"2020-09-23T00:00:00Z","quality_controlled":"1","_id":"10346","month":"09","article_processing_charge":"No","publication_identifier":{"issn":["0006-3495"]},"acknowledgement":"We thank Melinda Duer, Patrick Mesquida, Lucy Colwell, Lucie Liu, Daan Frenkel, and Ivan Palaia for helpful discussions. We acknowledge support from the Engineering and Physical Sciences Research Council (A.E.H., L.K.D., and A.Š.), Biotechnology and Biological Sciences Research Council LIDo programme (N.G.G. and C.A.B.), the Royal Society (A.Š.), and the UK Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC ( EP/P020194/1).","status":"public","title":"Modeling fibrillogenesis of collagen-mimetic molecules","year":"2020","doi":"10.1016/j.bpj.2020.09.013","publisher":"Cell Press","oa_version":"Published Version","type":"journal_article","main_file_link":[{"url":"https://www.biorxiv.org/content/10.1101/2020.06.08.140061v1","open_access":"1"}],"author":[{"full_name":"Hafner, Anne E.","first_name":"Anne E.","last_name":"Hafner"},{"full_name":"Gyori, Noemi G.","last_name":"Gyori","first_name":"Noemi G."},{"last_name":"Bench","first_name":"Ciaran A.","full_name":"Bench, Ciaran A."},{"full_name":"Davis, Luke K.","first_name":"Luke K.","last_name":"Davis"},{"orcid":"0000-0002-7854-2139","last_name":"Šarić","first_name":"Anđela","full_name":"Šarić, Anđela","id":"bf63d406-f056-11eb-b41d-f263a6566d8b"}],"extern":"1","pmid":1,"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","volume":119,"intvolume":"       119","date_created":"2021-11-26T07:27:24Z","abstract":[{"lang":"eng","text":"One of the most robust examples of self-assembly in living organisms is the formation of collagen architectures. Collagen type I molecules are a crucial component of the extracellular matrix, where they self-assemble into fibrils of well-defined axial striped patterns. This striped fibrillar pattern is preserved across the animal kingdom and is important for the determination of cell phenotype, cell adhesion, and tissue regulation and signaling. The understanding of the physical processes that determine such a robust morphology of self-assembled collagen fibrils is currently almost completely missing. Here, we develop a minimal coarse-grained computational model to identify the physical principles of the assembly of collagen-mimetic molecules. We find that screened electrostatic interactions can drive the formation of collagen-like filaments of well-defined striped morphologies. The fibril axial pattern is determined solely by the distribution of charges on the molecule and is robust to the changes in protein concentration, monomer rigidity, and environmental conditions. We show that the striped fibrillar pattern cannot be easily predicted from the interactions between two monomers but is an emergent result of multibody interactions. Our results can help address collagen remodeling in diseases and aging and guide the design of collagen scaffolds for biotechnological applications."}],"publication_status":"published","publication":"Biophysical Journal","citation":{"apa":"Hafner, A. E., Gyori, N. G., Bench, C. A., Davis, L. K., &#38; Šarić, A. (2020). Modeling fibrillogenesis of collagen-mimetic molecules. <i>Biophysical Journal</i>. Cell Press. <a href=\"https://doi.org/10.1016/j.bpj.2020.09.013\">https://doi.org/10.1016/j.bpj.2020.09.013</a>","ieee":"A. E. Hafner, N. G. Gyori, C. A. Bench, L. K. Davis, and A. Šarić, “Modeling fibrillogenesis of collagen-mimetic molecules,” <i>Biophysical Journal</i>, vol. 119, no. 9. Cell Press, pp. 1791–1799, 2020.","chicago":"Hafner, Anne E., Noemi G. Gyori, Ciaran A. Bench, Luke K. Davis, and Anđela Šarić. “Modeling Fibrillogenesis of Collagen-Mimetic Molecules.” <i>Biophysical Journal</i>. Cell Press, 2020. <a href=\"https://doi.org/10.1016/j.bpj.2020.09.013\">https://doi.org/10.1016/j.bpj.2020.09.013</a>.","mla":"Hafner, Anne E., et al. “Modeling Fibrillogenesis of Collagen-Mimetic Molecules.” <i>Biophysical Journal</i>, vol. 119, no. 9, Cell Press, 2020, pp. 1791–99, doi:<a href=\"https://doi.org/10.1016/j.bpj.2020.09.013\">10.1016/j.bpj.2020.09.013</a>.","ama":"Hafner AE, Gyori NG, Bench CA, Davis LK, Šarić A. Modeling fibrillogenesis of collagen-mimetic molecules. <i>Biophysical Journal</i>. 