The present invention relates to a method for establishing a correlation between sizes of genetic material contained in virus, virus-like particles (VLPs) or bacteriophages and the measured particle interior intensities of the particles using hydrated state imaging to quantitatively describe, separate and classify the particle populations. The method further comprises the step of graphically displaying the distribution of the particle interior intensities for each reference sample relative to sizes of the genetic material in each reference sample.
C07K 14/005 - Peptides having more than 20 amino acidsGastrinsSomatostatinsMelanotropinsDerivatives thereof from viruses
C12N 7/00 - Viruses, e.g. bacteriophagesCompositions thereofPreparation or purification thereof
C12Q 1/70 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving virus or bacteriophage
G01N 1/42 - Low-temperature sample treatment, e.g. cryofixation
G01N 15/1433 - Signal processing using image recognition
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
2.
METHOD FOR MACHINE-LEARNING BASED TRAINING AND SEGMENTATION OF OVERLAPPING OBJECTS
The method is for training and automatically segmenting overlapping objects (102, 104) in images such as overlapping objects in images acquired with an imaging device such as a microscope. The overlapping objects are divided into non-overlapping connected components and overlapping segments. The method includes combinatorial set theory in a training scheme and at inference of a machine learning approach for automatic segmentation of overlapping objects (102, 104) imaged with an electron microscope.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
The method is for dividing dark objects, substructures and background of an image from an electron microscope into segments by analyzing pixel values. The segments are transformed and aligned so that the transformed objects, sub-structures and background are meaningfully comparable. The transformed segments are clustered into classes which are used for ontological investigation of samples that are visualized by using electron microscopy. A triangle inequality comparison can be used to further cluster groups of objects to transfer understanding from different interactions between objects and to associate interactions with each other.
G06V 20/69 - Microscopic objects, e.g. biological cells or cellular parts
G06V 10/60 - Extraction of image or video features relating to illumination properties, e.g. using a reflectance or lighting model
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G02B 21/36 - Microscopes arranged for photographic purposes or projection purposes
4.
AN AUTONOMOUS MICROFLUIDIC DEVICE FOR SAMPLE PREPARATION
The microfluidic device (100, 200) has a first reservoir (102, 202) that preferably includes a first liquid (108, 208). The first liquid (108, 208) is being held by a capillary stop valve (228, 230) in the first reservoir (102, 202). A second reservoir (104, 204) is in fluid communication with the first reservoir (102, 202). The second reservoir (104, 204) has a second liquid (110, 210) and a sample support (116, 216) disposed therein. The second reservoir (104, 204) has an inlet opening (145, 236) defined therein. A draining unit (106, 206) is adjacent to the second reservoir (104, 204). The draining unit (106, 206) is in fluid communication with the second reservoir (104, 204). The draining unit (106, 206) has a first absorption member (158, 218, 858) disposed therein.
The method is for preparing a sample in a microfluidic device. The microfluidic device is provided that has a first reservoir in fluid communication with a second reservoir in fluid communication with and adjacent to a draining unit that has a first absorbing member disposed therein. The first reservoir contains a first liquid that is held in the first reservoir by a capillary stop valve connecting the first and second reservoirs. The second reservoir has a sample support disposed therein. A second liquid, containing substances, is added to the second reservoir. The second liquid contacts the first liquid and the first absorbing member. The first absorbing member absorbs the second liquid and the first liquid. The substances adhere to the sample support.
The microfluidic device has a first reservoir that preferably includes a first liquid. The first liquid is being held by a capillary stop valve in the first reservoir. A second reservoir is in fluid communication with the first reservoir. The second reservoir has a second liquid and a sample support disposed therein. The second reservoir has an inlet opening defined therein. A draining unit is adjacent to the second reservoir. The draining unit is in fluid communication with the second reservoir. The draining unit has a first absorption member disposed therein.
The method is for preparing a sample in a microfluidic device. A microfluidic device is provided that has a first reservoir in fluid communication with a second reservoir in fluid communication with and adjacent to a draining unit that has a first absorbing member disposed therein. The first reservoir contains a first liquid that is held in the first reservoir by a capillary stop valve connecting the first and second reservoirs. The second reservoir has a sample support disposed therein. A second liquid, containing substances, is added to the second reservoir. The second liquid contacts the first liquid and the first absorbing member. The first absorbing member absorbs the second liquid and the first liquid. The substances adhere to the sample support.
The method is for dividing dark objects, sub-structures and background of an image from an electron microscope into segments by analyzing pixel values. The segments are transformed and aligned so that the transformed objects, sub-structures and background are meaningfully comparable. The transformed segments are clustered into classes which are used for ontological investigation of samples that are visualized by using electron microscopy. A triangle inequality comparison can be used to further cluster groups of objects to transfer understanding from different interactions between objects and to associate interactions with each other.
The method is for quantification of purity of sub-visible particle samples. A sample to be analyzed is place in an electron microscope to obtain an electron microscopy image of the sample. The sample contains objects. The objects that have sizes being different from a size range of primary particles and sizes being within the size range of primary particles are enhanced. The objects are detected as being primary particles or debris. The detected primary particles are excluded from the objects so that the objects contain debris but no primary particles. A first total area (T1) of the detected debris is measured. A second total area (T2) of the detected primary particles is measured.
