10 research outputs found

    Cell arrest and cell death in mammalian preimplantation development

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    The causes, modes, biological role and prospective significance of cell death in preimplantation development in humans and other mammals are still poorly understood. Early bovine embryos represent a very attractive experimental model for the investigation of this fundamental and important issue. To obtain reference data on the temporal and spatial occurrence of cell death in early bovine embryogenesis, three-dimensionally preserved embryos of different ages and stages of development up to hatched blastocysts were examined in toto by confocal laser scanning microscopy. In parallel, transcript abundance profiles for selected apoptosis-related genes were analyzed by real-time reverse transcriptase-polymerase chain reaction. Our study documents that in vitro as well as in vivo, the first four cleavage cycles are prone to a high failure rate including different types of permanent cell cycle arrest and subsequent non-apoptotic blastomere death. In vitro produced and in vivo derived blastocysts showed a significant incidence of cell death in the inner cell mass (ICM), but only in part with morphological features of apoptosis. Importantly, transcripts for CASP3, CASP9, CASP8 and FAS/FASLG were not detectable or found at very low abundances. In vitro and in vivo, errors and failures of the first and the next three cleavage divisions frequently cause immediate embryo death or lead to aberrant subsequent development, and are the main source of developmental heterogeneity. A substantial occurrence of cell death in the ICM even in fast developing blastocysts strongly suggests a regular developmentally controlled elimination of cells, while the nature and mechanisms of ICM cell death are unclear. Morphological findings as well as transcript levels measured for important apoptosis-related genes are in conflict with the view that classical caspase-mediated apoptosis is the major cause of cell death in early bovine development

    Self-organizing maps of typhoon tracks allow for flood forecasts up to two days in advance

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    [[abstract]]Typhoons are among the greatest natural hazards along East Asian coasts. Typhoon-related precipitation can produce flooding that is often only predictable a few hours in advance. Here, we present a machine-learning method comparing projected typhoon tracks with past trajectories, then using the information to predict flood hydrographs for a watershed on Taiwan. The hydrographs provide early warning of possible flooding prior to typhoon landfall, and then real-time updates of expected flooding along the typhoon’s path. The method associates different types of typhoon tracks with landscape topography and runoff data to estimate the water inflow into a reservoir, allowing prediction of flood hydrographs up to two days in advance with continual updates. Modelling involves identifying typhoon track vectors, clustering vectors using a self-organizing map, extracting flow characteristic curves, and predicting flood hydrographs. This machine learning approach can significantly improve existing flood warning systems and provide early warnings to reservoir management.[[notice]]補正完

    Zinc-rich inhibitor of apoptosis proteins (IAPs) as regulatory factors in the epithelium of normal and inflamed airways

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