Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin roughness. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A skin roughness model, trained with the pixel data, is operable to output, across a range of a skin roughness scale, skin roughness values associated with a degree of skin roughness. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin roughness model, to determine a user-specific skin roughness value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining dark eye circles. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A dark eye circles model, trained with the pixel data, is operable to output, across a range of a dark eye circles scale, dark eye circles values associated with a degree of dark eye circles. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the dark eye circles model, to determine a user-specific dark eye circles value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining body contour. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A body contour model, trained with the pixel data, is operable to output, across a range of a body contour scale, body contour values associated with a degree of body contour. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the body contour model, to determine a user-specific body contour value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin dryness. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A skin dryness model, trained with the pixel data, is operable to output, across a range of a skin dryness scale, skin dryness values associated with a degree of skin dryness. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin dryness model, to determine a user-specific skin dryness value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin oiliness. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A skin oiliness model, trained with the pixel data, is operable to output, across a range of a skin oiliness scale, skin oiliness values associated with a degree of skin oiliness. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin oiliness model, to determine a user-specific skin oiliness value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin pore size. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A skin pore size model, trained with the pixel data, is operable to output, across a range of a skin pore size scale, skin pore size values associated with a degree of skin pore size. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin pore size model, to determine a user-specific skin pore size value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin hyperpigmentation. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A skin hyperpigmentation model, trained with the pixel data, is operable to output, across a range of a skin hyperpigmentation scale, skin hyperpigmentation values associated with a degree of skin hyperpigmentation. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin hyperpigmentation model, to determine a user-specific skin hyperpigmentation value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: Digital imaging systems and methods are described for analyzing pixel data of an image of a skin area of a user for determining skin puffiness. A plurality of training images of a plurality of individuals are aggregated, each of the training images comprising pixel data of a respective skin area of an individual. A skin puffiness model, trained with the pixel data, is operable to output, across a range of a skin puffiness scale, skin puffiness values associated with a degree of skin puffiness. An image of a user comprising pixel data of at least a portion of a user skin area is received and analyzed, by the skin puffiness model, to determine a user-specific skin puffiness value of the user skin area. A user-specific electronic recommendation addressing at least one feature identifiable within the pixel data is generated and rendered, on a display screen of a user computing device.

Abstract: The invention relates to a ligand compound having a structure A-B-C, wherein (a) A represents a mono- or polyphosphorylated amino acid linked to part B by its amino group to form an amide bond; B represents (i) a carboxylic acid, and (ii) an amino acid or peptidyl group of 2-10 amino acids, an alkyl or alkenyl group comprising 1-26 carbon atoms, a polyethylene glycol group comprising 1-26 carbon atoms or a combination thereof covalently linked to the carboxylic acid; and C represents a hydrophilic group covalently linked to the group of B (ii) or (b) A represents a mono-or polyphosphorylated amino acid linked to B by its carboxylic acid to form an amide bond; B represents an amino acid or peptidyl group of 2-10 amino acids, an amino substituted alkyl or alkenyl group comprising 1-26 carbon atoms, an amino substituted polyethylene glycol group comprising 1-26 carbon atoms or a combination thereof covalently linked to A by their amino group; C represents a hydrophilic group covalently linked to the group of B.

Abstract: A system for providing a customized list of links to content, the customized list of links provided to a user. The system receives a collection of unfiltered links to content, the collection of unfiltered links received from a server. The system hides the collection of unfiltered links from the user. The system receives parameters for customizing the collection of unfiltered links. The parameters are received from the user. The system selectively presents a customized list of links to content to the user, wherein selectively presenting the customized list of links to content comprises presenting a subset of the collection of unfiltered links in accordance with the received parameters.

Abstract: A system for providing a customized list of links to content, the customized list of links provided to a user. The system receives a collection of unfiltered links to content, the collection of unfiltered links received from a server. The system hides the collection of unfiltered links from the user. The system receives parameters for customizing the collection of unfiltered links. The parameters are received from the user. The system selectively presents a customized list of links to content to the user, wherein selectively presenting the customized list of links to content comprises presenting a subset of the collection of unfiltered links in accordance with the received parameters.