--- _id: '8698' abstract: - lang: eng text: The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable with or better than that of state-of-the-art models. Importantly, the model can be learned using a small number of samples and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation. acknowledgement: We thank Udi Karpas, Roy Harpaz, Tal Tamir, Adam Haber, and Amir Bar for discussions and suggestions; and especially Oren Forkosh and Walter Senn for invaluable discussions of the learning rule. This work was supported by European Research Council Grant 311238 (to E.S.) and Israel Science Foundation Grant 1629/12 (to E.S.); as well as research support from Martin Kushner Schnur and Mr. and Mrs. Lawrence Feis (E.S.); National Institute of Mental Health Grant R01MH109180 (to R.K.); a Pew Scholarship in Biomedical Sciences (to R.K.); Simons Collaboration on the Global Brain Grant 542997 (to R.K. and E.S.); and a CRCNS (Collaborative Research in Computational Neuroscience) grant (to R.K. and E.S.). article_processing_charge: No article_type: original author: - first_name: Ori full_name: Maoz, Ori last_name: Maoz - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Mohamad Saleh full_name: Esteki, Mohamad Saleh last_name: Esteki - first_name: Roozbeh full_name: Kiani, Roozbeh last_name: Kiani - first_name: Elad full_name: Schneidman, Elad last_name: Schneidman citation: ama: Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. 2020;117(40):25066-25073. doi:10.1073/pnas.1912804117 apa: Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2020). Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.1912804117 chicago: Maoz, Ori, Gašper Tkačik, Mohamad Saleh Esteki, Roozbeh Kiani, and Elad Schneidman. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences, 2020. https://doi.org/10.1073/pnas.1912804117. ieee: O. Maoz, G. Tkačik, M. S. Esteki, R. Kiani, and E. Schneidman, “Learning probabilistic neural representations with randomly connected circuits,” Proceedings of the National Academy of Sciences of the United States of America, vol. 117, no. 40. National Academy of Sciences, pp. 25066–25073, 2020. ista: Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. 2020. Learning probabilistic neural representations with randomly connected circuits. Proceedings of the National Academy of Sciences of the United States of America. 117(40), 25066–25073. mla: Maoz, Ori, et al. “Learning Probabilistic Neural Representations with Randomly Connected Circuits.” Proceedings of the National Academy of Sciences of the United States of America, vol. 117, no. 40, National Academy of Sciences, 2020, pp. 25066–73, doi:10.1073/pnas.1912804117. short: O. Maoz, G. Tkačik, M.S. Esteki, R. Kiani, E. Schneidman, Proceedings of the National Academy of Sciences of the United States of America 117 (2020) 25066–25073. date_created: 2020-10-25T23:01:16Z date_published: 2020-10-06T00:00:00Z date_updated: 2023-08-22T12:11:23Z day: '06' ddc: - '570' department: - _id: GaTk doi: 10.1073/pnas.1912804117 external_id: isi: - '000579045200012' pmid: - '32948691' file: - access_level: open_access checksum: c6a24fdecf3f28faf447078e7a274a88 content_type: application/pdf creator: cziletti date_created: 2020-10-27T14:57:50Z date_updated: 2020-10-27T14:57:50Z file_id: '8713' file_name: 2020_PNAS_Maoz.pdf file_size: 1755359 relation: main_file success: 1 file_date_updated: 2020-10-27T14:57:50Z has_accepted_license: '1' intvolume: ' 117' isi: 1 issue: '40' language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: 25066-25073 pmid: 1 publication: Proceedings of the National Academy of Sciences of the United States of America publication_identifier: eissn: - '10916490' issn: - '00278424' publication_status: published publisher: National Academy of Sciences quality_controlled: '1' scopus_import: '1' status: public title: Learning probabilistic neural representations with randomly connected circuits tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 117 year: '2020' ... --- _id: '8704' abstract: - lang: eng text: Traditional robotic control suits require profound task-specific knowledge for designing, building and testing control software. The rise of Deep Learning has enabled end-to-end solutions to be learned entirely from data, requiring minimal knowledge