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    • 4. 发明授权
    • Dynamic motion contrast and transverse flow estimation using optical coherence tomography
    • 使用光学相干断层扫描的动态运动对比度和横向流量估计
    • US07995814B2
    • 2011-08-09
    • US11767187
    • 2007-06-22
    • Jeffrey P. FinglerScott E. FraserDaniel Schwartz
    • Jeffrey P. FinglerScott E. FraserDaniel Schwartz
    • G06K9/00A61B6/00
    • A61B5/0261A61B3/102A61B5/0066G01N21/4795G01N2021/1787
    • The methods described herein are methods to ascertain motion contrast within optical coherence tomography data based upon phase variance. The phase variance contrast observes the nanometer scale motion of scatterers associated with Brownian motion and other non-flow motion. The inventive method of calculating motion contrast from the phase variance can differentiate regions of different mobility based on the motion contrast differences, and can use the phase information to characterize mobility properties of the scatterers. In flow regions, the inventive method for acquiring and analyzing motion contrast can identify the regions as well as characterize the motion. Furthermore, the inventive method can determine quantitative flow estimation, the index of refraction variations, and absorption variations within flow regions.
    • 本文描述的方法是基于相位方差来确定光学相干断层摄影数据内的运动对比度的方法。 相位差对比度观察与布朗运动和其他非流动运动相关的散射体的纳米级运动。 从相位方差计算运动对比度的本发明方法可以基于运动对比差异来区分不同移动性的区域,并且可以使用相位信息来表征散射体的移动性质。 在流动区域中,用于获取和分析运动对比度的本发明的方法可以识别区域以及表征运动。 此外,本发明的方法可以确定流量区域中的定量流量估计,折射率变化指数和吸收变化。
    • 9. 发明申请
    • Extraction of motifs from large scale sequence data
    • 从大规模序列数据中提取图案
    • US20060259250A1
    • 2006-11-16
    • US11130310
    • 2005-05-16
    • Daniel Schwartz
    • Daniel Schwartz
    • G06F19/00
    • G16B40/00G16B30/00
    • A method for extraction of statistically significant motifs from large naturally occurring datasets relies upon the intrinsic alignment of the data, extracting motifs through iterative comparison to a dynamic statistical background. In the preferred embodiment, a series of statistical correlations is performed to determine the most significant correlated residues, which in turn are used to identify a motif. The motif is then removed from the dataset and the routine is repeated until no more motifs are found. The motifs are identified in the context of a core residue with respect to a user-selected background, building significant motifs from smaller motifs. In the initial step, the sequence data is justified around a selected core residue. A second matrix that contains a background dataset is then created. The binomial probability of each residue in every column of the data matrix is calculated and the residue-column pair which had the lowest binomial probability below a defined threshold is selected. Those sequences in the background and data matrices that contain that significant residue at the appropriate column are extracted and placed into new matrices. The process is repeated for successive ones of these new matrices until no residue-column pairs with p-values below the threshold are detected. Upon completion, a motif is identified by listing each of the residue-column pairs that have been found to be statistically significant. Next, all sequences from matrices the background and data matrices that contain that motif are removed from those matrices and the process is repeated using the same core residue, completing when no significant residue-column pairs are detected.
    • 从大型天然存在的数据集中提取统计学上显着的基序的方法取决于数据的内在对齐,通过迭代比较提取图案到动态统计背景。 在优选实施例中,执行一系列统计相关性以确定最显着的相关残基,其又用于识别基序。 然后从数据集中删除图案,并重复该程序,直到找不到更多的图案。 相对于用户选择的背景,在核心残基的上下文中鉴定基序,从较小图案构建显着的基序。 在初始步骤中,序列数据围绕选定的核心残基进行校正。 然后创建包含背景数据集的第二个矩阵。 计算数据矩阵每列中每个残差的二项概率,并选择具有低于定义阈值的二项概率最低的残差列对。 在背景中的那些序列和包含在适当列处的显着残基的数据矩阵被提取并置于新的基质中。 对这些新矩阵中的连续的这些新矩阵重复该过程,直到没有检测到p值低于阈值的残留列对。 完成后,通过列出已被发现具有统计学意义的每个残基 - 列对来鉴定基序。 接下来,从这些矩阵中除去包含该基序的背景和数据矩阵的矩阵的所有序列,并且使用相同的核心残基重复该过程,当没有检测到显着的残基 - 列对时完成。