MR image reconstruction using deep density priors

Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly. In this article, we propose an alternative approach that learns the probability distribution of fully sampled MR images using unsupervised DL, specifically Variational Autoencoders (VAE), and use this as an explicit prior term in reconstruction, completely decoupling the encoding operation from the prior. The resulting reconstruction algorithm enjoys a powerful image prior to compensate for missing k-space data without requiring paired datasets for training nor being prone to associated sensitivities, such as deviations in undersampling patterns used in training and test time or coil settings. We evaluated the proposed method with T1 weighted images from a publicly available dataset, multi-coil complex images acquired from healthy volunteers (N=8) and images with white matter lesions. The proposed algorithm, using the VAE prior, produced visually high quality reconstructions and achieved low RMSE values, outperforming most of the alternative methods on the same dataset. On multi-coil complex data, the algorithm yielded accurate magnitude and phase reconstruction results. In the experiments on images with white matter lesions, the method faithfully reconstructed the lesions. Keywords: Reconstruction, MRI, prior probability, machine learning, deep learning, unsupervised learning, density estimationComment: Published in IEEE TMI. Main text and supplementary material, 19 pages tota

Visual Feature Attribution using Wasserstein GANs

Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localisation to understanding hidden effects in the data. In recent years, approaches based on interpreting a previously trained neural network classifier have become the de facto state-of-the-art and are commonly used on medical as well as natural image datasets. In this paper, we discuss a limitation of these approaches which may lead to only a subset of the category specific features being detected. To address this problem we develop a novel feature attribution technique based on Wasserstein Generative Adversarial Networks (WGAN), which does not suffer from this limitation. We show that our proposed method performs substantially better than the state-of-the-art for visual attribution on a synthetic dataset and on real 3D neuroimaging data from patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). For AD patients the method produces compellingly realistic disease effect maps which are very close to the observed effects.Comment: Accepted to CVPR 201

Transit timing variation analysis of the low-mass brown dwarf KELT-1 b

We investigate whether there is a variation in the orbital period of the short-period brown dwarf-mass KELT-1 b, which is one of the best candidates to observe orbital decay. We obtain 19 high-precision transit light curves of the target using six different telescopes. We add all precise and complete transit light curves from open databases and the literature, as well as the available Transiting Exoplanet Survey Satellite (TESS) observations from sectors 17 and 57, to form a transit timing variation (TTV) diagram spanning more than 10 yr of observations. The analysis of the TTV diagram, however, is inconclusive in terms of a secular or periodic variation, hinting that the system might have synchronized. We update the transit ephemeris and determine an informative lower limit for the reduced tidal quality parameter of its host star of Q ′⋆>(8.5±3.9)×106 assuming that the stellar rotation is not yet synchronized. Using our new photometric observations, published light curves, the TESS data, archival radial velocities, and broadband magnitudes, we also update the measured parameters of the system. Our results are in good agreement with those found in previous analyses

Structural Evidence for Asymmetrical Nucleotide Interactions in Nitrogenase

The roles of ATP hydrolysis in electron-transfer (ET) reactions of the nitrogenase catalytic cycle remain obscure. Here, we present a new structure of a nitrogenase complex crystallized with MgADP and MgAMPPCP, an ATP analogue. In this structure the two nucleotides are bound asymmetrically by the Fe-protein subunits connected to the two different MoFe-protein subunits. This binding mode suggests that ATP hydrolysis and phosphate release may proceed by a stepwise mechanism. Through the associated Fe-protein conformational changes, a stepwise mechanism is anticipated to prolong the lifetime of the Fe-protein-MoFe-protein complex and, in turn, could orchestrate the sequence of intracomplex ET required for substrate reduction

Effective Mass Dirac-Morse Problem with any kappa-value

The Dirac-Morse problem are investigated within the framework of an approximation to the term proportional to $1/r^2$ in the view of the position-dependent mass formalism. The energy eigenvalues and corresponding wave functions are obtained by using the parametric generalization of the Nikiforov-Uvarov method for any $\kappa$-value. It is also studied the approximate energy eigenvalues, and corresponding wave functions in the case of the constant-mass for pseudospin, and spin cases, respectively.Comment: 12 page

Nitrogenase Complexes: Multiple Docking Sites for a Nucleotide Switch Protein

Adenosine triphosphate (ATP) hydrolysis in the nitrogenase complex controls the cycle of association and dissociation between the electron donor adenosine triphosphatase (ATPase) (Fe-protein) and its target catalytic protein (MoFe-protein), driving the reduction of dinitrogen into ammonia. Crystal structures in different nucleotide states have been determined that identify conformational changes in the nitrogenase complex during ATP turnover. These structures reveal distinct and mutually exclusive interaction sites on the MoFe-protein surface that are selectively populated, depending on the Fe-protein nucleotide state. A consequence of these different docking geometries is that the distance between redox cofactors, a critical determinant of the intermolecular electron transfer rate, is coupled to the nucleotide state. More generally, stabilization of distinct docking geometries by different nucleotide states, as seen for nitrogenase, could enable nucleotide hydrolysis to drive the relative motion of protein partners in molecular motors and other systems

Repurposing proteins for new bioinorganic functions

Inspired by the remarkable sophistication and complexity of natural metalloproteins, the field of protein design and engineering has traditionally sought to understand and recapitulate the design principles that underlie the interplay between metals and protein scaffolds. Yet, some recent efforts in the field demonstrate that it is possible to create new metalloproteins with structural, functional and physico-chemical properties that transcend evolutionary boundaries. This essay aims to highlight some of these efforts and draw attention to the ever-expanding scope of bioinorganic chemistry and its new connections to synthetic biology, biotechnology, supramolecular chemistry and materials engineering