Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.

Abstract: A head-mounted display may include a display system and an optical system in a housing. The display system may have displays that produce images. Positioners may be used to move the displays relative to the eye positions of a user's eyes. An adjustable optical system may include tunable lenses such as tunable cylindrical liquid crystal lenses. The displays may be viewed through the lenses when the user's eyes are at the eye positions. A sensor may be incorporated into the head-mounted display to measure refractive errors in the user's eyes. The sensor may include waveguides and volume holograms, and a camera for gathering light that has reflected from the retinas of the user's eyes. Viewing comfort may be enhanced by adjusting display positions relative to the eye positions and/or by adjusting lens settings based on the content being presented on the display and/or measured refractive errors.

Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.

Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.

Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.

Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.

Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.

Abstract: Disclosed herein is one or more subtilisin variant useful for cleaning applications and in methods of cleaning. One embodiment is directed to one or more subtilisin variant, including one or more Bacillus sp. subtilisin polypeptide variant, and one or more cleaning composition comprising one or more such variant.

Abstract: The present disclosure relates to endo-beta-mannanases from Paenibacillus and Bacillus spp., polynucleotides encoding such endo-beta-mannanases, compositions containing such mannanases, and methods of use thereof. Compositions containing such endo-beta-mannanases are suitable for use as detergents and cleaning fabrics and hard surfaces, as well as a variety of other industrial applications.

Abstract: Described are compositions and methods relating to delayed release enzyme formulations for bleach-containing detergents, and methods of use, thereof. The formulations are designed to promote the rapid release of a bleaching agent into a wash liquor and the delayed release of a protease, to reduce the undesirable effect that high concentrations of bleach have on protease activity. The compositions and methods have applications in, e.g., laundry and dishwashing.

Abstract: Disclosed herein is one or more subtilisin variant useful for cleaning applications and in methods of cleaning. One embodiment is directed to one or more subtilisin variant, including one or more Bacillus sp. subtilisin polypeptide variant, and one or more cleaning composition comprising one or more such variant.

Abstract: A phosphate-free automatic dishwashing cleaning composition including an organic complexing agent and a new protease.

Abstract: The present disclosure relates to endo-beta-mannanases from Paenibacillus and Bacillus spp., polynucleotides encoding such endo-beta-mannanases, compositions containing such mannanases, and methods of use thereof. Compositions containing such endo-beta-mannanases are suitable for use as detergents and cleaning fabrics and hard surfaces, as well as a variety of other industrial applications.

Abstract: Variants of Bacillus sp. TS-23 strain alpha-amylases exhibit improved enzymatic performance, including increased thermostability, reduced calcium dependence, increased washing/cleaning performance, and baking ability. Compositions comprising these variants are useful in methods of starch processing, starch liquefaction, fermatation, starch saccharification, cleaning, laundrying, textile desizing, baking, and biofilm removal.