Abstract: A method for training a neural network for detecting a plurality of classes of object within a sample comprises providing a training data set comprising a plurality of samples, each annotated according to whether the samples include labelled objects of interest. In a first type of samples, all objects of interest are labelled according to their class and comprise a foreground of said samples, the remainder of the samples comprising background. In a second type of samples, some objects of interest are labelled in a foreground and their background may comprise unlabelled objects. A third type of samples comprise only background comprising no objects of interest. Negative mining is only performed on the results of processing the first and third types of samples.

Abstract: A method, non-transitory computer readable medium, and system that manages remote access including receiving a reservation request from a client computing device to access a physical location at a future specified time period secured by one or more remote locking devices. A determination is made on when one or more stored criteria associated with the one or more remote locking devices have been satisfied by the requesting client computing device. An identifier for the requesting client computing device and an access code to unlock the one or more remote locking devices during the future specified time period which is accessible by the one or more remote locking devices without further communication with the remote access management computing device is provided when the determination indicates the one or more stored criteria are satisfied. Additionally, the access code to the requesting client computing device is provided when the determination indicates the one or more stored criteria are satisfied.

Abstract: The invention provides methods of detecting a natriuretic protein, a troponin, and galectin-3 in samples from subjects in order to determine risk of developing new onset heart failure with preserved ejection fraction (HFpEF).