Abstract: Aspects presented herein may improve the accuracy of uplink-based positioning when at least one network entity participating in the uplink-based positioning is operating under an energy saving mode. In one aspect, a network entity receives information indicative of a set of antenna panel configurations for a plurality of network nodes, where the set of antenna panel configurations is associated with a set of energy saving modes. The network entity transmits an indication of one or more UL-SRS transmission parameters for a UE based on the information indicative of the set of antenna panel configurations for the plurality of network nodes. In some examples, each antenna panel configuration in the set of antenna panel configurations may include a number of antennas to be used in an UL azimuth angle measurement and a number of antennas to be used in an UL elevation angle measurement under one energy saving mode.

Abstract: The present invention relates to a process for identifying and/or isolating T cells which are capable of binding to a tumour in a mammalian subject having the tumour. Such T cells are of use in cancer immunotherapy. The invention also relates to a method of identifying a peptide neo-antigen for use in cancer immunotherapy in a mammalian subject having a tumour.

Abstract: Technologies relating to improving performance of a computer-implemented model that acts as a multi-class classifier are described herein. A chatbot includes the computer-implemented model, and the computer-implemented model receives natural language input from end users. A subset of the natural language inputs are identified as training examples that are to be used to update the computer-implemented model, wherein the natural language inputs are identified as the training examples based upon comparisons between scores for the natural language inputs output by different classifiers of the computer-implemented model. The training examples are labeled by a developer, and the computer-implemented model is updated based upon the labeled training examples.

Abstract: The present invention relates to a method of whole genome sequencing of a single cell or cell-group for identification of single nucleotide variants, determining chromosome structural variations, or determining phasing information in the genome of the single cell or cell-group. Methods of preparing an indexed DNA library for sequencing of nucleic acid molecules; preparing an indexed DNA library for whole genome sequencing of single cells or cell-groups for the identification of single nucleotide variants, determining chromosome structural variations, or determining phasing information in the genome of the single cells or cell-groups; and whole genome sequencing of a single cell or cell-group to provide data for the identification of single nucleotide variants (SNVs), determining chromosome structural variations, or determining phasing information in the genome of the single cell or cell-group are also described.

Abstract: The present invention relates to a method of determining the status of high-grade serous ovarian carcinoma (HGSOC) in a subject, the method comprising: providing a sample obtained from the subject; and detecting the presence of HGSOC biomarkers in the sample, wherein the method comprises detecting the presence of: a differentiated cell type; a KRT17 Cluster cell type; an epithelial-mesenchymal transition (EMT) cell type; a cell cycle cell type; and a ciliated cell type; wherein the level of the biomarkers is used to determine the fraction of EMT cells in the high-grade serous ovarian carcinoma in the subject. The invention further relates to associated kits, use and methods of treatment.

Abstract: Technologies relating to improving performance of a computer-implemented model that acts as a multi-class classifier are described herein. A chatbot includes the computer-implemented model, and the computer-implemented model receives natural language input from end users. A subset of the natural language inputs are identified as training examples that are to be used to update the computer-implemented model, wherein the natural language inputs are identified as the training examples based upon comparisons between scores for the natural language inputs output by different classifiers of the computer-implemented model. The training examples are labeled by a developer, and the computer-implemented model is updated based upon the labeled training examples.