about the application area. We design a learning scheme to train end-to-end linear dynamical systems (LDS)s by gradient descent in imitation learning robotic domains. We introduce a new regularization loss component together with a learning algorithm that improves the stability of the learned autonomous system, by forcing the eigenvalues of the internal state updates of an LDS to be negative reals. We evaluate our approach on a series of real-life and simulated robotic experiments, in comparison to linear and nonlinear Recurrent Neural Network (RNN) architectures. Our results show that our stabilizing method significantly improves test performance of LDS, enabling such linear models to match the performance of contemporary nonlinear RNN architectures. A video of the obstacle avoidance performance of our method on a mobile robot, in unseen environments, compared to other methods can be viewed at https://youtu.be/mhEsCoNao5E. acknowledgement: M.L. is supported in parts by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). R.H., and R.G. are partially supported by the Horizon-2020 ECSELProject grant No. 783163 (iDev40), and the Austrian Research Promotion Agency (FFG), Project No. 860424. R.H. and D.R. is partially supported by the Boeing Company. alternative_title: - ICRA article_processing_charge: No author: - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Ramin full_name: Hasani, Ramin last_name: Hasani - first_name: Daniela full_name: Rus, Daniela last_name: Rus - first_name: Radu full_name: Grosu, Radu last_name: Grosu citation: ama: 'Lechner M, Hasani R, Rus D, Grosu R. Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. In: Proceedings - IEEE International Conference on Robotics and Automation. IEEE; 2020:5446-5452. doi:10.1109/ICRA40945.2020.9196608' apa: 'Lechner, M., Hasani, R., Rus, D., & Grosu, R. (2020). Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 5446–5452). Paris, France: IEEE. https://doi.org/10.1109/ICRA40945.2020.9196608' chicago: Lechner, Mathias, Ramin Hasani, Daniela Rus, and Radu Grosu. “Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-End Robot Learning Scheme.” In Proceedings - IEEE International Conference on Robotics and Automation, 5446–52. IEEE, 2020. https://doi.org/10.1109/ICRA40945.2020.9196608. ieee: M. Lechner, R. Hasani, D. Rus, and R. Grosu, “Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme,” in Proceedings - IEEE International Conference on Robotics and Automation, Paris, France, 2020, pp. 5446–5452. ista: 'Lechner M, Hasani R, Rus D, Grosu R. 2020. Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. Proceedings - IEEE International Conference on Robotics and Automation. ICRA: International Conference on Robotics and Automation, ICRA, , 5446–5452.' mla: Lechner, Mathias, et al. “Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-End Robot Learning Scheme.” Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2020, pp. 5446–52, doi:10.1109/ICRA40945.2020.9196608. short: M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2020, pp. 5446–5452. conference: end_date: 2020-08-31 location: Paris, France name: 'ICRA: International Conference on Robotics and Automation' start_date: 2020-05-31 date_created: 2020-10-25T23:01:19Z date_published: 2020-05-01T00:00:00Z date_updated: 2023-08-22T10:40:15Z day: '01' ddc: - '000' department: - _id: ToHe doi: 10.1109/ICRA40945.2020.9196608 external_id: isi: - '000712319503110' file: - access_level: open_access checksum: fccf7b986ac78046918a298cc6849a50 content_type: application/pdf creator: dernst date_created: 2020-11-06T10:58:49Z date_updated: 2020-11-06T10:58:49Z file_id: '8733' file_name: 2020_ICRA_Lechner.pdf file_size: 1070010 relation: main_file success: 1 file_date_updated: 2020-11-06T10:58:49Z has_accepted_license: '1' isi: 1 language: - iso: eng month: '05' oa: 1 oa_version: Submitted Version page: 5446-5452 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: Proceedings - IEEE International Conference on Robotics and Automation publication_identifier: isbn: - '9781728173955' issn: - '10504729' publication_status: published publisher: IEEE quality_controlled: '1' scopus_import: '1' status: public title: Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme type: conference