公开(公告)号：US20230056540A1

公开(公告)日：2023-02-23

申请号：US17910468

申请日：2020-03-16

Applicant: Shimadzu Corporation

Inventor： Tetsuya KOBAYASHI ,  Yoshihiro YAMADA

IPC: G06T11/00 ,  G01T1/161

Abstract: A list mode image reconstruction method includes a step of dividing list mode data into a plurality of subsets and a step of acquiring a subset balance coefficient based on the number of events in the plurality of subsets.

公开(公告)号：US20220382834A1

公开(公告)日：2022-12-01

申请号：US17729906

申请日：2022-04-26

Applicant: SHIMADZU CORPORATION

Inventor： Yuichiro FUJITA ,  Akira NODA ,  Yusuke TAMAI ,  Yoshihiro YAMADA

IPC: G06F17/18 ,  G01N21/27

Abstract: An analysis method for analyzing a sample includes a first step of acquiring measurement data including a first signal based on the sample and a second signal based on noise added to the first signal as a result of analysis of the sample, a second step of assuming a shape representing the first signal and a shape representing the second signal and modeling the measurement data using Bayesian inference, and a third step of estimating a probability distribution of characteristics of the sample based on the modeled measurement data.

公开(公告)号：US20150066387A1

公开(公告)日：2015-03-05

申请号：US14471907

申请日：2014-08-28

Applicant: SHIMADZU CORPORATION

Inventor： Yoshihiro YAMADA ,  Shigeki KAJIHARA

IPC: H01J49/00

CPC classification number: H01J49/0027 ,  H01J49/004

Abstract: MS1 and MS2 measurements of fractionated samples are performed. Based on the identification results and the S/N ratios of the MS1 peaks, an identification probability estimation model showing a relationship between the cumulative number of MS1 peaks and the number of MS1 peaks successfully identified through the MS2 measurements and identifications performed in ascending order of S/N ratio is created. S/N ratios of the MS1 peaks obtained by MS1 measurements are determined, and probabilities of substances in a target sample are estimated from S/N ratios using the aforementioned model. Optimization of precursor-ion selection and data-accumulation number is defined as the problem of maximizing the sum of identification probabilities of MS1 peaks selected for MS2 measurement, and formulated as an objective function using 0-1 variables. This function is solved as a 0-1 integer programming problem under preset conditions. Optimal precursor ions and data-accumulation numbers are determined from variables of the solution.

Abstract translation: 进行分离样品的MS1和MS2测量。 基于MS1峰值的识别结果和S / N比，识别概率估计模型显示了通过MS2测量成功识别的MS1峰值的累积数与MS1峰值之间的关系，并按照升序 创建S / N比。 确定通过MS1测量获得的MS1峰的S / N比，并且使用上述模型从S / N比估计目标样品中物质的概率。 前导离子选择和数据累积数的优化定义为最大化MS2测量选择的MS1峰的识别概率之和，并使用0-1变量表示为目标函数的问题。 该功能在预设条件下被解决为0-1整数编程问题。 最佳前体离子和数据累积数由溶液的变量确定。

公开(公告)号：US20130253848A1

公开(公告)日：2013-09-26

申请号：US13848324

申请日：2013-03-21

Applicant: SHIMADZU CORPORATION

Inventor： Yoshihiro YAMADA

IPC: H01J49/00 ,  G06F19/00

CPC classification number: H01J49/0036 ,  G06F19/70 ,  G06F19/703 ,  H01J49/004

Abstract: An identification probability estimation model, which shows the relationship between the S/N ratios of MS1 peaks and the cumulative number of the peaks in the case where MS2 measurements and identifications is performed in descending order of S/N ratio, is created beforehand from the S/N ratios of MS1 peaks as well as the results of MS1 or MS2 measurements and identifications (success or failure) performed for a number of fractionated samples obtained from a predetermined sample. Based on an evaluated value of the identification probability and that of the identification probability increment, an order of priority of MS2 measurements for a plurality of MS1 peaks is determined, and an MS2 measurement sequence which gives the maximal expectation value of the number of substances to be identified under a limitation on the number of MS2 measurements or other factors is determined.

Abstract translation: 显示了在S / N比的降序执行MS2测量和标识的情况下，MS1峰的S / N比与峰的累计数之间的关系的识别概率估计模型是从 MS1峰的S / N比以及对从预定样品获得的多个分级样品进行的MS1或MS2测量和鉴定（成功或失败）的结果。 基于识别概率的评估值和识别概率增量的评估值，确定多个MS1峰的MS2测量的优先级顺序，以及给出物质数量的最大期望值的MS2测量序列 在确定MS2测量数量或其他因素的限制下确定。

公开(公告)号：US20130116934A1

公开(公告)日：2013-05-09

申请号：US13670396

申请日：2012-11-06

Applicant: SHIMADZU CORPORATION

Inventor： Yoshihiro YAMADA

IPC: G06F19/00 ,  H01J49/26

CPC classification number: H01J49/0036 ,  H01J49/004

Abstract: Provided is a method for quantitatively estimating the probability of substance identification based on the result of an MS2 analysis using a certain MS1 peak as the precursor ion, before performing the MS2 analysis. Based on the result of MS1 and MS2 analyses and substance identification performed for each of a number of fractionated samples obtained from a known preparatory sample, an identification probability estimation model creator grasps m/z and S/N ratios of MS1 peaks having high probabilities of successful identification, calculates a parameter which determines the order of MS1 peaks and a parameter representing an identification probability estimation model, and stores the parameters in a memory. When identifying a substance, an approximate order is calculated for an MS1 peak obtained by the analysis. The identification probability for that peak is estimated from the approximate order with reference to the identification probability estimation model.

Abstract translation: 提供了在进行MS2分析之前，基于使用某个MS1峰作为前体离子的MS2分析的结果，定量估计物质识别的概率的方法。 基于从已知准备样本获得的多个分数样本中的每一个进行的MS1和MS2分析和物质识别的结果，识别概率估计模型创建者掌握具有高可能性的MS1峰的m / z和S / N比 成功识别，计算确定MS1峰值顺序的参数和表示识别概率估计模型的参数，并将参数存储在存储器中。 当识别物质时，计算通过分析得到的MS1峰的近似次序。 参考识别概率估计模型，从大致顺序估计该峰值的识别概率。

公开(公告)号：US20230282310A1

公开(公告)日：2023-09-07

申请号：US17685453

申请日：2022-03-03

Applicant: SHIMADZU CORPORATION

Inventor： Yoshihiro YAMADA ,  Kanae TERAMOTO

IPC: G16B40/00 ,  H01J49/00

CPC classification number: G16B40/00 ,  H01J49/0036

Abstract: To enable a correct and easy discrimination of microorganisms, a microorganism discrimination method includes: acquiring mass spectra related to known microorganisms which belong to the same species and whose subspecies, strains or types are known (S11); retrieving a list describing m/z values of marker-candidate proteins which are supposed to vary in mass among different subspecies, strains or types (S12); creating a mask which gives non-zero values only within a predetermined range including each of the listed m/z values (S14); masking each of the mass spectra (S15); creating wavelet images by performing continuous wavelet transform on the mass spectra (S16); creating a discriminant model by machine learning using, as training data, the wavelet images and information of the subspecies, strains or types of the known microorganisms; and discriminating the subspecies, strain or type of an unknown microorganism by applying a mass spectrum of this microorganism to the discriminant model.