Syngene, a world-leading manufacturer of image analysis solutions, is pleased to announce its Dyversity 2D gel imaging system is being used by scientists at one of Latin America’s most prestigious Ophthalmological Institutions, the Institute of Ophthalmology “Fundación Conde de Valenciana” in Mexico, to study which proteins are associated with ocular diseases.
Researchers in the Microbiology and Ocular Proteomics Area at the Institute of Ophthalmology “Fundación Conde de Valenciana” are using the Dyversity, Syngene’s 2D gel imaging system to accurately visualise proteins stained with either silver stain or Coomassie blue on 2D and 1D gels. The system is also being used to analyse chemiluminescent protein arrays and Western blots. The information from the gels and blots is helping detect which proteins are responsible for a range of ocular diseases, and it is hoped that determining the molecular basis of these conditions, may help find new therapies to treat them.
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UVP, LLC announces the release of the new iBox® Explorer™ Fluorescence Microscope at ASM. The Explorer system combines the technology from its macro imager, the iBox Scientia™ Imaging System, with new micro imaging technology that incorporates imaging tissues, tissue margins and individual cells. The Explorer provides breakthrough advances in its dual lighting system and software controlled objectives. The Explorer system supplies the benefit of using one complete system for macro and micro in vivo animal fluorescent imaging.
"The iBox Explorer is significant for speed and versatility," according to Sean Gallagher (VP and CTO UVP). "Enabling the rapid and multiplexed fluorescence detection of tumor margins and micro metastasis, the Explorer cleanly separates normal from cancer tissues via the cell's fluorescent signature. Operating in the visible and NIR wavelengths, the Explorer yields detailed images of tissues and cells or, using the joy stick, 'flies' across an area such as the open abdominal region or skin flap of a mouse for rapid screening." In addition to imaging both the whole organ and cells of small animals, the Explorer delivers optical configurations that are parcentered and parfocal, allowing seamless imaging through the magnification ranges.
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The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.
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