Abstract: A composition includes a crosslinked polysiloxane elastomer with 2 or more carbon-oxygen-silicon linkages between a polysaccharide component and polysiloxane component, where the polysaccharide component is other than a cellulose or starch component.

Abstract: Techniques for efficiently generating and displaying light-field data are disclosed. In one particular embodiment, the techniques may be realized as a method for generating light-field data, the method comprising receiving input image data, synthesizing a first plurality of viewpoints based on the input image data, synthesizing a second plurality of viewpoints based on cached image data, combining the first and second plurality of viewpoints, yielding a plurality of blended viewpoints, displaying the plurality of blended viewpoints, and caching image data associated with the plurality of blended viewpoints.

Abstract: Techniques for efficiently generating and displaying light-field data are disclosed. In one particular embodiment, the techniques may be realized as a method for generating light-field data, the method comprising receiving input image data, synthesizing a first plurality of viewpoints based on the input image data, synthesizing a second plurality of viewpoints based on cached image data, combining the first and second plurality of viewpoints, yielding a plurality of blended viewpoints, displaying the plurality of blended viewpoints, and caching image data associated with the plurality of blended viewpoints.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a machine learning model such as a trained convolutional neural network, the presence of a genomic instability in a tumor sample. The genomic data analyzer may use whole genome sequencing reads as input data even at low sequencing coverage in a high throughput sequencing workflow as may be routinely employed in a diversity of clinical oncology setups. The genomic data analyzer may arrange the aligned read data coverage from chromosome arms or full chromosomes to form a coverage data signal array possibly as an image. The trained machine learning model may process the coverage data signal array to determine whether a chromosomal spatial instability (CSI) such as for instance a genomic instability caused by a homologous repair or recombination deficiency (HRD) is present in the tumor sample. The latter indication may guide the choice of a preferred anticancer treatment for the tumor.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a machine learning model such as a trained convolutional neural network, the presence of a genomic instability in a tumor sample. The genomic data analyzer may use whole genome sequencing reads as input data even at low sequencing coverage in a high throughput sequencing workflow as may be routinely employed in a diversity of clinical oncology setups. The genomic data analyzer may arrange the aligned read data coverage from chromosome arms or full chromosomes to form a coverage data signal array possibly as an image. The trained machine learning model may process the coverage data signal array to determine whether a chromosomal spatial instability (CSI) such as for instance a genomic instability caused by a homologous repair or recombination deficiency (HRD) is present in the tumor sample. The latter indication may guide the choice of a preferred anticancer treatment for the tumor.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a machine learning model such as a trained convolutional neural network, the presence of a genomic instability in a tumor sample. The genomic data analyzer may use whole genome sequencing reads as input data even at low sequencing coverage in a high throughput sequencing workflow as may be routinely employed in a diversity of clinical oncology setups. The genomic data analyzer may arrange the aligned read data coverage from chromosome arms or full chromosomes to form a coverage data signal array possibly as an image. The trained machine learning model may process the coverage data signal array to determine whether a chromosomal spatial instability (CSI) such as for instance a genomic instability caused by a homologous repair or recombination deficiency (HRD) is present in the tumor sample. The latter indication may guide the choice of a preferred anticancer treatment for the tumor.