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    • 4. 发明申请
    • METHODS FOR DIRECTING DIFFERENTIATION OF CLONOGENIC NEURAL STEM CELLS WITH COUMARINS
    • 用COUMARIN指导克隆性神经干细胞分化的方法
    • US20090170930A1
    • 2009-07-02
    • US12400756
    • 2009-03-09
    • Cheng HEWeidong ZHANGXiaohui XUWei ZHANGJuan SUChuan ZHANG
    • Cheng HEWeidong ZHANGXiaohui XUWei ZHANGJuan SUChuan ZHANG
    • A61K31/35
    • A61K31/37
    • A method for promoting differentiation of clonogenic neural stem cells (NSCs), comprising administering to a patient in the need of such promoting a coumarin compound represented by formula I or by formula II. The representative coumarin compounds include 7-hydroxycoumarin, daphnoretin, scopoletin, edgeworin, aesculetin and esculetin-6-β-D-glucopyranoside. The coumarin compounds showed significant activity of directing the differentiation of NSCs in pharmacological test and thereof could be used to prepare drugs to direct NSCs differentiated to oligodendrocyte progenitor cells (OPCs) for the treatment of demyelinating diseases or spinal cord injury. The drug could be a pure coumarin compound or a pharmaceutical composition comprising a therapeutical dose of a coumarin compound as active ingredients and a pharmaceutically-acceptable carrier. The content of the active ingredients in the pharmaceutical composition is between 0.1% and 99.5% by weight.
    • 一种促进克隆型神经干细胞(NSCs)分化的方法,包括对需要这样促进由式I或式II表示的香豆素化合物的患者施用。 代表性的香豆素化合物包括7-羟基香豆素,芫花tin tin,scopoletin,edgeworin,七叶苷和七叶苷-6-β-D-吡喃葡萄糖苷。 香豆素化合物在药理学试验中显示出指导NSCs分化的显着活性,并且其可用于制备药物以指导分化为少突胶质细胞祖细胞(OPCs)的NSCs用于治疗脱髓鞘疾病或脊髓损伤。 药物可以是纯香豆素化合物或包含治疗剂量的香豆素化合物作为活性成分和药学上可接受的载体的药物组合物。 药物组合物中活性成分的含量为0.1〜99.5重量%。
    • 7. 发明申请
    • Visualizing Large Data Volumes Utilizing Initial Sampling and Multi-Stage Calculations
    • 使用初始采样和多阶段计算可视化大数据卷
    • US20160179852A1
    • 2016-06-23
    • US14575633
    • 2014-12-18
    • Alexis NaiboXiaohui XuYann Le Biannic
    • Alexis NaiboXiaohui XuYann Le Biannic
    • G06F17/30
    • G06F16/2462G06F16/217G06F16/23G06F16/2458G06F16/2465G06F16/26G06F16/338G06F16/34G06F16/9038
    • Embodiments visualize large data volumes utilizing initial sampling to reduce size of a dataset. This sampling may be random in nature. The sampled dataset may be refined (wrangled) by binning, grouping, cleansing, and/or other techniques to produce a wrangled sample dataset. A user defines useful end visualization(s) by inputting expected dimension/measures. From these visualizations of sampled data, minimal grouping sets are deduced for application to the full dataset. The user publishes/schedules the wrangled operation and grouping sets definition. Based on this, a wrangled dataset and grouping sets are produced in the big data layer. When the user accesses the visualization(s), minimal grouping sets are retrieved in the in-memory engine of the client and processed by an in-memory database engine according to the common processing plan. This produces result sets and a final set of visualizations of the full dataset, in which the user can recognize valuable data trends and/or relationships.
    • 实施例可以利用初始采样可视化大数据量来减少数据集的大小。 此抽样本质上可能是随机的。 采样数据集可以通过分组,分组,清理和/或其他技术进行改进(争吵),以产生被争吵的样本数据集。 用户通过输入预期的尺寸/度量来定义有用的结束可视化。 从采样数据的这些可视化中,推导出最小分组集应用于完整数据集。 用户发布/调度被纠正的操作和分组集定义。 基于此,在大数据层中产生了一个被扭曲的数据集和分组集。 当用户访问可视化时,根据公共处理计划,在客户端的内存中引擎中检索最小的分组集合,并由内存数据库引擎进行处理。 这产生了完整数据集的结果集和可视化的最终集合,用户可以在其中识别有价值的数据趋势和/或关系。