Abstract: A method for purchasing an item using a mobile device is provided. The method includes but is not limited to inputting identification information into the mobile device which identifies the item for purchase and generating an order for the item identified by the identification information. The method also includes but is not limited to displaying on a display of the mobile device an optical machine-readable representation of the order.

Abstract: An identification validation system and method may include detecting that a first age restricted item has been selected for purchase at a first point of sale by a user, determining that an age validation is not enabled, requiring that the user submit an identification instrument to verify an age of the user, after the purchase is complete, prompting the user to enable the age validation feature of the mobile wallet application of the user mobile device, directing a software application loaded on the user mobile device to access at least one biometric data input functionality of the user mobile device to capture the at least one biometric of the user, and comparing biometric features of the user captured by at least one camera.

Abstract: An example system for digital wallet management including one or more databases and a computing system is described. The databases are configured to store data corresponding to purchase history of a user, the purchase history including payment account information for a first transaction captured through a reader at a point-of-sale system at the time of the first transaction and an identifier associated with the first transaction. The computing system receives a machine-readable element having the identifier associated with the first transaction encoded therein, searches the databases to determine the payment account information associated with the identifier, generates a prompt requesting confirmation of adding the payment account information for the first transaction to a digital wallet, receives positive confirmation to the generated prompt, and populates the digital wallet with the payment account information.

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 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 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 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 dividing dark objects, substructures and background of an image from an electron microscope into segments 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 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 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 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 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 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 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 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 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 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.