2020;119(9):1791-1799. doi:<a href=\"https://doi.org/10.1016/j.bpj.2020.09.013\">10.1016/j.bpj.2020.09.013</a>","ista":"Hafner AE, Gyori NG, Bench CA, Davis LK, Šarić A. 2020. Modeling fibrillogenesis of collagen-mimetic molecules. Biophysical Journal. 119(9), 1791–1799.","short":"A.E. Hafner, N.G. Gyori, C.A. Bench, L.K. Davis, A. Šarić, Biophysical Journal 119 (2020) 1791–1799."},"keyword":["biophysics"],"oa":1,"scopus_import":"1","day":"23"},{"language":[{"iso":"eng"}],"quality_controlled":"1","date_published":"2020-09-14T00:00:00Z","article_type":"original","external_id":{"pmid":["32929030"]},"date_updated":"2021-11-26T08:59:06Z","page":"24251-24257","issue":"39","publication_identifier":{"eissn":["1091-6490"],"issn":["0027-8424"]},"_id":"10347","month":"09","article_processing_charge":"No","oa_version":"Published Version","acknowledgement":"We acknowledge support from Peterhouse, Cambridge (T.C.T.M.); the Swiss National Science Foundation (T.C.T.M.); the Royal Society (A.S. and S.C.); the Academy of Medical Sciences (A.S.); Sidney Sussex College, Cambridge (G.M.); Newnham College, Cambridge (G.T.H.); the Wellcome Trust (T.P.J.K.); the Cambridge Center for Misfolding Diseases (T.P.J.K. and M.V.); the Biotechnology and Biological Sciences Research Council (T.P.J.K.); the Frances and Augustus Newman Foundation (T.P.J.K.); and the Synapsis Foundation for Alzheimer’s disease (P.A.). The research leading to these results has received funding from the European Research Council (ERC) under the European Union’s Seventh Framework Program (FP7/2007-2013) through the ERC Grant PhysProt (Agreement 337969).","title":"Thermodynamic and kinetic design principles for amyloid-aggregation inhibitors","status":"public","year":"2020","doi":"10.1073/pnas.2006684117","publisher":"National Academy of Sciences","main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/content/10.1101/2020.02.22.960716"}],"author":[{"first_name":"Thomas C. T.","last_name":"Michaels","full_name":"Michaels, Thomas C. T."},{"orcid":"0000-0002-7854-2139","last_name":"Šarić","first_name":"Anđela","full_name":"Šarić, Anđela","id":"bf63d406-f056-11eb-b41d-f263a6566d8b"},{"last_name":"Meisl","first_name":"Georg","full_name":"Meisl, Georg"},{"full_name":"Heller, Gabriella T.","last_name":"Heller","first_name":"Gabriella T."},{"full_name":"Curk, Samo","last_name":"Curk","first_name":"Samo"},{"full_name":"Arosio, Paolo","first_name":"Paolo","last_name":"Arosio"},{"full_name":"Linse, Sara","last_name":"Linse","first_name":"Sara"},{"full_name":"Dobson, Christopher M.","last_name":"Dobson","first_name":"Christopher M."},{"full_name":"Vendruscolo, Michele","first_name":"Michele","last_name":"Vendruscolo"},{"last_name":"Knowles","first_name":"Tuomas P. J.","full_name":"Knowles, Tuomas P. J."}],"type":"journal_article","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","intvolume":"       117","volume":117,"date_created":"2021-11-26T07:48:27Z","pmid":1,"extern":"1","abstract":[{"lang":"eng","text":"Understanding the mechanism of action of compounds capable of inhibiting amyloid-fibril formation is critical to the development of potential therapeutics against protein-misfolding diseases. A fundamental challenge for progress is the range of possible target species and the disparate timescales involved, since the aggregating proteins are simultaneously the reactants, products, intermediates, and