Abstract: Disclosed herein include systems, machines, devices, and methods for single-pass methylation mapping. C-to-T converted sequence reads and G-to-A converted sequence reads generated from a sample subjected to a methylation assay can be mapped to a mapping reference sequence comprising a C-to-T converted reference sequence and a G-to-A converted reference sequence generated to a reference genome sequence. The counts of Cs and Ts of sequence reads mapped to each of one or more positions with Cs in the reference genome sequence can be used to determine whether the position is a methylated C or an unmethylated C in the sample.

Abstract: Provided is a computer-implemented method, including inputting to a trained machine learning classifier genomic information of a non-training subject that includes features from a tumor sample, wherein the trained machine learning classifier trained on features of tumor samples obtained from training subjects and their a responsiveness to checkpoint inhibition treatment and the machine-learning classifier is trained to predict responsiveness to the treatment, and generating a checkpoint inhibition responsiveness classification predictive of the subject's responding to the checkpoint inhibition with the trained machine-learning classifier, and reporting the checkpoint inhibition responsiveness classification using a graphical user interface. Also provided are a computer system for performing the method and a machine learning classifier trained by the method.

Abstract: A system includes a personal device in communication with a vehicle component and an on-board server and including a processor programmed to receive, from the component, an advertisement defining a low-footprint interface template and a unique identifier indicative of a corresponding rich content interface template, send, to the server, a request including the identifier to provide the corresponding template, and, upon receipt of the corresponding template, render a rich content user interface based on the corresponding template.

Abstract: Provided is a computer-implemented method, including inputting to a trained machine learning classifier genomic information of a non-training subject that includes features from a tumor sample, wherein the trained machine learning classifier trained on features of tumor samples obtained from training subjects and their a responsiveness to checkpoint inhibition treatment and the machine-learning classifier is trained to predict responsiveness to the treatment, and generating a checkpoint inhibition responsiveness classification predictive of the subject's responding to the checkpoint inhibition with the trained machine-learning classifier, and reporting the checkpoint inhibition responsiveness classification using a graphical user interface. Also provided are a computer system for performing the method and a machine learning classifier trained by the method.

Abstract: A system includes a personal device in communication with a vehicle component and an on-board server and including a processor programmed to receive, from the component, an advertisement defining a low-footprint interface template and a unique identifier indicative of a corresponding rich content interface template, send, to the server, a request including the identifier to provide the corresponding template, and, upon receipt of the corresponding template, render a rich content user interface based on the corresponding template.

Abstract: Responsive to location information received from a positioning system indicating a vehicle location corresponding to a predefined location, a controller of a vehicle broadcasts a notification including the vehicle location. Responsive to receiving a request including a device location from a mobile device having interpreted the broadcast, the controller permits access to the vehicle responsive to a difference between the device location and the vehicle location being less than a predefined threshold distance.