Abstract: An interface joint with one or both of a needle actuation mechanism or an integrated shaft rotation mechanism. The interface joint comprises a flexible shaft between a housing and an electrosurgical instrument, e.g. for conveying power and/or fluid therebetween. The needle actuation mechanism comprises an actuation rod extending out of the housing and an actuator movably mounted on the housing. The shaft rotation mechanism comprises a rotation actuator rotatably mounted on the housing and operably coupled to the flexible shaft. Providing an integrated shaft rotation mechanism may obviate the need for a separate torque transmission unit. The improved needle actuation mechanism may use a pivoting connection to enable longer needle extension distances to be achieved for a given actuation distance.

Abstract: A method of generating base calls by a base caller is disclosed. The method includes receiving a plurality of sensor data from a flow cell, wherein the plurality of sensor data is within a first range and identifying a second range, such that at least a threshold percentage of the plurality of sensor data are within the second range. At least a subset of the plurality of sensor data, that are within the second range, are mapped to a third range, thereby generating a plurality of normalized sensor data. The plurality of normalized sensor data is processed in a base caller, to call, for the plurality of normalized sensor data, one or more corresponding bases.

Abstract: This disclosure describes methods, non-transitory computer readable media, and systems that can train a genome-location-classification model to classify or score genomic coordinates or regions by the degree to which nucleobases can be accurately identified at such genomic coordinates or regions. For instance, the disclosed systems can determine sequencing metrics for sample nucleic-acid sequences or contextual nucleic-acid subsequences surrounding particular nucleobase calls. By leveraging ground-truth classifications for genomic coordinates, the disclosed systems can train a genome-location-classification model to relate data from one or both of the sequencing metrics and contextual nucleic-acid subsequences to confidence classifications for such genomic coordinates or regions.