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https://www.selleckchem.com/products/3-typ.html ine.Brain extraction is a fundamental prerequisite step in neuroimage analysis for fetus. Due to surrounding maternal tissues and unpredictable movement, brain extraction from fetal Magnetic Resonance (MR) images is a challenging task. In this paper, we propose a novel deep learning-based multi-step framework for brain extraction from 3D fetal MR images. In the first step, a global localization network is applied to estimate probability maps for brain candidates. Connected-component labeling algorithm is applied to eliminate small erroneous components and accurately locate the candidate brain area. In the second step, a local refinement network is implemented in the brain candidate area to obtain fine-grained probability maps. Final extraction results are derived by a fusion network with the two cascaded probability maps obtained from previous two steps. Experimental results demonstrate that our proposed method has superior performance compared with existing deep learning-based methods.As an important sector of the chemical industry, biocatalysis requires the continuous development of enzymes with tailor-made activity, selectivity, stability, or tolerance to unnatural environments. This is now routinely achieved by directed evolution based on iterative cycles of genetic diversification and activity screening. Here, we highlight its recent developments. First, the design of "smarter" libraries by focused mutagenesis may be a crucial start-up for a fast and successful outcome. Then library assembly and expression are also key steps that benefits from modern molecular biology progresses. Finally, various strategies may be considered for library screening depending on the final objective while low-throughput direct assays have been very successful in generating enzymes for important biocatalytic processes, even in bringing completely new chemistries to the enzyme world, ultrahigh-throughput screening methods are emerging a

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