Abstract: Disclosed in the present invention is an anti-Nipah virus monoclonal antibody having neutralization activity. The antibody consists of a monkey-derived variable region and a human constant region, and both light and heavy chains of the monkey-derived variable region have unique CDR regions. The antibody provided by the present invention has an excellent antigen binding capability, and has good binding activity with Bangladesh Nipah virus and Malaysia Nipah virus glycoprotein G. The antibody can effectively neutralize the Nipahpseudovirus. Moreover, the neutralization activity of the antibody is enhanced as the concentration of the antibody increases, and nearly 100% neutralization of the Nipahpseudovirus can be achieved at a concentration of 1 ?g/mL. Also disclosed in the present invention is an application of the monoclonal antibody against the Nipah virus glycoprotein G in preparation of a Nipah virus treatment drug.

Abstract: Aspects discussed herein may relate to using machine learning models as part of methods and techniques for ingesting, creating, storing, editing, and managing a document. The document may be a legal contract that includes one or more clauses. Among other things, one or more machine learning models may be configured to recognize clauses and/or classifications, or types, of clauses. For example, the one or more generative language models may be used to generate one or more recommended edits to a clause, generate one or more suggested clauses that are missing from the contract, and/or generate one or more suggested locations where a clause may be inserted into or moved within the contract.

Abstract: Aspects discussed herein may relate to using machine learning models as part of methods and techniques for ingesting, creating, storing, editing, and managing a document. The document may be a legal contract that includes one or more clauses. Among other things, one or more machine learning models may be configured to recognize clauses and/or classifications, or types, of clauses. For example, the one or more generative language models may be used to generate one or more recommended edits to a clause, generate one or more suggested clauses that are missing from the contract, and/or generate one or more suggested locations where a clause may be inserted into or moved within the contract.

Abstract: A method of preparing a flavor composition containing D-allulose includes: (1) weighing D-allulose, mogroside, steviol glycoside and trichlorosucrose in parts by weight, and mixing them evenly to obtain a mixed powder; (2) degassing, compressing, and rolling the mixed powder, and extruding the raw materials into flakes; and (3) crushing and granulating the shaped flakes to obtain the flavor composition. D-allulose is used as the main raw material, and added high-intensity sweeteners as auxiliary raw materials. After the raw materials are mixed evenly, dry granulation is adopted, and the powder is degassed and pre-compressed by using the crystal water in the raw materials, and squeezed by a hydraulic roller to form a bonding force between the various sweetener molecules to form flakes, and the flakes are processed by processes such as crushing and granulating to obtain the flavor composition granules containing D-allulose with the various sweeteners integrated uniformly as a whole.