Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of molecules.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of molecules.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of molecules.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of molecules.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of a molecule. In one aspect, a method comprises: obtaining data identifying an input molecule; generating data defining a toxicity analysis tree for the input molecule; and processing the toxicity analysis tree to generate a respective toxicity score for each of a plurality of molecule fragments in the input molecule that characterizes an impact of the molecule fragment on a toxicity of the input molecule.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of molecules.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of molecules.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of a molecule. In one aspect, a method comprises: obtaining data identifying an input molecule; generating data defining a toxicity analysis tree for the input molecule; and processing the toxicity analysis tree to generate a respective toxicity score for each of a plurality of molecule fragments in the input molecule that characterizes an impact of the molecule fragment on a toxicity of the input molecule.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of a molecule. In one aspect, a method comprises: obtaining data identifying an input molecule; generating data defining a toxicity analysis tree for the input molecule; and processing the toxicity analysis tree to generate a respective toxicity score for each of a plurality of molecule fragments in the input molecule that characterizes an impact of the molecule fragment on a toxicity of the input molecule.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of a molecule. In one aspect, a method comprises: obtaining data identifying an input molecule; generating a toxicity dose-response curve for the input molecule using a toxicity prediction machine learning model, comprising: for each dose value in a sequence of dose values: generating a model input to the toxicity prediction machine learning model that comprises: (i) a set of features characterizing the input molecule, and (ii) the dose value; and processing the model input using the toxicity prediction machine learning model to generate a toxicity prediction for the input molecule at the dose value; generating the toxicity dose-response curve for the input molecule based on the toxicity predictions for the sequence of dose values; and outputting the toxicity dose-response curve for the input molecule.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of molecules.

Abstract: In an aspect of the present disclosure is a system for monitoring health of a motor, including at least one sensor configured to detect at least a motor metric and send motor datum based on the at least a motor metric, an augmented reality display configured to display a visual representation of the motor datum, and a computing device communicatively connected to the at least one sensor and the augmented reality display, wherein the computing device is configured to: receive the motor datum from the at least one sensor; and command the augmented reality display to display the visual representation of the motor datum.

Abstract: An apparatus for manufacturing a stator for an electric aircraft motor includes a manufacturing device. The manufacturing device includes a work holding device configured to hold at least one segment of teeth. The manufacturing device also includes a winding device configured to create a modular winding set in the at least one segment of teeth. Creating the modular winding set includes acquiring a continuous conductor and winding a continuous conducting coil on each tooth of the plurality of teeth held by the work holding device using the continuous conductor. The manufacturing device may also include an installation device configured to install a plurality of segments of teeth into the stator.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: A method for motor manufacturing using a clip system is illustrated. The method includes sliding a plurality of magnets onto a vertical dowel hoop, wherein the plurality of magnets are stacked on each dowel, attaching a first plate to the top of the vertical dowel hoop and a second plate to the bottom of the vertical dowel hoop, wherein the plates compress the plurality of magnets on each vertical dowel, and curing the combination of the first plate, the second plate, the plurality of magnets and the vertical dowel hoop.

Abstract: An apparatus for manufacturing a stator for an electric aircraft motor, wherein the apparatus includes at least a manufacturing device. The manufacturing device further includes at least a work-holding device configured to hold a first segment of teeth. The manufacturing device further includes at least a winding device configured to create a modular winding set in the first segment of teeth, wherein creating a modular winding set further include acquiring a continuous conductor and winding a continuous conducting coil upon each tooth of the plurality of teeth using the continuous conductor. The manufacturing device further includes at least an installation device configured to install a plurality of segment of teeth into the stator.

Abstract: A high density correlation system can train a machine-learned model to determine one or more phenotypes of a cell and identify compounds corresponding to a user-queried phenotype. The high density correlation system can generate training data using single-cell images and train the machine-learned model using the generated training data. The machine-learned model can determine phenotypes of cells based on images of the cells. The high density correlation system can generate a database that includes phenotype-compound mappings generated based on the outputs of the machine-learned model. After receiving a query that identifies a phenotype, the high density correlation system can generate a result set of the query using the database for display at a graphical user interface (GUI). The result set can identify compounds corresponding to the identified phenotype. Additionally, the displayed compounds can be ordered based on a score for each compound.

Abstract: An apparatus for manufacturing a stator for an electric aircraft motor, wherein the apparatus includes at least a manufacturing device. The manufacturing device further includes at least a work-holding device configured to hold a first segment of teeth. The manufacturing device further includes at least a winding device configured to create a modular winding set in the first segment of teeth, wherein creating a modular winding set further include acquiring a continuous conductor and winding a continuous conducting coil upon each tooth of the plurality of teeth using the continuous conductor. The manufacturing device further includes at least an installation device configured to install a plurality of segment of teeth into the stator.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: A method for in utero and postnatal genome editing of a lysosomal storage disease gene, the method comprising administering to a subject an ABE or CBE complex, wherein the subject is an embryo, a fetus, a neonate, a child or an adult, ABE or CBE complex comprising CRISPR-mediated base editor and a guide RNA (gRNA), the gRNA targeting a mutation in a therapeutic gene; and introducing a modified codon in the therapeutic gene by base editing the therapeutic gene without inducing double strand DNA breaks, wherein the base editing is performed by the adenoviral vector, an adeno-associated viral vector, nucleoprotein complex or an mRNA in a lipid based nanoparticle.