Abstract: A method for detecting a user's outlier days uses data corresponding to features of the user acquired over multiple days by sensors on the user's electronic device. The data acquired for each day and feature is labeled as regular or irregular by applying N labeling approaches. One of the N labeling approaches compares the data for each feature with how values of previously acquired data for corresponding features are distributed. N labels are generated for the data for each feature and day. The machine learning classification model is trained using one of the N labels for each of the N labeling approaches. An optimal labeling approach is selected from among the N labeling approaches for each feature using the machine learning classification model. For each feature, the method determines whether each of the days is an outlier day for the user using the labels obtained with the optimal labeling approach.

Abstract: A system for improving a user's wellbeing uses machine learning to evaluate statistical parameters and to recommend parameter ranges that will improve the user's wellbeing. The system receives user data including ambient and environmental data related to the user's location, physiological data related to the user's body, and behavioral data of the user. Statistical parameters are generated that characterize the data based on a time period corresponding to the data. A wellbeing score for the user is generated by applying a probabilistic model to the statistical parameters. The model determines a recommended range of values for the parameters related to the ambient data, environmental data, physiological data and behavioral data that will improve the user's wellbeing score based on the statistical parameters and the wellbeing score. Behavior modifications based on the recommended range of values for parameters are recommended to the user so as to improve the user's wellbeing score.

Abstract: A method for optimizing a user's behavioral changes uses an artificial intelligence system to present actions to the user for achieving a behavioral change goal. The goal is first set at a time instant t. A user characteristics vector and current state vector are generated using static and dynamic user characteristics describing the user's current state. The vectors are used to generate a behavioral change tool vector for the time instant t. The behavioral change tool vector is presented to the user to select a suggested action for achieving the goal. The user characteristics, current state, and behavioral change tool vectors for the time instant t are mapped to a next current state vector for a next time instant t+1. The system repeats generating the behavioral change tool vector using vectors updated for the next time instant t+1 until the mapping indicates that the behavioral change goal has been achieved.

Abstract: The potential role of so called ‘cell adhesion recognition motifs’ (CARs) in cadherin adhesion has been emphasized. Due to the importance of cadherin binding in biological process, there remains a need to develop effective ways of manipulating cadherin adhesion. According to the present invention, there is provided a pair of cadherin molecules modified to enhance intermolecular adhesion (i.e. adhesion or binding between the pair of cadherin molecules) compared with corresponding unmodified cadherin molecules. Intermolecular adhesion between the cadherin molecules may be enhanced by reducing or eliminating intramolecular binding within each cadherin molecule. For example, intramolecular binding may be reduced or eliminated by diminishing or preventing intramolecular binding of an N-terminal binding strand of each cadherin molecule with a binding strand acceptor pocket of each cadherin molecule.