Cell-free protein synthesis is a versatile protein production system. Performance of the protein synthesis depends on highly active cytoplasmic extracts. Extracts from E. coli are believed to work best; they are routinely obtained from exponential growing cells, aiming to capture the most active translation system. Here, we report an active cell-free protein synthesis system derived from cells harvested at non-growth, stressed conditions. We found a downshift of ribosomes and proteins. However, a characterization revealed that the stoichiometry of ribosomes and key translation factors was conserved, pointing to a fully intact translation system. This was emphasized by synthesis rates, which were comparable to those of systems obtained from fast-growing cells. Our approach is less laborious than traditional extract preparation methods and multiplies the yield of extract per cultivation. This simplified growth protocol has the potential to attract new entrants to cell-free protein synthesis and to broaden the pool of applications. In this respect, a translation system originating from heat stressed, non-growing E. coli enabled an extension of endogenous transcription units. This was demonstrated by the sigma factor depending activation of parallel transcription. Our cell-free expression platform adds to the existing versatility of cell-free translation systems and presents a tool for cell-free biology.
In this work we present an efficient GPU implementation of the Fast Directional Chamfer Matching (FDCM) algorithm [10]. We propose some extensions to the original FDCM algorithm. In particular, we extend the algorithm to handle templates with variable size, to account for perspective effects. To the best of our knowledge, our work is the first to present a full implementation of a shape based matching algorithm on a GPU. Further contributions of our work consist of implementing a highly optimized CPU version of the algorithm (via multi-threading and SSE2), as well as a thorough comparison between pure GPU, pure CPU, and a hybrid version. The hybrid CPU-GPU version which turns out to be the fastest, achieves run-time of 44 fps on PAL resolution images.
In this paper we present a background subtraction algorithm for a practical surveillance system, on a GPU. It utilizes a compressed non-parametric representation of the history of each pixel, using YCbCr color space, not requiring an offline training period. Although it can be parametrized to cope successfully with moving background, we rather focus on fulfilling some requirements of a practical surveillance system monitoring pedestrians and traffic. First, the time it takes for a stopped foreground object to be absorbed into the background (integration time) should be large enough. Furthermore, the integration time should be controllable by the user and should remain constant, regardless of the complexity of the scene. A further requirement is that objects which repeatedly re-appear in the image, e.g. vehicles having similar colors crossing repeatedly the same region in the image, need not be incorporated into the background. In addition, foreground aperture is undesired, even in case of slowly moving large objects. We implement our method on a NVidia GeForce 9800 GT GPU, achieving 635 fps for the background algorithm, or 436 fps when memory transfer to and from the GPU is included, on a video with 352x288 resolution. We demonstrate the capability of the algorithm by comparing it to MoG, both on moving background and on practical surveillance scenarios. Our method outperforms MoG in both modes, in terms of adaptation speed, run-time and the quality of the foreground segmentation. Furthermore, the integration time is more stable.
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