user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 year: '2020' ... --- _id: '14096' abstract: - lang: eng text: A binary neutron star merger has been observed in a multi-messenger detection of gravitational wave (GW) and electromagnetic (EM) radiation. Binary neutron stars that merge within a Hubble time, as well as many other compact binaries, are expected to form via common envelope evolution. Yet five decades of research on common envelope evolution have not yet resulted in a satisfactory understanding of the multi-spatial multi-timescale evolution for the systems that lead to compact binaries. In this paper, we report on the first successful simulations of common envelope ejection leading to binary neutron star formation in 3D hydrodynamics. We simulate the dynamical inspiral phase of the interaction between a 12M⊙ red supergiant and a 1.4M⊙ neutron star for different initial separations and initial conditions. For all of our simulations, we find complete envelope ejection and final orbital separations of af≈1.3-5.1R⊙ depending on the simulation and criterion, leading to binary neutron stars that can merge within a Hubble time. We find αCE-equivalent efficiencies of ≈0.1-2.7 depending on the simulation and criterion, but this may be specific for these extended progenitors. We fully resolve the core of the star to ≲0.005R⊙ and our 3D hydrodynamics simulations are informed by an adjusted 1D analytic energy formalism and a 2D kinematics study in order to overcome the prohibitive computational cost of simulating these systems. The framework we develop in this paper can be used to simulate a wide variety of interactions between stars, from stellar mergers to common envelope episodes leading to GW sources. article_number: '2011.06630' article_processing_charge: No author: - first_name: Jamie A. P. Law-Smith full_name: Jamie A. P. Law-Smith, Jamie A. P. Law-Smith last_name: Jamie A. P. Law-Smith - first_name: Rosa Wallace full_name: Everson, Rosa Wallace last_name: Everson - first_name: Enrico Ramirez-Ruiz full_name: Enrico Ramirez-Ruiz, Enrico Ramirez-Ruiz last_name: Enrico Ramirez-Ruiz - first_name: Selma E. de full_name: Mink, Selma E. de last_name: Mink - first_name: Lieke A. C. van full_name: Son, Lieke A. C. van last_name: Son - first_name: Ylva Louise Linsdotter full_name: Götberg, Ylva Louise Linsdotter id: d0648d0c-0f64-11ee-a2e0-dd0faa2e4f7d last_name: Götberg orcid: 0000-0002-6960-6911 - first_name: Stefan full_name: Zellmann, Stefan last_name: Zellmann - first_name: Alejandro Vigna-Gómez full_name: Alejandro Vigna-Gómez, Alejandro Vigna-Gómez last_name: Alejandro Vigna-Gómez - first_name: Mathieu full_name: Renzo, Mathieu last_name: Renzo - first_name: Samantha full_name: Wu, Samantha last_name: Wu - first_name: Sophie L. full_name: Schrøder, Sophie L. last_name: Schrøder - first_name: Ryan J. full_name: Foley, Ryan J. last_name: Foley - first_name: Tenley Hutchinson-Smith full_name: Tenley Hutchinson-Smith, Tenley Hutchinson-Smith last_name: Tenley Hutchinson-Smith citation: ama: Jamie A. P. Law-Smith JAPL-S, Everson RW, Enrico Ramirez-Ruiz ER-R, et al. Successful common envelope ejection and binary neutron star formation in 3D hydrodynamics. arXiv. doi:10.48550/arXiv.2011.06630 apa: Jamie A. P. Law-Smith, J. A. P. L.-S., Everson, R. W., Enrico Ramirez-Ruiz, E. R.-R., Mink, S. E. de, Son, L. A. C. van, Götberg, Y. L. L., … Tenley Hutchinson-Smith, T. H.-S. (n.d.). Successful common envelope ejection and binary neutron star formation in 3D hydrodynamics. arXiv. https://doi.org/10.48550/arXiv.2011.06630 chicago: Jamie A. P. Law-Smith, Jamie A. P. Law-Smith, Rosa Wallace Everson, Enrico Ramirez-Ruiz Enrico Ramirez-Ruiz, Selma E. de Mink, Lieke A. C. van Son, Ylva Louise Linsdotter Götberg, Stefan Zellmann, et al. “Successful Common Envelope Ejection and Binary Neutron Star Formation in 3D Hydrodynamics.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2011.06630. ieee: J. A. P. L.-S. Jamie A. P. Law-Smith et al., “Successful common envelope ejection and binary neutron star formation in 3D hydrodynamics,” arXiv. . ista: Jamie A. P. Law-Smith JAPL-S, Everson RW, Enrico Ramirez-Ruiz ER-R, Mink SE de, Son LAC van, Götberg YLL, Zellmann S, Alejandro Vigna-Gómez AV-G, Renzo M, Wu S, Schrøder SL, Foley RJ, Tenley Hutchinson-Smith TH-S. Successful common envelope ejection and binary neutron star formation in 3D hydrodynamics. arXiv, 2011.06630. mla: Jamie A. P. Law-Smith, Jamie A. P. Law-Smith, et al. “Successful Common Envelope Ejection and Binary Neutron Star Formation in 3D Hydrodynamics.” ArXiv, 2011.06630, doi:10.48550/arXiv.2011.06630. short: J.A.P.L.