Abstract: The present disclosure relates to a nucleic acid encoding human HGF and use thereof. The nucleic acid comprises one or more open reading frames (ORFs). The ORF nucleic acid sequence is at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 97%, at least 98%, at least 99% or 100% identical to the nucleic acid sequence of SEQ ID NOs: 21-31. The present disclosure provides a nucleic acid encoding human HGF, as well as a nucleic acid construct, a vector, a cell and a drug which comprise the nucleic acid. The nucleic acid has a protein expression level superior to that of a natural sequence.

Abstract: A nanoparticle preparation system, a control method of the nanoparticle preparation system, an electronic device, a non-transitory computer readable medium, and a method for preparing nanoparticles using the nanoparticle preparation system are disclosed. The nanoparticle preparation system includes a mixing element, a first liquid suction device and a second liquid suction device. The mixing element includes a plurality of mixing channels; the first liquid suction device includes a plurality of first liquid suction tubes arranged in rows along a first direction and configured to input a first preparation solution into the plurality of mixing channels through first inlets of the mixing channels, respectively; and the second liquid suction device includes a plurality of second liquid suction tubes arranged in rows along a second direction and configured to input a second preparation solution into the plurality of mixing channels through second inlets of the mixing channels, respectively.

Abstract: The present disclosure pertains to processing a data model having hierarchical data. A front-end computer sends a request to a back-end computer for dimension candidates for the data model, which is visualized by the front-end computer. The front-end computer is configured score and rank such dimension candidates in order to determine statistics from the data model. The back-end computer determines dimension candidates based on their cardinality and hierarchical information and sends the dimension candidates to the front-end computer. The front-end computer performs the scoring and ranking of dimension candidates and determines statistics for a set of the dimension candidates. The statistics may be presented to a user along with charts and graphs representing the data model.

Abstract: The present disclosure pertains to processing a data model having hierarchical data. A front-end computer sends a request to a back-end computer for dimension candidates for the data model, which is visualized by the front-end computer. The front-end computer is configured score and rank such dimension candidates in order to determine statistics from the data model. The back-end computer determines dimension candidates based on their cardinality and hierarchical information and sends the dimension candidates to the front-end computer. The front-end computer performs the scoring and ranking of dimension candidates and determines statistics for a set of the dimension candidates. The statistics may be presented to a user along with charts and graphs representing the data model.