Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.