Abstract: Methods and systems are presented for providing a data control framework that enables storing, sharing, and transferring of data in a secure manner. Data files stored in data repositories are scanned. Content associated with different section of each data file is analyzed, and each section is tagged with a sensitivity level based on the content and a subject matter derived for the data file. Each data file is also assigned to a clearance classification based on an expected viewer of the data file. When sections from a first data file is being transferred to a second data file, a data control mechanism is triggered. If a particular section from the first data file is incompatible with the second data file, the data control mechanism may prevent the particular section from being transferred to the second data file, while allowing the remaining sections being transferred to the second data file.

Abstract: An electroporation system including one or more of a pipette, a pipette tip, a pipette docking assembly, and a pulse generator. The pipette docking assembly includes a pipette station, a pipette station guard, and a reservoir (e.g., a buffer tube). A method for transfecting a cell with a payload including providing an electroporation system, providing the cell, providing the payload, introducing the cell and the payload into a pipette tip, and electroporating the cell within the pipette tip by operating the electroporation system.

Abstract: Techniques are disclosed relating to automatically generating and analyzing database queries. In various embodiments, a database inquiry assistance system maintains a first machine learning model trained using query history data for a database and a second machine learning model using analysis history for the database. In an embodiment, the system receives from a user system a request for an inquiry into data stored in the database and identifies a sequence of queries for responding to the request, where identifying the sequence of queries includes applying the second machine learning model to the request. The system generates corresponding database query code for implementing one or more of the queries in the sequence of queries, where generating the corresponding database query code includes applying the first machine learning model to descriptors of one or more of the queries, and sends a plan identifying the sequence of queries to the user system.