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    • 8. 发明授权
    • Transcriptional regulation of gene expression by small double-stranded modulatory RNA
    • 通过小双链调节RNA的基因表达的转录调控
    • US08092992B2
    • 2012-01-10
    • US10857784
    • 2004-05-28
    • Tomoko KuwabaraFred H. Gage
    • Tomoko KuwabaraFred H. Gage
    • C12Q1/68C12N5/00C12N5/02A61K48/00C07H21/04C07H21/02
    • C12N5/0619C12N2501/60C12N2799/027
    • The invention provides a method for modulating gene expression by contacting a cellular system with a double-stranded ribonucleic acid molecule capable of associating with a regulatory machinery that controls transcription of one or more genes, wherein the association results in altered expression of the one or more genes. The invention is further directed to method for directing the differentiation of neuronal stem cells into neurons by contacting a cellular system with a double-stranded ribonucleic acid molecule capable of associating with a regulatory machinery that controls transcription of one or more genes involved in neuronal differentiation and directing the transcription of the one or more genes. In related embodiments, the invention provides particular compositions of double-stranded ribonucleic acid molecules as well as therapeutic and screening applications of the invention.
    • 本发明提供了一种通过使细胞系统与能够与控制一种或多种基因转录的调节机制缔合的双链核糖核酸分子来调节基因表达的方法,其中所述缔合导致一个或多个 基因。 本发明还涉及通过使细胞系统与能够与控制一种或多种参与神经元分化的基因转录的调节机制相关联的双链核糖核酸分子的方式来指导神经元干细胞分化为神经元的方法 并指导一个或多个基因的转录。 在相关实施方案中,本发明提供了双链核糖核酸分子的特定组合物以及本发明的治疗和筛选应用。
    • 9. 发明申请
    • Temporally dynamic artificial neural networks
    • 时间动态人工神经网络
    • US20100235310A1
    • 2010-09-16
    • US12657748
    • 2010-01-27
    • Fred H. GageJames Bradley AimoneJanet Wiles
    • Fred H. GageJames Bradley AimoneJanet Wiles
    • G06F15/18G06N3/02
    • G06N3/082
    • An apparatus, article and method containing an artificial neural network that, after training, produces new trainable nodes such that input data representative of a first event and input data representative of a second event both activate a subset of the new trainable nodes. The artificial neural network can generate an output that is influenced by the input data of both events. In various embodiments, the new trainable nodes are sequentially produced and show decreasing trainability over time such that, at a particular point in time, newer produced nodes are more trainable than earlier produced nodes. The artificial neural network can be included in various embodiments of methods, apparatus and articles for use in predicting or profiling events.
    • 一种包含人造神经网络的装置,物品和方法,其在训练之后产生新的可训练节点,使得表示第一事件的输入数据和表示第二事件的输入数据都激活新的可训练节点的子集。 人造神经网络可以产生受两个事件的输入数据影响的输出。 在各种实施例中,新的可训练节点被顺序地产生并且随着时间的推移显示降低的可训练性,使得在特定的时间点,较新的生成的节点比先前产生的节点更可训练。 人造神经网络可以包括在用于预测或分析事件的方法,装置和物品的各种实施例中。
    • 10. 发明申请
    • TARGET-ORIENTED WHOLE GENOME AMPLIFICATION OF NUCLEIC ACIDS
    • 目标靶向的核酸的全基因组扩增
    • US20100184152A1
    • 2010-07-22
    • US12445926
    • 2007-10-22
    • Vladislav SandlerFred H. Gage
    • Vladislav SandlerFred H. Gage
    • C12P19/34C12N9/10
    • C12Q1/6846C12Q2531/119C12Q2525/179C12Q2525/161
    • Disclosed herein are methods of amplifying target nucleic acid sequences (e.g., DNA or RNA), particularly from a very small amount of starting material, such as a single cell. These methods involve targeting the amplification of specific sequence(s) by use of sequence-specific primers and random primers for whole genome amplification using multiple displacement amplification. Generally, the provided methods are referred to herein as “target-oriented” whole genome amplification. Starting material for target-oriented whole genome amplification can be any sample containing DNA or RNA, however, the technique is particularly suitable for very small amounts of starting material, such as a few cells, a single cell, or a single nucleus. The methods provide amplified nucleic acid (including the target sequence of interest) that can subsequently be analyzed.
    • 本文公开了扩增靶核酸序列(例如DNA或RNA)的方法,特别是从非常少量的起始材料如单细胞扩增的方法。 这些方法包括通过使用序列特异性引物和使用多重置换扩增的全基因组扩增的随机引物靶向特异性序列的扩增。 通常,所提供的方法在本文中称为“靶向”全基因组扩增。 用于靶向全基因组扩增的起始材料可以是含有DNA或RNA的任何样品,然而,该技术特别适用于非常少量的起始材料,例如少量细胞,单细胞或单核。 该方法提供随后可以分析的扩增核酸(包括目标靶序列)。