catalysts of the reaction. It is a complex problem, therefore, to choose the states of the aggregating proteins that should be bound by the compounds to achieve the most potent inhibition. We present here a comprehensive kinetic theory of amyloid-aggregation inhibition that reveals the fundamental thermodynamic and kinetic signatures characterizing effective inhibitors by identifying quantitative relationships between the aggregation and binding rate constants. These results provide general physical laws to guide the design and optimization of inhibitors of amyloid-fibril formation, revealing in particular the important role of on-rates in the binding of the inhibitors."}],"publication_status":"published","oa":1,"publication":"Proceedings of the National Academy of Sciences","citation":{"short":"T.C.T. Michaels, A. Šarić, G. Meisl, G.T. Heller, S. Curk, P. Arosio, S. Linse, C.M. Dobson, M. Vendruscolo, T.P.J. Knowles, Proceedings of the National Academy of Sciences 117 (2020) 24251–24257.","ista":"Michaels TCT, Šarić A, Meisl G, Heller GT, Curk S, Arosio P, Linse S, Dobson CM, Vendruscolo M, Knowles TPJ. 2020. Thermodynamic and kinetic design principles for amyloid-aggregation inhibitors. Proceedings of the National Academy of Sciences. 117(39), 24251–24257.","ama":"Michaels TCT, Šarić A, Meisl G, et al. Thermodynamic and kinetic design principles for amyloid-aggregation inhibitors. <i>Proceedings of the National Academy of Sciences</i>. 2020;117(39):24251-24257. doi:<a href=\"https://doi.org/10.1073/pnas.2006684117\">10.1073/pnas.2006684117</a>","mla":"Michaels, Thomas C. T., et al. “Thermodynamic and Kinetic Design Principles for Amyloid-Aggregation Inhibitors.” <i>Proceedings of the National Academy of Sciences</i>, vol. 117, no. 39, National Academy of Sciences, 2020, pp. 24251–57, doi:<a href=\"https://doi.org/10.1073/pnas.2006684117\">10.1073/pnas.2006684117</a>.","chicago":"Michaels, Thomas C. T., Anđela Šarić, Georg Meisl, Gabriella T. Heller, Samo Curk, Paolo Arosio, Sara Linse, Christopher M. Dobson, Michele Vendruscolo, and Tuomas P. J. Knowles. “Thermodynamic and Kinetic Design Principles for Amyloid-Aggregation Inhibitors.” <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences, 2020. <a href=\"https://doi.org/10.1073/pnas.2006684117\">https://doi.org/10.1073/pnas.2006684117</a>.","apa":"Michaels, T. C. T., Šarić, A., Meisl, G., Heller, G. T., Curk, S., Arosio, P., … Knowles, T. P. J. (2020). Thermodynamic and kinetic design principles for amyloid-aggregation inhibitors. <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2006684117\">https://doi.org/10.1073/pnas.2006684117</a>","ieee":"T. C. T. Michaels <i>et al.</i>, “Thermodynamic and kinetic design principles for amyloid-aggregation inhibitors,” <i>Proceedings of the National Academy of Sciences</i>, vol. 117, no. 39. National Academy of Sciences, pp. 24251–24257, 2020."},"keyword":["multidisciplinary"],"day":"14","scopus_import":"1"},{"oa_version":"Published Version","year":"2020","status":"public","title":"An ESCRT-III polymerization sequence drives membrane deformation and fission","acknowledgement":"The authors thank Nicolas Chiaruttini, Jean Gruenberg, and Lena Harker-Kirschneck for careful correction of this manuscript and helpful discussions. The authors want to thank the NCCR Chemical Biology for constant support during this project. A.R. acknowledges funding from the Swiss National Fund for Research (31003A_130520, 31003A_149975, and 31003A_173087) and the European Research Council Consolidator (311536). A.