-S. Jamie A. P. Law-Smith, R.W. Everson, E.R.-R. Enrico Ramirez-Ruiz, S.E. de Mink, L.A.C. van Son, Y.L.L. Götberg, S. Zellmann, A.V.-G. Alejandro Vigna-Gómez, M. Renzo, S. Wu, S.L. Schrøder, R.J. Foley, T.H.-S. Tenley Hutchinson-Smith, ArXiv (n.d.). date_created: 2023-08-21T10:10:41Z date_published: 2020-11-12T00:00:00Z date_updated: 2023-08-22T11:03:00Z day: '12' doi: 10.48550/arXiv.2011.06630 external_id: arxiv: - '2011.06630' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2011.06630 month: '11' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Successful common envelope ejection and binary neutron star formation in 3D hydrodynamics type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '8699' abstract: - lang: eng text: In the high spin–orbit-coupled Sr2IrO4, the high sensitivity of the ground state to the details of the local lattice structure shows a large potential for the manipulation of the functional properties by inducing local lattice distortions. We use epitaxial strain to modify the Ir–O bond geometry in Sr2IrO4 and perform momentum-dependent resonant inelastic X-ray scattering (RIXS) at the metal and at the ligand sites to unveil the response of the low-energy elementary excitations. We observe that the pseudospin-wave dispersion for tensile-strained Sr2IrO4 films displays large softening along the [h,0] direction, while along the [h,h] direction it shows hardening. This evolution reveals a renormalization of the magnetic interactions caused by a strain-driven cross-over from anisotropic to isotropic interactions between the magnetic moments. Moreover, we detect dispersive electron–hole pair excitations which shift to lower (higher) energies upon compressive (tensile) strain, manifesting a reduction (increase) in the size of the charge gap. This behavior shows an intimate coupling between charge excitations and lattice distortions in Sr2IrO4, originating from the modified hopping elements between the t2g orbitals. Our work highlights the central role played by the lattice degrees of freedom in determining both the pseudospin and charge excitations of Sr2IrO4 and provides valuable information toward the control of the ground state of complex oxides in the presence of high spin–orbit coupling. acknowledgement: 'We gratefully acknowledge C. Sahle for experimental support at the ID20 beamline of the ESRF. The soft X-ray experiments were carried out at the ADRESS beamline of the Swiss Light Source, Paul Scherrer Institut (PSI). E. Paris and T.S. thank X. Lu and C. Monney for valuable discussions. The work at PSI is supported by the Swiss National Science Foundation (SNSF) through Project 200021_178867, the NCCR (National Centre of Competence in Research) MARVEL (Materials’ Revolution: Computational Design and Discovery of Novel Materials) and the Sinergia network Mott Physics Beyond the Heisenberg Model (MPBH) (SNSF Research Grants CRSII2_160765/1 and CRSII2_141962). K.W. acknowledges support by the Narodowe Centrum Nauki Projects 2016/22/E/ST3/00560 and 2016/23/B/ST3/00839. E.M.P. and M.N. acknowledge funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreements 754411 and 701647, respectively. M.R. was supported by the Swiss National Science Foundation under Project 200021 – 182695. This research used resources of the APS, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract DE-AC02-06CH11357.' article_processing_charge: No article_type: original author: - first_name: Eugenio full_name: Paris, Eugenio last_name: Paris - first_name: Yi full_name: Tseng, Yi last_name: Tseng - first_name: Ekaterina full_name: Paerschke, Ekaterina id: 8275014E-6063-11E9-9B7F-6338E6697425 last_name: Paerschke orcid: 0000-0003-0853-8182 - first_name: Wenliang full_name: Zhang, Wenliang last_name: Zhang - first_name: Mary H full_name: Upton, Mary H last_name: Upton - first_name: Anna full_name: Efimenko, Anna last_name: Efimenko - first_name: Katharina full_name: Rolfs, Katharina