Abstract: The present invention generally relates to specificity assays using cell cultures, in particular to chimeric antigen receptor (CAR) expressing reporter T (CAR-T) cell assays to test novel antigen binding moieties in different formats. Furthermore, the present invention relates to the use of CAR-T cells, transfected/transduced with an engineered chimeric antigen receptor (CAR) comprising a target antigen binding moiety capable of specific binding to a recognition domain of an antigen binding molecule. The invention also relates to methods and kits for specificity testing of a candidate antigen binding moiety and/or nucleic acid molecules and vectors expressing engineered CARs.

Abstract: The present invention relates to combination therapies employing anti-CD20/anti-CD3 bispecific antibodies and 4-1BB (CD137) agonists, in particular 4-1 BBL trimer containing antigen binding molecules, the use of these combination therapies for the treatment of cancer and methods of using the combination therapies.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a variant analysis module, biallelic genomic alterations for at least one gene in next generation sequencing variant calling information for patient tumor samples characterized by different purity ratios of somatic genomic material. The variant analysis module may compare the observed variant fraction distributions of putative heterozygous germline mutations to the theoretical distributions corresponding to different chromosomal aberration events to detect a combination of genomic alteration events possibly causing the biallelic loss of function of the gene.

Abstract: A method for lossy video encoding, transmission and decoding, the method comprising the steps of: receiving an input video at a first computer system; encoding an input frame of the input video to produce a latent representation; producing a quantized latent; producing a hyper-latent representation; producing a quantized hyper-latent; entropy encoding the quantized latent; transmitting the entropy encoded quantized latent and the quantized hyper-latent to a second computer system; decoding the quantized hyper-latent to produce a set of context variables, wherein the set of context variables comprise a temporal context variable; entropy decoding the entropy encoded quantized latent using the set of context variables to obtain an output quantized latent; and decoding the output quantized latent to produce an output frame, wherein the output frame is an approximation of the input frame.

Abstract: A method of training one or more neural networks, the one or more neural networks being for use in lossy image or video encoding, transmission and decoding, the method comprising steps including: receiving an input image at a first computer system; encoding the input image using a first neural network and decoding the latent representation using a second neural network to produce an output image; at least one of the plurality of layers of the first or second neural network comprises a transformation; and the method further comprises the steps of: evaluating a difference between the output image and the input image and evaluating a function based on an output of the transformation; updating the parameters of the first neural network and the second neural network based on the evaluated difference and the evaluated function; and repeating the above steps.

Abstract: The present invention generally relates to antibodies that bind to GPRC5D, including bispecific antigen binding molecules e.g. for activating T cells. In addition, the present invention relates to polynucleotides encoding such antibodies, and vectors and host cells comprising such polynucleotides. The invention further relates to methods for producing the antibodies, and to methods of using them in the treatment of disease.

Abstract: In various example embodiments, relevant changes between 3D models of a scene are detected and classified by transforming the 3D models into point clouds and applying a deep learning model to the point clouds. The model may employ a Siamese arrangement of sparse lattice networks each including a number of modified BCLs. The sparse lattice networks may each take a point cloud as input and extract features in 3D space to provide a primary output with features in 3D space and an intermediate output with features in lattice space. The intermediate output from both sparse lattice networks may be compared using a lattice convolution layer. The results may be projected into the 3D space of the point clouds using a slice process and concatenated to the primary io outputs of the sparse lattice networks. Each concatenated output may be subject to a convolutional network to detect and classify relevant changes.