G01N 23/04 - Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups , or by transmitting the radiation through the material and forming images of the material
G01B 21/28 - Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring areas
C12Q 1/70 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving virus or bacteriophage
G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G01N 15/14 - Optical investigation techniques, e.g. flow cytometry
G06K 9/46 - Extraction of features or characteristics of the image
G01B 15/00 - Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons
The method is for quantitative measurement of particle content using hydrated state imaging such as CryoTEM. A sample of virus-like particles (VLPs) or virus particles is provided. Preferably, the sample is rapidly frozen into a cryogenic liquid at a cryogenic temperature. While at the cryogenic temperature, the particle content of each VLP in the frozen sample is observed in the CryoTEM. An amount of the particle content of the VLPs is determined to assess whether the VLPs are empty or not.
C12Q 1/70 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving virus or bacteriophage
11.
High precision quantification of sub-visible particles
The method is for quantification of sub-visible particles. A filter membrane is provided that has a plurality of pores defined therethrough. The pores are sealed with a sealant such as glycine or poly-vinyl alcohol (PVA). A sample droplet, containing liquid and sub-visible particles, is applied onto the filter membrane. The liquid dissolves the sealant in pores disposed directly below the sample droplet. The liquid flows through the pores in which the sealant has been dissolved and the sub-visible particles remain on top of the filter membrane. The particles are enumerated in an electron microscope.
The method is for dividing dark objects (102, 122, 123, 126), substructures and background of an image from an electron microscope (304) into segments (128, 142, 150) by analysing pixel values. The segments are transformed and aligned so that the transformed objects, sub-structures and background are meaningfully comparable. The transformed segments (128', 142', 150') are clustered into classes which are used for ontological investigation of samples that are visualized by using electron microscopy. A triangle inequality comparison can be used to further cluster groups of objects to transfer understanding from different interactions between objects and to associate interactions with each other.
The method is for quantification of purity of sub-visible particle samples. A sample to be analyzed is place in an electron microscope to obtain an electron microscopy image of the sample. The sample contains objects. The objects that have sizes being different from a size range of primary particles and sizes being within the size range of primary particles are enhanced. The objects are detected as being primary particles or debris. The detected primary particles are excluded from the objects so that the objects contain debris but no primary particles. A first total area (T1) of the detected debris is measured. A second total area (T2) of the detected primary particles is measured.
G01N 23/04 - Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups , or by transmitting the radiation through the material and forming images of the material
C12Q 1/70 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving virus or bacteriophage
G01B 21/28 - Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring areas
G01N 15/14 - Optical investigation techniques, e.g. flow cytometry
G01B 15/00 - Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons
The method is for quantitative measurement of particle content using hydrated state imaging such as CryoTEM. A sample (100) of virus-like particles (VLPs) or virus particles is provided. Preferably, the sample (100) is rapidly frozen into a cryogenic liquid at a cryogenic temperature. While at the cryogenic temperature, the particle content of each VLP in the frozen sample is observed in the CryoTEM. An amount of the particle content of the VLPs is determined to assess whether the VLPs are empty or not.
The method is for quantification of sub-visible particles. A filter membrane is provided that has a plurality of pores defined therethrough. The filter membrane is in operational engagement with a vacuum chamber. The pores are sealed with a sealant. A sample droplet, containing a liquid and sub-visible particles, is applied onto the filter membrane. The liquid dissolves the sealant in pores disposed directly below the sample droplet. The liquid flows through the pores in which the sealant has been dissolved and the sub-visible particles remain on top of the filter membrane. The particles are enumerated in an electron microscopy.
The method is for quantification of purity of sub-visible particle samples. A sample to be analyzed is place in an electron microscope to obtain an electron microscopy image (100) of the sample. The sample contains objects (114). The objects (114) that have sizes being different from a size range of primary particles (120) and sizes being within the size range of primary particles (120) are enhanced. The objects (114) are detected as being primary particles (120) or debris (106). The detected primary particles (120) are excluded from the objects (114) so that the objects (114) contain debris (106) but no primary particles (120). A first total area (T1) of the detected debris (106) is measured. A second total area (T2) of the detected primary particles (120) is measured.
C12Q 1/70 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving virus or bacteriophage
The method is for automatic astigmatism correction of a lens system. A first image of a first frequency spectrum in a microscope is provided. The first image of a view is not in focus. The first image is then imaged. A first roundness measure of a distribution and directions of intensities in the first image is determined. The lens is changed to a second stigmator setting to provide a second image of a second frequency spectrum. The second image of the view is not in focus. The second image is the same view as the first image of the view at the first stigmator setting. A second roundness measure of a distribution and directions of intensities in the second image is determined. The first roundness measure is compared with the second roundness measure. The image with the roundness measure indicating the roundest distribution is selected.
