Abstract: Disclosed are a method for increasing a sequencing resolution, and a sequencing device and system. The method includes carrying out irradiation and excitation on a sample to be tested containing a fluorescent dye with at least one type of excitation light to generate a fluorescence signal, where a maximum wavelength in the excitation light is smaller than a wavelength of red light; and the transmitting the generated fluorescence signal through an optical path, and collecting and imaging the signal.

Abstract: This application relates to an interface generation method and an electronic device. In an example interface generation method applied to a first electronic device, the first electronic device determines that content displayed in a first display area includes an interface of a first process and an interface of a second process. The method includes generating, by the first process, a first render tree used to draw the interface of the first process and generating, by the second process, a second render tree used to draw the interface of the second process. The method further includes generating, by a third process, a first target interface based on the first render tree and the second render tree. The first target interface includes the interface of the first process and the interface of the second process and is displayed in the first display area.

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.