Abstract: This disclosure provides, e.g., novel cells having a modification (e.g., insertion or deletion) in the endogenous CLL1 gene. The disclosure also provides compositions, e.g., gRNAs, that can be used to make such a modification.

Abstract: Various embodiments relate generally to data science and data analysis, computer software and systems, and data-driven control systems and algorithms based on graph-based data arrangements, and, more specifically, to a computing platform configured to receive and analyze datasets to implement a data model with which to identify relevant data catalog data derived from graph-based data arrangements, whereby a processor may be configured to cause implementation of subsets of programmatic executable instructions based on relevant data catalog data, at least one example of which generates a concept interface portion adapted to an associated concept, among other things. In some examples, a method may include identifying attributes associated with data values linked to a conceptual entity, determining criteria data of a targeted application, selecting programmatic executable instructions based on the criteria data, and extracting data from a graph data arrangement as a function of the attributes, among other things.

Abstract: Embodiments generate changes to a logical model. Embodiments receive the changes in a configuration file, the changes comprising a declarative configuration, extract the changes and load the changes into a database and update a corresponding database model. Embodiments generate a first logical model that represents the database model and generate a second logical model that includes the changes. Embodiments generate automatically in a container using the declarative configuration a compiled visualization image from the second logical model, wherein the visualization image is adapted to be used by a business intelligence system to provide a visualization of data that incorporates the changes.

Abstract: Embodiments generate machine learning predictions to discover targeted device energy usage. Embodiments train a first machine learning model to predict a presence of a first device, where a training data used to train the first machine is deficient for a second device. Embodiments train a second machine learning model to predict a presence of a second device. Embodiments receive input data of household energy use and weather data and, based on the input data, use the trained first machine learning model to predict the presence of the first device per household. Based on the input data, embodiments use the trained second machine learning model to predict the presence of the second device per household. Embodiments then subtract the households predicted to have the second device from the households predicted to have the first device to generate a prediction of households that have the first device.

Abstract: A one-component thermosetting epoxy resin adhesive, includes: a) at least one epoxy resin A of the formula (II): wherein the substituents R? and R? independently of one another are either H or CH3 and the index s has a value of 0-12, at least one epoxy novolac EN of the formula: where R2= or CH2, R1=H or methyl and z=0-7, at least one trifunctional or higher functional glycidyl amine type epoxy resin GA, least one latent hardener B for epoxy resins and at least at least one toughness improver D. The epoxy resin adhesive is notable in particular for providing a one-component thermosetting epoxy resin adhesive which shows good adhesion on metal substrates at temperatures around 80° C., even when cured at relatively low temperatures.

Abstract: A cloud-based performance enhancement service captures and collects data relating to interactions of users with industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform.

Abstract: A cloud-based performance enhancement service captures and collects data relating to interactions of users with industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform.

Abstract: Various embodiments relate generally to data science and data analysis, computer software and systems, and data-driven control systems and algorithms based on graph-based data arrangements, and, more specifically, to a computing platform configured to receive and analyze datasets to implement a data model with which to identify relevant data catalog data derived from graph-based data arrangements, whereby a processor may be configured to cause implementation of subsets of programmatic executable instructions based on relevant data catalog data, at least one example of which generates a concept interface portion adapted to an associated concept, among other things. In some examples, a method may include identifying attributes associated with data values linked to a conceptual entity, determining criteria data of a targeted application, selecting programmatic executable instructions based on the criteria data, and extracting data from a graph data arrangement as a function of the attributes, among other things.

Abstract: A cloud-based simulation generation service collects industrial data from multiple industrial customers for storage and analysis on a cloud platform. The service employs a simulation generator component that analyzes data to facilitate generating a simulation model that simulates an industrial automation system, including simulating or emulating industrial devices, industrial processes, other industrial assets, or network-related assets or devices, and their respective interrelationships with each other. The simulation generator component also analyzes modification data to facilitate generating a modified simulation model that simulates the industrial automation system based on the modification.

Abstract: A cloud-based simulation generation service collects industrial data from multiple industrial customers for storage and analysis on a cloud platform. The service employs a simulation generator component that analyzes data to facilitate generating a simulation model that simulates an industrial automation system, including simulating or emulating industrial devices, industrial processes, other industrial assets, or network-related assets or devices, and their respective interrelationships with each other. The simulation generator component also analyzes modification data to facilitate generating a modified simulation model that simulates the industrial automation system based on the modification.

Abstract: Embodiments target users based on energy usage and profile. For example, energy usage data can be received for households monitored using a plurality of meters, where the energy usage data is associated with the households and users. A data set that includes estimated energy usage over geographic areas can be received. The monitored energy usage data and the data set can be combined using a mapping, where the mapping includes location mappings between location information for the households and the geographic areas from the data set. Users with user profiles that meet a targeting criteria can be targeted based on the combined monitored energy usage data and the data set, where the users are targeted based on the location mappings. A messaging campaign that transmits messages to the targeted users can be executed.

Abstract: Embodiments target users based on energy usage and profile. For example, energy usage data can be received for households monitored using a plurality of meters, where the energy usage data is associated with the households and users. A data set that includes estimated energy usage over geographic areas can be received. The monitored energy usage data and the data set can be combined using a mapping, where the mapping includes location mappings between location information for the households and the geographic areas from the data set. Users with user profiles that meet a targeting criteria can be targeted based on the combined monitored energy usage data and the data set, where the users are targeted based on the location mappings. A messaging campaign that transmits messages to the targeted users can be executed.

Abstract: A cloud-based performance enhancement service captures and collects data relating to interactions of users with industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform.

Abstract: A cloud-based performance enhancement service captures and collects data relating to interactions of users with industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform.

Abstract: A cloud-based virtualization generation service collects industrial-automation-system-related data from multiple industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform. A virtualization management component analyzes the data and generates a virtualized industrial automation system of the industrial automation system based on the analysis results. The virtualization management component updates the virtualized industrial automation system based on operation of, and user interactions with, the industrial automation system. The virtualization management component receives user interactions with the virtualized industrial automation system to facilitate remotely interacting with and/or controlling the industrial automation system and/or generates corresponding control signals that it sends to the industrial automation system to facilitate controlling operation of the industrial automation system.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: The invention relates to a method, system, and hardware that creates a differentiated patient/member experience by assessing the complexity of the patient's need and selecting the correct venue of communication. The invention further relates to a system facilitating development, deployment, and use of various applications using storefronts that reflect differing user, privacy, and information requirements of distinct user groups accessing a common data model for information related to health and health care.

Abstract: A cloud-based virtualization generation service collects industrial data from multiple industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform. A virtualization management component (VMC) generates a virtualized industrial automation system of an industrial automation system based on data analysis results. The VMC facilitates remotely controlling the industrial automation system based on user interactions with the virtualized industrial automation system, and updates the virtualized industrial automation system based on collected data relating to the industrial automation system. The VMC customizes a user's view of the virtualized industrial automation system based on a user's role, authorization, location, or preferences, wherein different views of the virtualized industrial automation system with different data overlays are presented on different communication devices of different users.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: A cloud-based simulation generation service collects industrial data from multiple industrial customers for storage and analysis on a cloud platform. The service employs a simulation generator component that analyzes data to facilitate generating a simulation model that simulates an industrial automation system, including simulating or emulating industrial devices, industrial processes, other industrial assets, or network-related assets or devices, and their respective interrelationships with each other. The simulation generator component also analyzes modification data to facilitate generating a modified simulation model that simulates the industrial automation system based on the modification.