The method is for quantification of sub-visible particles. A filter membrane (116) is provided that has a plurality of pores (138) defined there through. The filter membrane (116) is in operational engagement with a vacuum chamber (104). The pores are sealed with a sealant (140). A sample droplet (126), containing a liquid (144) and sub-visible particles (142), is applied onto the filter membrane (116). The liquid (144) dissolves the sealant (140) in pores (138e-138h) disposed directly below the sample droplet (126). The liquid (144) flows through the pores in which the sealant (140) has been dissolved and the sub-visible particles (142) remain on top of the filter membrane (116). The particles (142) are enumerated in an electron microscopy.
The method is for automatic astigmatism correction of a lens system. A first image is provided that is not in focus at a first stigmator setting of a set of lenses. A calculating device calculates a corresponding first Fourier spectrum image. A distribution and direction of pixels of the Fourier spectrum image are determined by calculating a first vector and a second vector. The first vector is compared with the second vector. The lens system is changed from a first stigmator setting to a second stigmator setting to provide a second image. A corresponding Fourier spectrum image is calculated. The distribution and direction of pixels of the second Fourier spectrum image is determined by calculating a third vector and a fourth vector. The third vector is compared to the fourth vector. The image that has the lowest vector ratio is selected.
The method is for automatic astigmatism correction of a lens system. A first image (96) is provided that is not in focus at a first stigmator setting of a set of lenses. A calculating device calculates a corresponding first Fourier spectrum image (312). A distribution and direction of pixels of the Fourier spectrum image (128, 130, 312) are determined by calculating a first vector (132) and a second vector (134). The first vector (132) is compared with the second vector (134). The lens system is changed from a first stigmator setting to a second stigmator setting to provide a second image (98). A corresponding Fourier spectrum image (314) is calculated. The distribution and direction of pixels of the second Fourier spectrum image (314) is determined by calculating a third vector and a fourth vector. The third vector is compared to the fourth vector. The image that has the lowest vector ratio is selected.
The method is for the identification and characterization of structures in electron micrographs. Structures in a first image are selected. The structures have a first shape type deformed in a first direction. The selected structures are transformed to a second shape type different from the first shape type. The transformed structures of the second shape type are used to form a plurality of templates. A new structure in a second image is identified. The new structure has the first shape type. The second shape type structure of each template is deformed in the first direction. It is determined which template is a preferred template that best matches the new structure.
A cell is provided that contains a plurality of virus particles. A first image of a first virus particle and a second image of a second virus particle are taken by electron microscopy technology. The first virus particle is characterized as being in a first maturity stage and the second virus particle as being in a second maturity stage. The first image and the second image are transformed to first and second gray scale profiles, respectively, based on pixel data. The first and second gray scale profiles are then saved as first and second templates, respectively. A third virus particle in a third image is identified. The third image is transformed into a third gray scale profile. The third gray scale is compared to the first and second template to determine a maturity stage of the third virus particle.
The method is for intracellular counting and segmentation of viral particles or infectious agents in an image. An image is provided that has a plurality of items therein. A radius range of viral particles is determined. Items in the image having a radius within the predetermined radius range are identified. Elliptical items that are formable from the predetermined radius range are determined. The round and elliptical items identified into groups are sorted. The viral particles among the round and elliptical items are identified. For example, the method may be used for intracellular counting and segmentation of siRNA treated human cytomegaloviral particles in TEM images.
The method is for intracellular counting and segmentation of viral particles in an image. An image is provided that has a plurality of items therein. A radius range of viral particles is determined. Round items in the image having a radius within the predetermined radius range are identified. Elliptical items that are formable from the predetermined radius range are determined. The round and elliptical items identified into groups are sorted. The viral particles among the round and elliptical items are identified. For example, the method may be used for intracellular counting and segmentation of siRNA treated human cytomegaloviral particles in TEM images.
The method is for intracellular counting and segmentation of viral particles in an image. An image is provided that has a plurality of items therein. A radius range of viral particles is determined. Round items in the image having a radius within the predetermined radius range are identified. Elliptical items that are formable from the predetermined radius range are determined. The round and elliptical items identified into groups are sorted. The viral particles among the round and elliptical items are identified. For example, the method may be used for intracellular counting and segmentation of siRNA treated human cytomegaloviral particles in TEM images.
The method is for the identification and characterization of structures in electron micrographs. Structures in a first image are selected. The structures have a first shape type deformed in a first direction. The selected structures are transformed to a second shape type different from the first shape type. The transformed structures of the second shape type are used to form a plurality of templates. A new structure in a second image is identified. The new structure has the first shape type. The second shape type structure of each template is deformed in the first direction. It is determined which template is a preferred template that best matches the new structure.
A cell is provided that contains a plurality of virus particles. A first image of a first virus particle and a second image of a second virus particle are taken by electron microscopy technology. The first virus particle is characterized as being in a first maturity stage and the second virus particle as being in a second maturity stage. The first image and the second image are transformed to first and second gray scale profiles, respectively, based on pixel data. The first and second gray scale profiles are then saved as first and second templates, respectively. A third virus particle in a third image is identified. The third image is transformed into a third gray scale profile. The third gray scale is compared to the first and second template to determine a maturity stage of the third virus particle.
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