Voyage astral | EPFL Graph Search

Le voyage astral est une expression de l'ésotérisme qui désigne l'esprit qui se dissocie du corps physique pour vivre une existence autonome et explorer librement l'espace environnant. Il existe plusieurs synonymes de cette expression incluant , , , (EHC),, , (SHC), , , et . L'expression d'expérience de hors-corps est plus récente et relève davantage de la médecine et la psychologie. L'expression est liée à la croyance des occultistes en un corps astral et en un plan astral. L'expérience se produirait en diverses occasions : à l'approche de la mort, au cours d'une opération sous anesthésie, sous le coup d'une douleur intense, au cours d'une méditation, lorsque le corps est dans un état de relaxation avancé, lors du sommeil profond, sous l'emprise de drogues hallucinogènes, en période de stress, lors de paralysie du sommeil ou même sans aucune raison directe et à tout moment. Il n'existe pas de preuve acceptée par la communauté scientifique quant à la possibilité d'un « voyage as

Reconstruction Methods for Cryo-Electron Microscopy: From Model-based to Data-driven

Laurène Donati

The topic of this thesis is the development of new reconstruction methods for cryo-electron microscopy (cryo-EM). Cryo-EM has revolutionized the field of structural biology over the last decade and now permits the regular discovery of biostructures. Yet, the technical challenges associated to cryo-EM are still numerous, and the measurements remain notoriously difficult to process. This calls for fast and robust algorithms that can reliably handle the challenging reconstruction task at hand. In this thesis, we investigated two reconstruction paradigms: model-based and data-driven. Model-based methods formulate the reconstruction task as an inverse problem and rely on a faithful model of the acquisition physics. By contrast, the central philosophy of data-driven approaches is to let the reconstruction algorithm be guided by the measured data through some learning procedure. Both paradigms share a tight link in all our works: their reliance on a rigorous mathematical formulation of the cryo-EM imaging model. The first cryo-EM method we considered is scanning transmission electron tomography (STET), a modality whose primary concern is to reduce the electron dosage required for accurate imaging. To handle this, we developed a tailored acquisition-reconstruction STET framework that relies on the principles of compressed sensing. This scheme permits high-quality reconstruction from a reduced number of measurements, hence greatly preserving the sample. We then designed several reconstruction algorithms for single-particle analysis (SPA), a popular cryo-EM method that enables the determination of structures at near-atomic resolution. A key challenge for the deployment of robust, iterative reconstruction methods in SPA is that they usually come with a prohibitive computational cost if not carefully engineered. To circumvent this problem, we developed a regularized reconstruction scheme whose cost-dominant operation is recast as a discrete convolution, which makes the use of our robust scheme feasible in SPA. Building on this development, we devised a joint optimization framework that efficiently alternates between the reconstruction and the estimation of the unknown orientations. We then explored a learning-based method to estimate the unknown orientations in SPA directly from the acquired dataset of projections. Capitalizing on our ability to model the cryo-EM procedure, we generated large synthetic SPA datasets to train a function---parametrized as a neural network---to predict the relative orientation between two projections based on their similarity. The framework relies on the postulate that it is possible to recover, from these estimated orientation distances, the orientations themselves through an appropriate minimization scheme, as supported by preliminary tests. Finally, we developed a completely new paradigm for SPA reconstruction that leverages the remarkable capability of deep neural networks to capture data distribution. The proposed algorithm uses a generative adversarial network to learn the 3D structure that has simulated projections that most closely match the real data in a distributional sense. By doing so, it can resolve a 3D structure in a single algorithmic run using only the dataset of projections and CTF estimations as inputs. Hence, it bypasses many processing steps that are necessary in the usual cryo-EM reconstruction pipeline, which opens new perspectives for reconstruction in SPA.

EPFL2020

CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM Via Deep Adversarial Learning

Laurène Donati, Harshit Gupta, Michael Thompson McCann, Michaël Unser

We present CryoGAN, a new paradigm for single-particle cryo-electron microscopy (cryo-EM) reconstruction based on unsupervised deep adversarial learning. In single-particle cryo-EM, the structure of a biomolecule needs to be reconstructed from a large set of noisy tomographic projections with unknown orientations. Current reconstruction techniques are based on a marginalized maximum-likelihood formulation that requires calculations over the set of all possible poses for each projection image, a computationally demanding procedure. Our approach is to seek a 3D structure that has simulated projections that match the real data in a distributional sense, thereby sidestepping pose estimation or marginalization. We prove that, in an idealized mathematical model of cryo-EM, this approach results in recovery of the correct structure. Motivated by distribution matching, we propose CryoGAN, a specialized GAN that consists of a 3D structure, a cryo-EM physics simulator, and a discriminator neural network. During reconstruction, the 3D structure is optimized so that its projections obtained through the simulator resemble real data (to the discriminator). Simultaneously, the discriminator is trained to distinguish real projections from simulated projections. CryoGAN takes as input only real projection images and the distribution of the cryo-EM imaging parameters. It involves neither prior training nor an initial estimation of the 3D structure. CryoGAN currently achieves a 10.8 angstrom resolution on a realistic synthetic dataset. Preliminary results on experimental beta-galactosidase and 80S ribosome data demonstrate the ability of CryoGAN to exploit data statistics under standard experimental imaging conditions. We believe that this paradigm opens the door to a family of novel likelihood-free algorithms for cryo-EM reconstruction.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Reconstruction Methods for Cryo-Electron Microscopy: From Model-based to Data-driven

Laurène Donati

EPFL2020

CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM Via Deep Adversarial Learning

Laurène Donati, Harshit Gupta, Michael Thompson McCann, Michaël Unser

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021