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    • 1. 发明专利
    • Improvements in check valves
    • GB239489A
    • 1926-03-18
    • GB1487725
    • 1925-06-08
    • EDWARD THAYER ADAMS
    • F16K15/03
    • 239,489. Adams, E. T. Sept. 2, 1924, [Convention date]. Check hinged valves. - A hinged non-return valve has the arm supporting the valve member pivoted to a removable ring disposed laterally of the line or flow and held against a shoulder in the casing by a detachable cover. The cupped valve disc 11 co-operates with a seat 10 and is rotatably secured to the arm 16 by a nut 13 engaging a screwed projection 18 on the. back of the valve. The projection is provided with a slot 17 to facilitate grinding and the face of the valve has a square projection 12 for holding the valve against rotation when applying the nut 13. The supporting ring 5 is held against the shoulder 6 by a faced part 7 on the cap 8 and has a series of radial arms 24. One of these arms has a depending rectangular wall 23 the sides of which are formed with ears adapted to receive a pin 20 which also passes through the end of the arm 16. The pin is held against displacement by projections on the casing engaging the sides of the ears. The casing has hexagonal ends 2, 3 having a flat surface at the bottom of the valve.
    • 2. 发明授权
    • Methods for identifying discrete populations (e.g., clusters) of data within a flow cytometer multi-dimensional data set
    • 用于识别流式细胞仪多维数据集中的数据的离散群体(例如,簇)的方法
    • US07299135B2
    • 2007-11-20
    • US11271316
    • 2005-11-10
    • Edward Thayer
    • Edward Thayer
    • G01N33/48G01N33/50
    • G01N15/1459G01N2015/008G01N2015/1006G01N2015/1402G01N2015/1477G06F19/00G06K9/00147G06K9/6226G06K9/626G16H50/20G16H50/70
    • Systems and methods for identifying populations of events in a multi-dimensional data set are described. The populations may, for example, be sets or clusters of data representing different white blood cell components in sample processed by a flow cytometer. The methods use a library consisting of one or more one finite mixture models, each model representing multi-dimensional Gaussian probability distributions with a density function for each population of events expected in the data set. The methods further use an expert knowledge set including one or more data transformations and one or more logical statements. The transformations and logical statements encode a priori expectations as to the populations of events in the data set. The methods further use program code by which a computer may operate on the data, a finite mixture model selected from the library, and the expert knowledge set to thereby identify populations of events in the data set.
    • 描述用于识别多维数据集中的事件群体的系统和方法。 例如,种群可以是由流式细胞仪处理的样品中代表不同白细胞组分的数据集或簇。 该方法使用由一个或多个有限混合模型组成的库,每个模型表示数据集中预期的每个事件群的密度函数的多维高斯概率分布。 所述方法还使用包括一个或多个数据变换和一个或多个逻辑语句的专家知识集。 变换和逻辑语句编码了对数据集中的事件群体的先验期望。 这些方法还使用计算机可以对数据进行操作的程序代码,从库中选择的有限混合模型和专家知识集合,从而识别数据集中的事件群体。
    • 4. 发明申请
    • Method for estimating error from a small number of expression samples
    • 用于从少量表达样本估计误差的方法
    • US20080108510A1
    • 2008-05-08
    • US11592006
    • 2006-11-02
    • Edward Thayer
    • Edward Thayer
    • C40B30/02
    • G16B25/00
    • A method for estimating error in expression data. In one embodiment, the method includes single molecule sequencing a plurality of expression tags from an organism; removing expression tags that ambiguously relate to multiple genes; assigning each remaining expression tag to a respective gene; selecting a random subset of the expression tags; and counting the number of expression tags associated with each gene. The process of selecting a random subset of the expression tags; and counting the number of expression tags associated with each gene is repeated a predetermined number of times, both for expression tags sequenced before and after exposure of the organism to a perturbation. The method also includes the step of calculating a measure of error in response to the counts of the number of expression tags before and after the perturbation.
    • 一种估计表达数据误差的方法。 在一个实施方案中,该方法包括从生物体单个分子测序多个表达标签; 去除与多个基因无关的表达标签; 将每个剩余的表达标签分配给相应的基因; 选择表达标签的随机子集; 并计算与每个基因相关联的表达标签的数量。 选择表达标签的随机子集的过程; 并且将与每个基因相关联的表达标签的数量计数重复预定次数,两者对于在生物体暴露于扰动之前和之后测序的表达标签。 该方法还包括响应于在扰动之前和之后的表达标签的数量的计数来计算误差量度的步骤。
    • 6. 发明申请
    • Methods for identifying discrete populations (e.g., clusters) of data within a flow cytometer multi-dimensional data set
    • 用于识别流式细胞仪多维数据集中的数据的离散群体(例如,簇)的方法
    • US20070118297A1
    • 2007-05-24
    • US11271316
    • 2005-11-10
    • Edward Thayer
    • Edward Thayer
    • G06F19/00
    • G01N15/1459G01N2015/008G01N2015/1006G01N2015/1402G01N2015/1477G06F19/00G06K9/00147G06K9/6226G06K9/626G16H50/20G16H50/70
    • Systems and methods for identifying populations of events in a multi-dimensional data set, e.g., seven dimensional flow cytometry data of a blood sample. The populations may, for example, be sets or clusters of data representing different white blood cell components in the sample. The methods use a library consisting of one or more one finite mixture models, each model component comprising parameters representing multi-dimensional Gaussian probability density functions, one density for each population of events expected in the data set. The methods further use an expert knowledge set comprising one or more data transformations for operation on the multi-dimensional data set and one or more logical statements. The transformations and logical statements encode a priori expectations as to the relationships between different event populations in the data set. The methods further use program code comprising instructions by which a processing unit such as a computer may operate on the multi-dimensional data, a finite mixture model selected from the library, and the expert knowledge set to thereby identify populations of events in the multi-dimensional data set.
    • 用于识别多维数据集中的事件群体的系统和方法,例如血液样品的七维流式细胞术数据。 群体可以例如是样本中表示不同白细胞成分的数据集或数据集。 该方法使用由一个或多个有限混合模型组成的库,每个模型组件包括表示多维高斯概率密度函数的参数,对于数据集中期望的每个事件群,一个密度。 所述方法还使用包括用于对多维数据集和一个或多个逻辑语句进行操作的一个或多个数据变换的专家知识集合。 转换和逻辑语句编码了数据集中不同事件群体之间的关系的先验期望。 该方法进一步使用程序代码,其包括诸如计算机的处理单元可以对多维数据进行操作的指令,从库中选择的有限混合模型和专家知识集合,从而识别多维数据中的事件群体, 维数据集。