Abstract: The present disclosure provides for endonuclease enzymes having distinguishing domain features, as well as methods of using such enzymes or variants thereof.

Abstract: The invention pertains to a process for the production of crystalline carbon structure networks in a reactor 3 which contains a reaction zone 3b and a termination zone 3c, by injecting a thermodynamically stable micro-emulsion c, comprising metal catalyst nanoparticles, into the reaction zone 3b which is at a temperature of above 600° C., preferably above 700° C., more preferably above 900° C., even more preferably above 1000° C., more preferably above 1100° C., preferably up to 3000° C., more preferably up to 2500° C., most preferably up to 2000° C., to produce crystalline carbon structure networks e, transferring these networks e to the termination zone 3c, and quenching or stopping the formation of crystalline carbon structure networks in the termination zone by spraying in water d.

Abstract: The present disclosure provides for endonuclease enzymes having distinguishing domain features, as well as methods of using such enzymes or variants thereof.

Abstract: Methods and systems are provided for determining the category of a software application utilizing machine learning (ML) and knowledge graph techniques, and for controlling access to the application by a user based on the category and configured time restrictions for the user. The system includes a feature set extractor and a category predictor with a trained ML model. The trained ML model generates the category of the application based on a feature(s) of the application. The generated category is indicated in a data structure. An access request handler receives a request related to access to the application from a user device. A category determiner determines the category of the application from the data structure. A time usage manager determines an available time usage for the category and the specified user. The access arbiter responds to the request from the user device with the available time usage.

Abstract: The present disclosure provides for endonuclease enzymes as well as methods of using such enzymes or variants thereof.

Abstract: Described herein are methods, compositions, and systems derived from uncultivated microorganisms useful for gene editing.

Abstract: The invention pertains to a process for the production of crystalline carbon nanofibre networks from pyrolysis oil in a furnace black reactor 3 which contains a reaction zone 3b and a termination zone 3c, by injecting a thermodynamically stable pyrolysis oil-comprising micro-emulsion c, comprising metal catalyst nanoparticles, into the reaction zone 3b which is at a temperature of above 600° C., preferably above 700° C., more preferably above 900° C., even more preferably above 1000° C., more preferably above 1100° C., preferably up to 3000° C., more preferably up to 2500° C., most preferably up to 2000° C., to produce crystalline carbon structure networks e, transferring these networks e to the termination zone 3c, and quenching or stopping the formation of crystalline carbon structure networks in the termination zone by spraying in water d.

Abstract: Methods and systems are provided for determining the category of a software application utilizing machine learning (ML) and knowledge graph techniques, and for controlling access to the application by a user based on the category and configured time restrictions for the user. The system includes a feature set extractor and a category predictor with a trained ML model. The trained ML model generates the category of the application based on a feature(s) of the application. The generated category is indicated in a data structure. An access request handler receives a request related to access to the application from a user device. A category determiner determines the category of the application from the data structure. A time usage manager determines an available time usage for the category and the specified user. The access arbiter responds to the request from the user device with the available time usage.

Abstract: Methods and systems are provided for determining the category of a software application utilizing machine learning (ML) and knowledge graph techniques, and for controlling access to the application by a user based on the category and configured time restrictions for the user. The system includes a feature set extractor and a category predictor with a trained ML model. The trained ML model generates the category of the application based on a feature(s) of the application. The generated category is indicated in a data structure. An access request handler receives a request related to access to the application from a user device. A category determiner determines the category of the application from the data structure. A time usage manager determines an available time usage for the category and the specified user. The access arbiter responds to the request from the user device with the available time usage.

Abstract: Methods and systems are provided for determining the category of a software application utilizing machine learning (ML) and knowledge graph techniques, and for controlling access to the application by a user based on the category and configured time restrictions for the user. The system includes a feature set extractor and a category predictor with a trained ML model. The trained ML model generates the category of the application based on a feature(s) of the application. The generated category is indicated in a data structure. An access request handler receives a request related to access to the application from a user device. A category determiner determines the category of the application from the data structure. A time usage manager determines an available time usage for the category and the specified user. The access arbiter responds to the request from the user device with the available time usage.