公开(公告)号：US10408757B2

公开(公告)日：2019-09-10

申请号：US15038629

申请日：2015-01-02

Applicant: Arizona Board of Regents on Behalf of Arizona State University

Inventor： Nongjian Tao ,  Hui Yu ,  Xiaonan Shan ,  Shaopeng Wang

IPC: G01N21/552 ,  C12M1/34

Abstract: Method and system to remove background noise with a differential approach in optical imaging is disclosed. The differential approach moves the sample position laterally over a small distance, and a differential image is generated from the images recorded before and after the lateral translation. This approach can significantly improve the image quality of objects, including single DNA molecules, for label-free optical imaging techniques, such as surface plasmon resonance imaging. Disclosed imaging technique provides high-resolution genome-wide restriction maps of single DNA molecules.

公开(公告)号：US20240052395A1

公开(公告)日：2024-02-15

申请号：US18494225

申请日：2023-10-25

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Nongjian Tao ,  Shaopeng Wang ,  Hui Yu

IPC: C12Q1/18 ,  G16H10/40 ,  G16H50/20 ,  G16H30/40 ,  G01N21/51 ,  G06T7/00

CPC classification number: C12Q1/18 ,  G16H10/40 ,  G16H50/20 ,  G16H30/40 ,  G01N21/51 ,  G06T7/0012 ,  G01N2201/1296 ,  G06T2207/10016 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024

Abstract: A method for deep learning video microscopy-based antimicrobial susceptibility testing of a bacterial strain in a sample by acquiring image sequences of individual bacterial cells of the bacterial strain in a subject sample before, during, and after exposure to each antibiotic at different concentrations. The image sequences are compressed into static images while preserving essential phenotypic features. Data representing the static images are input into a pre-trained deep learning (DL) model which generates output data; and antimicrobial susceptibility for the bacterial strain is determined from the output data.

公开(公告)号：US20160299069A1

公开(公告)日：2016-10-13

申请号：US15038629

申请日：2015-01-02

Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

Inventor： Nongjian Tao ,  Hui Yu ,  Xiaonan Shan ,  Shaopeng Wang

IPC: G01N21/552

CPC classification number: G01N21/553 ,  C12M41/46 ,  G01N21/552

Abstract: Method and system to remove background noise with a differential approach in optical imaging is disclosed. The differential approach moves the sample position laterally over a small distance, and a differential image is generated from the images recorded before and after the lateral translation. This approach can significantly improve the image quality of objects, including single DNA molecules, for label-free optical imaging techniques, such as surface plasmon resonance imaging. Disclosed imaging technique provides high-resolution genome-wide restriction maps of single DNA molecules.

Abstract translation: 公开了在光学成像中用差分方法去除背景噪声的方法和系统。 差分方法将样本位置横向移动一小段距离，并且从侧向平移之前和之后记录的图像产生差分图像。 这种方法可以显着提高物体（包括单个DNA分子）的图像质量，用于无标记光学成像技术，如表面等离子体共振成像。 公开的成像技术提供了单分子DNA分子的高分辨率全基因组限制性图谱。

公开(公告)号：US11834696B2

公开(公告)日：2023-12-05

申请号：US16500370

申请日：2018-04-05

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Nongjian Tao ,  Shaopeng Wang ,  Hui Yu

IPC: C12Q1/18 ,  G16H10/40 ,  G16H50/20 ,  G16H30/40 ,  G01N21/51 ,  G06T7/00

CPC classification number: C12Q1/18 ,  G01N21/51 ,  G06T7/0012 ,  G16H10/40 ,  G16H30/40 ,  G16H50/20 ,  G01N2201/1296 ,  G06T2207/10016 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024

Abstract: A method for deep learning video microscopy-based antimicrobial susceptibility testing of a bacterial strain in a sample by acquiring image sequences of individual bacterial cells of the bacterial strain in a subject sample before, during, and after exposure to each antibiotic at different concentrations. The image sequences are compressed into static images while preserving essential phenotypic features. Data representing the static images is input into a pre-trained deep learning (DL) model which generates output data; and antimicrobial susceptibility for the bacterial strain is determined from the output data.

公开(公告)号：US20210130868A1

公开(公告)日：2021-05-06

申请号：US16500370

申请日：2018-04-05

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Nongjian Tao ,  Shaopeng Wang ,  Hui Yu

IPC: C12Q1/18 ,  G01N21/51 ,  G06T7/00 ,  G16H10/40 ,  G16H30/40 ,  G16H50/20

Abstract: A method for deep learning video microscopy-based antimicrobial susceptibility testing of a bacterial strain in a sample by acquiring image sequences of individual bacterial cells of the bacterial strain in a subject sample before, during, and after exposure to each antibiotic at different concentrations. The image sequences are compressed into static images while preserving essential phenotypic features. Data representing the static images is input into a pre-trained deep learning (DL) model which generates output data; and antimicrobial susceptibility for the bacterial strain is determined from the output data.