last_name: Rolfs - first_name: Daniel E full_name: McNally, Daniel E last_name: McNally - first_name: Laura full_name: Maurel, Laura last_name: Maurel - first_name: Muntaser full_name: Naamneh, Muntaser last_name: Naamneh - first_name: Marco full_name: Caputo, Marco last_name: Caputo - first_name: Vladimir N full_name: Strocov, Vladimir N last_name: Strocov - first_name: Zhiming full_name: Wang, Zhiming last_name: Wang - first_name: Diego full_name: Casa, Diego last_name: Casa - first_name: Christof W full_name: Schneider, Christof W last_name: Schneider - first_name: Ekaterina full_name: Pomjakushina, Ekaterina last_name: Pomjakushina - first_name: Krzysztof full_name: Wohlfeld, Krzysztof last_name: Wohlfeld - first_name: Milan full_name: Radovic, Milan last_name: Radovic - first_name: Thorsten full_name: Schmitt, Thorsten last_name: Schmitt citation: ama: Paris E, Tseng Y, Paerschke E, et al. Strain engineering of the charge and spin-orbital interactions in Sr2IrO4. Proceedings of the National Academy of Sciences of the United States of America. 2020;117(40):24764-24770. doi:10.1073/pnas.2012043117 apa: Paris, E., Tseng, Y., Paerschke, E., Zhang, W., Upton, M. H., Efimenko, A., … Schmitt, T. (2020). Strain engineering of the charge and spin-orbital interactions in Sr2IrO4. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.2012043117 chicago: Paris, Eugenio, Yi Tseng, Ekaterina Paerschke, Wenliang Zhang, Mary H Upton, Anna Efimenko, Katharina Rolfs, et al. “Strain Engineering of the Charge and Spin-Orbital Interactions in Sr2IrO4.” Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences, 2020. https://doi.org/10.1073/pnas.2012043117. ieee: E. Paris et al., “Strain engineering of the charge and spin-orbital interactions in Sr2IrO4,” Proceedings of the National Academy of Sciences of the United States of America, vol. 117, no. 40. National Academy of Sciences, pp. 24764–24770, 2020. ista: Paris E, Tseng Y, Paerschke E, Zhang W, Upton MH, Efimenko A, Rolfs K, McNally DE, Maurel L, Naamneh M, Caputo M, Strocov VN, Wang Z, Casa D, Schneider CW, Pomjakushina E, Wohlfeld K, Radovic M, Schmitt T. 2020. Strain engineering of the charge and spin-orbital interactions in Sr2IrO4. Proceedings of the National Academy of Sciences of the United States of America. 117(40), 24764–24770. mla: Paris, Eugenio, et al. “Strain Engineering of the Charge and Spin-Orbital Interactions in Sr2IrO4.” Proceedings of the National Academy of Sciences of the United States of America, vol. 117, no. 40, National Academy of Sciences, 2020, pp. 24764–70, doi:10.1073/pnas.2012043117. short: E. Paris, Y. Tseng, E. Paerschke, W. Zhang, M.H. Upton, A. Efimenko, K. Rolfs, D.E. McNally, L. Maurel, M. Naamneh, M. Caputo, V.N. Strocov, Z. Wang, D. Casa, C.W. Schneider, E. Pomjakushina, K. Wohlfeld, M. Radovic, T. Schmitt, Proceedings of the National Academy of Sciences of the United States of America 117 (2020) 24764–24770. date_created: 2020-10-25T23:01:17Z date_published: 2020-10-06T00:00:00Z date_updated: 2023-08-22T12:11:52Z day: '06' ddc: - '530' department: - _id: MiLe doi: 10.1073/pnas.2012043117 ec_funded: 1 external_id: arxiv: - '2009.12262' isi: - '000579059100029' pmid: - '32958669' file: - access_level: open_access checksum: 1638fa36b442e2868576c6dd7d6dc505 content_type: application/pdf creator: cziletti date_created: 2020-10-28T11:53:12Z date_updated: 2020-10-28T11:53:12Z file_id: '8715' file_name: 2020_PNAS_Paris.pdf file_size: 1176522 relation: main_file success: 1 file_date_updated: 2020-10-28T11:53:12Z has_accepted_license: '1' intvolume: ' 117' isi: 1 issue: '40' language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: 24764-24770 pmid: 1 project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Proceedings of the National Academy of Sciences of the United States of America publication_identifier: eissn: - '10916490' issn: - '00278424' publication_status: published publisher: National Academy of Sciences quality_controlled: '1' scopus_import: '1' status: public title: Strain engineering of the charge and spin-orbital interactions in Sr2IrO4 tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 117 year: '2020' ... --- _id: '8737' abstract: - lang: eng text: Mitochondrial complex I couples NADH:ubiquinone oxidoreduction to