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

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

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

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

Tomographic Inverse Problems: Theory and Applications

This was the tenth Oberwolfach conference on the mathematics of tomography. The field rests on the interplay between the theoretical and applied; practical questions lead to new mathematics and pure mathematics motivates new algorithms. This workshop encompassed classical areas such as X-ray computed tomography (CT) as well as new modalities and applications such as dynamic imaging, Compton scattering tomography, hybrid imaging, optical tomography or multi-energy CT and addressed inter alia the use of methods from machine learning

New Algorithms in Computational Microscopy

Microscopy plays an important role in providing tools to microscopically observe objects and their surrounding areas with much higher resolution ranging from the scale between molecular machineries (angstrom) and individual cells (micrometer). Under microscopes, illumination, such as visible light and electron-magnetic radiation/electron beam, interacts with samples, then they are scattered to a plane and are recorded. Computational microscopy corresponds to image reconstruction from these measurements as well as improving quality of the images. Along with the evolution of microscopy, new studies are discovered and algorithms need development not only to provide high-resolution imaging but also to decipher new and advanced research. In this dissertation, we focus on algorithm development for inverse problems in microscopy, specifically phase retrieval and tomography, and the application of these techniques to machine learning. The four studies in this dissertation demonstrates the use of optimization and calculus of variation in imaging science and other different disciplines.Study 1 focuses on coherent diffractive imaging (CDI) or phase retrieval, a non-linear inverse problem that aims to recover 2D image from it Fourier transforms in modulus taking into account that extra information provided by oversampling as a second constraint. To solve this two-constraint minimization, we proceed from Hamilton-Jacobi partial differential equation (HJ-PDE) and its Hopf-Lax formula. Introducing generalized Bregman distance to the HJ-PDE and applying Legendre transform, we derive our generalized proximal smoothing (GPS) algorithm under the form of primal-dual hybrid gradient (PDHG). While the reflection operator, known as extrapolating momentum, helps overcome local minima, the smoothing by the generalized Bregman distance is adjusted to improve convergence and consistency of phase retrieval.Study 2 focuses on electron tomography, 3D image reconstruction from a set of 2D projections obtained from a transmission electron microscope (TEM) or X-ray microscope. Notice that current tomography algorithms limit to a single tilt axis and fail to work with fully or partially missing data. In the light of calculus of variations and Fourier slice theorem (FST), we develop a highly accurate tomography iterative algorithm that can provide higher resolution imaging and work with missing data as well as has capability to perform multiple-tilt-axis tomography. The algorithm is further developed to work with non-isolated objects and partially-blocked projections which have become more popular in experiment. The success of real space iterative reconstruction engine (RESIRE) opens a new era to the study of tomography in material science and magnetic structures (vector Tomography).Study 3 and 4 are applications of our algorithms to machine learning. Study 3 develops a backward Euler method in a stochastic manner to solve K-mean clustering, a well-known non-convex optimization problem. The algorithm has been shown to improve minimums and consistency, providing a new powerful tool to the class of classification techniques. Study 4 is a direct application of GPS to deep learning gradient descent algorithms. Linearizing the Hopf-Lax formula derived in GPS, we derive our method Laplacian smoothing gradient descent (LSGD), simply known as gradient smoothing. Our experiment shows that LSGD has the ability to search for better and flatter minimums, reduce variation, and obtain higher accuracy and consistency

OAST Space Systems Studies Review Meeting

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

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