Š. acknowledges the European Research Council (802960). B.B. thanks the BBSRC (BB/K009001/1) and Wellcome Trust (203276/Z/16/Z) for support. J.M.v.F. acknowledges funding through an EMBO Long-Term Fellowship (ALTF 1065-2015), the European Commission FP7 (Marie Curie Actions, LTFCOFUND2013, and GA-2013-609409), and a Transitional Postdoc fellowship (2015/345) from the Swiss SystemsX.ch initiative, evaluated by the Swiss National Science Foundation and Swiss National Science Foundation Research (SNSF SINERGIA 160728/1 [leader, Sophie Martin]).","publisher":"Elsevier","doi":"10.1016/j.cell.2020.07.021","main_file_link":[{"url":"https://www.sciencedirect.com/science/article/pii/S0092867420309296","open_access":"1"}],"author":[{"full_name":"Pfitzner, Anna-Katharina","last_name":"Pfitzner","first_name":"Anna-Katharina"},{"full_name":"Mercier, Vincent","first_name":"Vincent","last_name":"Mercier"},{"last_name":"Jiang","first_name":"Xiuyun","full_name":"Jiang, Xiuyun"},{"first_name":"Joachim","last_name":"Moser von Filseck","full_name":"Moser von Filseck, Joachim"},{"full_name":"Baum, Buzz","last_name":"Baum","first_name":"Buzz"},{"id":"bf63d406-f056-11eb-b41d-f263a6566d8b","first_name":"Anđela","last_name":"Šarić","full_name":"Šarić, Anđela","orcid":"0000-0002-7854-2139"},{"first_name":"Aurélien","last_name":"Roux","full_name":"Roux, Aurélien"}],"type":"journal_article","language":[{"iso":"eng"}],"quality_controlled":"1","date_published":"2020-08-18T00:00:00Z","date_updated":"2021-11-26T08:58:37Z","external_id":{"pmid":["32814015"]},"article_type":"original","issue":"5","page":"1140-1155.e18","publication_identifier":{"issn":["0092-8674"]},"_id":"10348","article_processing_charge":"No","month":"08","oa":1,"publication":"Cell","keyword":["general biochemistry","genetics and molecular biology"],"citation":{"short":"A.-K. Pfitzner, V. Mercier, X. Jiang, J. Moser von Filseck, B. Baum, A. Šarić, A. Roux, Cell 182 (2020) 1140–1155.e18.","ama":"Pfitzner A-K, Mercier V, Jiang X, et al. An ESCRT-III polymerization sequence drives membrane deformation and fission. <i>Cell</i>. 2020;182(5):1140-1155.e18. doi:<a href=\"https://doi.org/10.1016/j.cell.2020.07.021\">10.1016/j.cell.2020.07.021</a>","ista":"Pfitzner A-K, Mercier V, Jiang X, Moser von Filseck J, Baum B, Šarić A, Roux A. 2020. An ESCRT-III polymerization sequence drives membrane deformation and fission. Cell. 182(5), 1140–1155.e18.","mla":"Pfitzner, Anna-Katharina, et al. “An ESCRT-III Polymerization Sequence Drives Membrane Deformation and Fission.” <i>Cell</i>, vol. 182, no. 5, Elsevier, 2020, p. 1140–1155.e18, doi:<a href=\"https://doi.org/10.1016/j.cell.2020.07.021\">10.1016/j.cell.2020.07.021</a>.","chicago":"Pfitzner, Anna-Katharina, Vincent Mercier, Xiuyun Jiang, Joachim Moser von Filseck, Buzz Baum, Anđela Šarić, and Aurélien Roux. “An ESCRT-III Polymerization Sequence Drives Membrane Deformation and Fission.” <i>Cell</i>. Elsevier, 2020. <a href=\"https://doi.org/10.1016/j.cell.2020.07.021\">https://doi.org/10.1016/j.cell.2020.07.021</a>.","ieee":"A.-K. Pfitzner <i>et al.</i>, “An ESCRT-III polymerization sequence drives membrane deformation and fission,” <i>Cell</i>, vol. 182, no. 5. Elsevier, p. 1140–1155.e18, 2020.","apa":"Pfitzner, A.-K., Mercier, V., Jiang, X., Moser von Filseck, J., Baum, B., Šarić, A., &#38; Roux, A. (2020). An ESCRT-III polymerization sequence drives membrane deformation and fission. <i>Cell</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.cell.2020.07.021\">https://doi.org/10.1016/j.cell.2020.07.021</a>"},"day":"18","scopus_import":"1","volume":182,"intvolume":"       182","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_created":"2021-11-26T08:02:27Z","pmid":1,"extern":"1","abstract":[{"text":"The endosomal sorting complex required for transport-III (ESCRT-III) catalyzes membrane fission from within membrane necks, a process that is essential for many cellular functions, from cell division to lysosome degradation and autophagy. How it breaks membranes, though, remains unknown. Here, we characterize a sequential polymerization of