22 research outputs found

    Computational methods for 3D imaging of neural activity in light-field microscopy

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    Light Field Microscopy (LFM) is a 3D imaging technique that captures spatial and angular information from light in a single snapshot. LFM is an appealing technique for applications in biological imaging due to its relatively simple implementation and fast 3D imaging speed. For instance, LFM can help to understand how neurons process information, as shown for functional neuronal calcium imaging. However, traditional volume reconstruction approaches for LFM suffer from low lateral resolution, high computational cost, and reconstruction artifacts near the native object plane. Therefore, in this thesis, we propose computational methods to improve the reconstruction performance of 3D imaging for LFM with applications to imaging neural activity. First, we study the image formation process and propose methods for discretization and simplification of the LF system. Typical approaches for discretization are performed by computing the discrete impulse response at different input locations defined by a sampling grid. Unlike conventional methods, we propose an approach that uses shift-invariant subspaces to generalize the discretization framework used in LFM. Our approach allows the selection of diverse sampling kernels and sampling intervals. Furthermore, the typical discretization method is a particular case of our formulation. Moreover, we propose a description of the system based on filter banks that fit the physics of the system. The periodic-shift invariant property per depth guarantees that the system can be accurately described by using filter banks. This description leads to a novel method to reduce the computational time using singular value decomposition (SVD). Our simplification method capitalizes on the inherent low-rank behaviour of the system. Furthermore, we propose rearranging our filter-bank model into a linear convolution neural network (CNN) that allows more convenient implementation using existing deep-learning software. Then, we study the problem of 3D reconstruction from single light-field images. We propose the shift-invariant-subspace assumption as a prior for volume reconstruction under ideal conditions. We experimentally show that artifact-free reconstruction (aliasing-free) is achievable under these settings. Furthermore, the tools developed to study the forward model are exploited to design a reconstruction algorithm based on ADMM that allows artifact-free 3D reconstruction for real data. Contrary to traditional approaches, our method includes additional priors for reconstruction without dramatically increasing the computational complexity. We extensively evaluate our approach on synthetic and real data and show that our approach performs better than conventional model-based strategies in computational time, image quality, and artifact reduction. Finally, we exploit deep-learning techniques for reconstruction. Specifically, we propose to use two-photon imaging to enhance the performance of LFM when imaging neurons in brain tissues. The architecture of our network is derived from a sparsity-based algorithm for reconstruction named Iterative Shrinkage and Thresholding Algorithm (ISTA). Furthermore, we propose a semi-supervised training based on Generative Adversarial Neural Networks (GANs) that exploits the knowledge of the forward model to achieve remarkable reconstruction quality. We propose efficient architectures to compute the forward model using linear CNNs. This description allows fast computation of the forward model and complements our reconstruction approach. Our method is tested under adverse conditions: lack of training data, background noise, and non-transparent samples. We experimentally show that our method performs better than model-based reconstruction strategies and typical neural networks for imaging neuronal activity in mammalian brain tissue. Our approach enjoys both the robustness of the model-based methods and the reconstruction speed of deep learning.Open Acces

    Skylab Operations Handbook Command/Service Modules CSM 116 Thru 118

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    The SKYLAB Operations Handbook (SOH) is a contractual document. The SOH (Volume 1) is system-oriented and not specifically designed for utilization by any special group. Volume 1 is the description portion of the SOH. It provides the description of all Command-Service Module (CSM) systems

    The roles of MS2 RNA in MS2 capsid assembly

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    Single strand (ss) RNA viruses are amongst the most prevalent viral pathogens in nature. A key event in the life cycle of many of these viruses is the packaging of their ssRNA genome into a capsid of defined size and shape. The mechanism by which genome packaging and capsid assembly proceeds is however poorly understood. Increased knowledge of this event is beneficial for novel anti-viral drug design, as well as contributing to our understanding of macromolecular assembly events. This project has explored the role(s) of the RNA genome in the capsid assembly process of the model ssRNA virus, bacteriophage MS2. In vitro capsid reassembly reactions have been carried out using recombinant coat protein and ssRNA transcripts corresponding to different regions of the MS2 genome. These reactions have been assayed by size distribution analysis using native gel shift assays and sedimentation velocity analysis. This has allowed the effects of RNA size, sequence and structure on capsid assembly to be investigated. All the genomic RNAs transcripts, independent of sequence and size, promoted capsid assembly. The efficiency in which they each promote assembly was, however, different. This was shown to be due to the mechanism by which genomic RNA is packaged. It appears that coat proteins bind to RNA causing conformational changes that reduce its volume to that of the capsid interior. This was evident from the observed RNA length dependence on capsid assembly efficiency. Estimates of the hydrodynamic radii of assembly components and the inhibitory effect that ethidium bromide, a compound which stiffens RNA structure, has on capsid formation also supported this hypothesis. The RNA structural transition was investigated using an RNA structure probing assay. The solution structures of the RNA transcripts were compared to the MS2 genome structure within the virion. Lead acetate was used to cause structure-specific cleavages within these RNAs which were then detected by reverse transcription using labelled primers. The results show that the RNA structure is partly conserved in solution and within the virion, implying that the conformational changes during encapsidation involve primarily tertiary structure rearrangement. The data suggest that the MS2 virion RNA has a defined structure within the virion. These results are consistent with cryo-electron microscopy of virions and capsids carried out by other members of the laboratory. One implication of this work is that compounds capable of inhibiting the conformational rearrangements required for virus assembly could serve as potent anti-viral therapeutics. The work presented in this thesis has contributed to our understanding of how ssRNA is packaged into ssRNA virus capsids and, in particular, the roles it plays in capsid assembly

    OAST Space Systems Studies Review Meeting

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    The agenda from the OAST review meeting is presented. Some of the following topics were reviewed in detail: (1) space utilization; (2) space transportation and (3) science and exploration

    Bibliography of Lewis Research Center technical publications announced in 1977

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    This compilation of abstracts describes and indexes over 780 technical reports resulting from the scientific and engineering work performed and managed by the Lewis Research Center in 1977. All the publications were announced in the 1977 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Documents cited include research reports, journal articles, conference presentations, patents and patent applications, and theses
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