Distributed Deblurring of Large Images of Wide Field-Of-View

Image deblurring is an economic way to reduce certain degradations (blur and noise) in acquired images. Thus, it has become essential tool in high resolution imaging in many applications, e.g., astronomy, microscopy or computational photography. In applications such as astronomy and satellite imaging, the size of acquired images can be extremely large (up to gigapixels) covering wide field-of-view suffering from shift-variant blur. Most of the existing image deblurring techniques are designed and implemented to work efficiently on centralized computing system having multiple processors and a shared memory. Thus, the largest image that can be handle is limited by the size of the physical memory available on the system. In this paper, we propose a distributed nonblind image deblurring algorithm in which several connected processing nodes (with reasonable computational resources) process simultaneously different portions of a large image while maintaining certain coherency among them to finally obtain a single crisp image. Unlike the existing centralized techniques, image deblurring in distributed fashion raises several issues. To tackle these issues, we consider certain approximations that trade-offs between the quality of deblurred image and the computational resources required to achieve it. The experimental results show that our algorithm produces the similar quality of images as the existing centralized techniques while allowing distribution, and thus being cost effective for extremely large images.Comment: 16 pages, 10 figures, submitted to IEEE Trans. on Image Processin

A condition-specific codon optimization approach for improved heterologous gene expression in Saccharomyces cerevisiae

All authors are with the Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX 78712, USA -- Hal S. Alper is with the Institute for Cellular and Molecular Biology, The University of Texas at Austin, 2500 Speedway Avenue, Austin, TX 78712, USA -- Amanda M. Lanza Current Address: Bristol-Myers Squibb, Biologics Development, 35 South Street, Hopkinton, MA 01748, USABackground: Heterologous gene expression is an important tool for synthetic biology that enables metabolic engineering and the production of non-natural biologics in a variety of host organisms. The translational efficiency of heterologous genes can often be improved by optimizing synonymous codon usage to better match the host organism. However, traditional approaches for optimization neglect to take into account many factors known to influence synonymous codon distributions. Results: Here we define an alternative approach for codon optimization that utilizes systems level information and codon context for the condition under which heterologous genes are being expressed. Furthermore, we utilize a probabilistic algorithm to generate multiple variants of a given gene. We demonstrate improved translational efficiency using this condition-specific codon optimization approach with two heterologous genes, the fluorescent protein-encoding eGFP and the catechol 1,2-dioxygenase gene CatA, expressed in S. cerevisiae. For the latter case, optimization for stationary phase production resulted in nearly 2.9-fold improvements over commercial gene optimization algorithms. Conclusions: Codon optimization is now often a standard tool for protein expression, and while a variety of tools and approaches have been developed, they do not guarantee improved performance for all hosts of applications. Here, we suggest an alternative method for condition-specific codon optimization and demonstrate its utility in Saccharomyces cerevisiae as a proof of concept. However, this technique should be applicable to any organism for which gene expression data can be generated and is thus of potential interest for a variety of applications in metabolic and cellular engineering.Chemical EngineeringInstitute for Cellular and Molecular [email protected]

On Reduced Input-Output Dynamic Mode Decomposition

The identification of reduced-order models from high-dimensional data is a challenging task, and even more so if the identified system should not only be suitable for a certain data set, but generally approximate the input-output behavior of the data source. In this work, we consider the input-output dynamic mode decomposition method for system identification. We compare excitation approaches for the data-driven identification process and describe an optimization-based stabilization strategy for the identified systems