ESCRT-III subunits that, driven by a recruitment cascade and by continuous subunit-turnover powered by the ATPase Vps4, induces membrane deformation and fission. During this process, the exchange of Vps24 for Did2 induces a tilt in the polymer-membrane interface, which triggers transition from flat spiral polymers to helical filament to drive the formation of membrane protrusions, and ends with the formation of a highly constricted Did2-Ist1 co-polymer that we show is competent to promote fission when bound on the inside of membrane necks. Overall, our results suggest a mechanism of stepwise changes in ESCRT-III filament structure and mechanical properties via exchange of the filament subunits to catalyze ESCRT-III activity.","lang":"eng"}],"publication_status":"published"},{"intvolume":"       369","volume":369,"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","date_created":"2021-11-26T08:21:34Z","pmid":1,"extern":"1","publication_status":"published","abstract":[{"text":"Sulfolobus acidocaldarius is the closest experimentally tractable archaeal relative of eukaryotes and, despite lacking obvious cyclin-dependent kinase and cyclin homologs, has an ordered eukaryote-like cell cycle with distinct phases of DNA replication and division. Here, in exploring the mechanism of cell division in S. acidocaldarius, we identify a role for the archaeal proteasome in regulating the transition from the end of one cell cycle to the beginning of the next. Further, we identify the archaeal ESCRT-III homolog, CdvB, as a key target of the proteasome and show that its degradation triggers division by allowing constriction of the CdvB1:CdvB2 ESCRT-III division ring. These findings offer a minimal mechanism for ESCRT-III–mediated membrane remodeling and point to a conserved role for the proteasome in eukaryotic and archaeal cell cycle control.","lang":"eng"}],"oa":1,"publication":"Science","keyword":["multidisciplinary"],"citation":{"ieee":"G. Tarrason Risa <i>et al.</i>, “The proteasome controls ESCRT-III–mediated cell division in an archaeon,” <i>Science</i>, vol. 369, no. 6504. American Association for the Advancement of Science, 2020.","apa":"Tarrason Risa, G., Hurtig, F., Bray, S., Hafner, A. E., Harker-Kirschneck, L., Faull, P., … Baum, B. (2020). The proteasome controls ESCRT-III–mediated cell division in an archaeon. <i>Science</i>. American Association for the Advancement of Science. <a href=\"https://doi.org/10.1126/science.aaz2532\">https://doi.org/10.1126/science.aaz2532</a>","chicago":"Tarrason Risa, Gabriel, Fredrik Hurtig, Sian Bray, Anne E. Hafner, Lena Harker-Kirschneck, Peter Faull, Colin Davis, et al. “The Proteasome Controls ESCRT-III–Mediated Cell Division in an Archaeon.” <i>Science</i>. American Association for the Advancement of Science, 2020. <a href=\"https://doi.org/10.1126/science.aaz2532\">https://doi.org/10.1126/science.aaz2532</a>.","mla":"Tarrason Risa, Gabriel, et al. “The Proteasome Controls ESCRT-III–Mediated Cell Division in an Archaeon.” <i>Science</i>, vol. 369, no. 6504, American Association for the Advancement of Science, 2020, doi:<a href=\"https://doi.org/10.1126/science.aaz2532\">10.1126/science.aaz2532</a>.","ista":"Tarrason Risa G, Hurtig F, Bray S, Hafner AE, Harker-Kirschneck L, Faull P, Davis C, Papatziamou D, Mutavchiev DR, Fan C, Meneguello L, Arashiro Pulschen A, Dey G, Culley S, Kilkenny M, Souza DP, Pellegrini L, de Bruin RAM, Henriques R, Snijders AP, Šarić A, Lindås A-C, Robinson NP, Baum B. 2020. The proteasome controls ESCRT-III–mediated cell division in an archaeon. Science. 369(6504).","ama":"Tarrason Risa G, Hurtig F, Bray S, et al. The proteasome controls ESCRT-III–mediated cell division in an archaeon. <i>Science</i>. 