proton pumping by an unknown mechanism. Here, we present cryo-electron microscopy structures of ovine complex I in five different conditions, including turnover, at resolutions up to 2.3 to 2.5 angstroms. Resolved water molecules allowed us to experimentally define the proton translocation pathways. Quinone binds at three positions along the quinone cavity, as does the inhibitor rotenone that also binds within subunit ND4. Dramatic conformational changes around the quinone cavity couple the redox reaction to proton translocation during open-to-closed state transitions of the enzyme. In the induced deactive state, the open conformation is arrested by the ND6 subunit. We propose a detailed molecular coupling mechanism of complex I, which is an unexpected combination of conformational changes and electrostatic interactions. acknowledged_ssus: - _id: LifeSc - _id: EM-Fac acknowledgement: We thank J. Novacek (CEITEC Brno) and V.-V. Hodirnau (IST Austria) for their help with collecting cryo-EM datasets. We thank the IST Life Science and Electron Microscopy Facilities for providing equipment. This work has been supported by iNEXT,project number 653706, funded by the Horizon 2020 program of the European Union. This article reflects only the authors’view,and the European Commission is not responsible for any use that may be made of the information it contains. CIISB research infrastructure project LM2015043 funded by MEYS CR is gratefully acknowledged for the financial support of the measurements at the CF Cryo-electron Microscopy and Tomography CEITEC MU.This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement no. 665385 article_number: eabc4209 article_processing_charge: No article_type: original author: - first_name: Domen full_name: Kampjut, Domen id: 37233050-F248-11E8-B48F-1D18A9856A87 last_name: Kampjut - first_name: Leonid A full_name: Sazanov, Leonid A id: 338D39FE-F248-11E8-B48F-1D18A9856A87 last_name: Sazanov orcid: 0000-0002-0977-7989 citation: ama: Kampjut D, Sazanov LA. The coupling mechanism of mammalian respiratory complex I. Science. 2020;370(6516). doi:10.1126/science.abc4209 apa: Kampjut, D., & Sazanov, L. A. (2020). The coupling mechanism of mammalian respiratory complex I. Science. American Association for the Advancement of Science. https://doi.org/10.1126/science.abc4209 chicago: Kampjut, Domen, and Leonid A Sazanov. “The Coupling Mechanism of Mammalian Respiratory Complex I.” Science. American Association for the Advancement of Science, 2020. https://doi.org/10.1126/science.abc4209. ieee: D. Kampjut and L. A. Sazanov, “The coupling mechanism of mammalian respiratory complex I,” Science, vol. 370, no. 6516. American Association for the Advancement of Science, 2020. ista: Kampjut D, Sazanov LA. 2020. The coupling mechanism of mammalian respiratory complex I. Science. 370(6516), eabc4209. mla: Kampjut, Domen, and Leonid A. Sazanov. “The Coupling Mechanism of Mammalian Respiratory Complex I.” Science, vol. 370, no. 6516, eabc4209, American Association for the Advancement of Science, 2020, doi:10.1126/science.abc4209. short: D. Kampjut, L.A. Sazanov, Science 370 (2020). date_created: 2020-11-08T23:01:23Z date_published: 2020-10-30T00:00:00Z date_updated: 2023-08-22T12:35:38Z day: '30' ddc: - '572' department: - _id: LeSa doi: 10.1126/science.abc4209 ec_funded: 1 external_id: isi: - '000583031800004' pmid: - '32972993' file: - access_level: open_access checksum: 658ba90979ca9528a2efdfac8547047a content_type: application/pdf creator: lsazanov date_created: 2020-11-26T18:47:58Z date_updated: 2020-11-26T18:47:58Z file_id: '8820' file_name: Full_manuscript_with_SI_opt_red.pdf file_size: 7618987 relation: main_file success: 1 file_date_updated: 2020-11-26T18:47:58Z has_accepted_license: '1' intvolume: ' 370' isi: 1 issue: '6516' language: - iso: eng month: '10' oa: 1 oa_version: Submitted Version pmid: 1 project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: Science publication_identifier: eissn: - '10959203' publication_status: published publisher: American Association for the Advancement of Science quality_controlled: '1' scopus_import: '1' status: public title: The coupling mechanism of mammalian respiratory complex I type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 370 year: '2020' ...