Abstract: Disclosed are a super-resolution imaging system (1, 41, 51), a super-resolution imaging method, a biological sample identification system (4, 61) and method, a nucleic acid sequencing imaging system (5) and method, and a nucleic acid identification system (6) and method. The super-resolution imaging system (1, 41, 51) includes an illumination system (A) and an imaging system (B). The illumination system (A) outputs excitation light to irradiate a biological sample to generate excited light, and the imaging system (B) collects and records the excited light to generate an excited light image. The illumination system (A) includes an excitation light source (10, 10a) and a structured light generation and modulation device (11, 11a). The excitation light source (10, 10a) outputs the excitation light, and the structured light generation and modulation device (11, 11a) modulates the excitation light into structured light to irradiate the biological sample to generate the excited light.

Abstract: Embodiments of the present disclosure pertain to network based machine learning generated simulations. In one embodiment, the present disclosure includes a computer implemented method comprising sending first code comprising a programmable calculator from a server system to a client system across a network. A data request is sent to a database, the data request configured to retrieve data from the database comprising a plurality of fields and a target field. The retrieved data is processed using a machine learning algorithm to produce a weight for each field of the plurality of fields and a scoring data structure. The fields and the scoring data structure are sent to the client system across the network. A user selects values for the plurality of fields and the programmable calculator is configured based on the scoring data structure to generate a simulated value for the target field based on the user selected values.

Abstract: Embodiments of the present disclosure pertain to network based machine learning generated simulations. In one embodiment, the present disclosure includes a computer implemented method comprising sending first code comprising a programmable calculator from a server system to a client system across a network. A data request is sent to a database, the data request configured to retrieve data from the database comprising a plurality of fields and a target field. The retrieved data is processed using a machine learning algorithm to produce a weight for each field of the plurality of fields and a scoring data structure. The fields and the scoring data structure are sent to the client system across the network. A user selects values for the plurality of fields and the programmable calculator is configured based on the scoring data structure to generate a simulated value for the target field based on the user selected values.