2020;369(6504). doi:<a href=\"https://doi.org/10.1126/science.aaz2532\">10.1126/science.aaz2532</a>","short":"G. Tarrason Risa, F. Hurtig, S. Bray, A.E. Hafner, L. Harker-Kirschneck, P. Faull, C. Davis, D. Papatziamou, D.R. Mutavchiev, C. Fan, L. Meneguello, A. Arashiro Pulschen, G. Dey, S. Culley, M. Kilkenny, D.P. Souza, L. Pellegrini, R.A.M. de Bruin, R. Henriques, A.P. Snijders, A. Šarić, A.-C. Lindås, N.P. Robinson, B. Baum, Science 369 (2020)."},"day":"07","scopus_import":"1","language":[{"iso":"eng"}],"date_published":"2020-08-07T00:00:00Z","quality_controlled":"1","external_id":{"pmid":["32764038"]},"date_updated":"2021-11-26T08:58:33Z","article_type":"original","issue":"6504","publication_identifier":{"issn":["0036-8075"],"eissn":["1095-9203"]},"_id":"10349","article_processing_charge":"No","month":"08","oa_version":"Preprint","title":"The proteasome controls ESCRT-III–mediated cell division in an archaeon","status":"public","year":"2020","acknowledgement":"We thank the MRC LMCB at UCL for their support; the flow cytometry STP at the Francis Crick Institute for assistance, with special thanks to S. Purewal and D. Davis; C. Bertoli for mentorship\r\nand advice; J. M. Garcia-Arcos for help early on in this project; the entire Baum lab for their input throughout the project; the Albers lab for advice and reagents, with special thanks to M. Van Wolferen and S. Albers; the members of the Wellcome consortium for archaeal cytoskeleton studies for advice and comments; and J. Löwe, S. Oliferenko, M. Balasubramanian, and D. Gerlich for discussions and advice on the manuscript. N.P.R. and S.B. would like to thank N. Rzechorzek, A. Simon, and S. Anjum for discussion and advice.","publisher":"American Association for the Advancement of Science","doi":"10.1126/science.aaz2532","main_file_link":[{"url":"https://www.biorxiv.org/content/10.1101/774273v1","open_access":"1"}],"author":[{"full_name":"Tarrason Risa, Gabriel","first_name":"Gabriel","last_name":"Tarrason Risa"},{"full_name":"Hurtig, Fredrik","first_name":"Fredrik","last_name":"Hurtig"},{"first_name":"Sian","last_name":"Bray","full_name":"Bray, Sian"},{"last_name":"Hafner","first_name":"Anne E.","full_name":"Hafner, Anne E."},{"first_name":"Lena","last_name":"Harker-Kirschneck","full_name":"Harker-Kirschneck, Lena"},{"first_name":"Peter","last_name":"Faull","full_name":"Faull, Peter"},{"last_name":"Davis","first_name":"Colin","full_name":"Davis, Colin"},{"first_name":"Dimitra","last_name":"Papatziamou","full_name":"Papatziamou, Dimitra"},{"first_name":"Delyan R.","last_name":"Mutavchiev","full_name":"Mutavchiev, Delyan R."},{"full_name":"Fan, Catherine","last_name":"Fan","first_name":"Catherine"},{"first_name":"Leticia","last_name":"Meneguello","full_name":"Meneguello, Leticia"},{"full_name":"Arashiro Pulschen, Andre","last_name":"Arashiro Pulschen","first_name":"Andre"},{"full_name":"Dey, Gautam","first_name":"Gautam","last_name":"Dey"},{"full_name":"Culley, Siân","last_name":"Culley","first_name":"Siân"},{"first_name":"Mairi","last_name":"Kilkenny","full_name":"Kilkenny, Mairi"},{"last_name":"Souza","first_name":"Diorge P.","full_name":"Souza, Diorge P."},{"full_name":"Pellegrini, Luca","first_name":"Luca","last_name":"Pellegrini"},{"first_name":"Robertus A. M.","last_name":"de Bruin","full_name":"de Bruin, Robertus A. M."},{"full_name":"Henriques, Ricardo","first_name":"Ricardo","last_name":"Henriques"},{"full_name":"Snijders, Ambrosius P.","first_name":"Ambrosius P.","last_name":"Snijders"},{"orcid":"0000-0002-7854-2139","full_name":"Šarić, Anđela","first_name":"Anđela","last_name":"Šarić","id":"bf63d406-f056-11eb-b41d-f263a6566d8b"},{"last_name":"Lindås","first_name":"Ann-Christin","full_name":"Lindås, Ann-Christin"},{"full_name":"Robinson, Nicholas P.","first_name":"Nicholas P.","last_name":"Robinson"},{"last_name":"Baum","first_name":"Buzz","full_name":"Baum, Buzz"}],"type":"journal_article"}]