An analysis instrument may perform analytical operations on an analyte that is combined with multiple reagents prior to being introduced into a flow cell. The instrument may include a nozzle sipper that aspirates reagents from a recipient, along with an analyte. The reagents may be directed to a volume and may be repeatedly moved into and out of the volume by cycling of a pump. The reagents may be ejected into a destination recipient with the nozzle sipper promoting vorticity in the recipient to enhance mixing. The repeated aspiration and ejection through the nozzle sipper effectively mixes the reagents and the template in an automated or semi-automated fashion.

Certain types of automated medical analysis equipment are used to analyze blood or other fluids. The equipment may thus use various diluents or reagents that allow the blood or other fluids to be run through the analysis equipment for analysis and data collection. Disclosed is a diluent preparation module that combines purified water and reagent concentrate for use by this equipment. Also disclosed is a diluent preparation unit that combines more than one diluent preparation modules for redundancy and back-up purposes. Also disclosed are systems for supplying the Diluent prepared by the diluent preparation module or diluent preparation unit to one or more analytic instruments.

The present disclosure provides machine learning techniques for computationally predicting the phenotypic performance of combinations of genetic variations and for designing new improved host cells. The machine learning models and methods described herein are host agnostic and therefore can be implemented across taxa. Furthermore, the disclosed platform can be implemented to modulate or improve any host cell parameter of interest.

A system for performing a chemical and/or biological process has a pipette for processing material used in the process. The pipette can have a tip that can pierce a sealing material of a chamber and then be placed in contact with the bottom surface of the chamber that is housing a material used in the process. The tip has a plurality of passageways with openings located along the outer perimeter of the tip. The placement of the openings at the outer perimeter permits the tip to be placed on the bottom surface of a chamber such that most of the material in the chamber can be extracted by the pipette. In addition, the placement of the tip on the bottom surface of the chamber with openings along the outer perimeter of the tip permits the pipette to agitate existing material in the chamber when the pipette is providing additional material into the chamber.

The present disclosure provides a HTP microbial genomic engineering platform that is computationally driven and integrates molecular biology, automation, and advanced machine learning protocols. This integrative platform utilizes a suite of HTP molecular tool sets to create HTP genetic design libraries, which are derived from, inter alia, scientific insight and iterative pattern recognition. The HTP genomic engineering platform described herein is microbial strain host agnostic and therefore can be implemented across taxa. Furthermore, the disclosed platform can be implemented to modulate or improve any microbial host parameter of interest.

A matrix droplet extruder includes one or a plurality of reagent containers. Pneumatic connectors are each connectable to a corresponding docking station connector of a docking station that is connectable to a pneumatic generator controllable by a controller to provide pneumatic pressure to the pneumatic docking station connectors. A droplet matrix extrusion surface includes an array of perforations. A liquid management chip has a network of dispensing channels for dispensing reagents from reagent containers through the array of perforations. A pneumatic control network includes pneumatic channels and gates that are controllable by application of the pneumatic pressure to the gates via the pneumatic channels to enable or block dispensing the one or more reagents to repeatedly generate a matrix of droplets when applying the pneumatic pressure to the reagents.

The present disclosure provides a HTP microbial genomic engineering platform that is computationally driven and integrates molecular biology, automation, and advanced machine learning protocols. This integrative platform utilizes a suite of HTP molecular tool sets to create HTP genetic design libraries, which are derived from, inter alia, scientific insight and iterative pattern recognition. The HTP genomic engineering platform described herein is microbial strain host agnostic and therefore can be implemented across taxa. Furthermore, the disclosed platform can be implemented to modulate or improve any microbial host parameter of interest.

Certain types of automated medical analysis equipment are used to analyze blood or other fluids. The equipment may thus use various diluents or reagents that allow the blood or other fluids to be run through the analysis equipment for analysis and data collection. Disclosed is a diluent preparation module that combines purified water and reagent concentrate for use by this equipment. Also disclosed is a diluent preparation unit that combines more than one diluent preparation modules for redundancy and back-up purposes. Also disclosed are systems for supplying the Diluent prepared by the diluent preparation module or diluent preparation unit to one or more analytic instruments.

A method includes, under control of control circuitry implementing a mixing protocol, aspirating reagents from multiple different reagent reservoirs into a cache channel. Designated amounts of the reagents are automatically aspirated from the corresponding reagent reservoirs by corresponding sippers based on the mixing protocol implemented by the control circuitry. The method also includes discharging the reagents from the cache channel into a mixing reservoir, and mixing the reagents within the mixing reservoir to form a reagent mixture.

An automated analyzer including: a stirring part provided with a stirring bar; a cleaning part that cleans the stirring bar; and a control unit that controls the stirring part and the cleaning part, when the control unit causes the stirring part to stir a liquid containing a specimen in a second cycle subsequent to a first cycle, the control unit performing first processing that sets a range of the stirring bar to be cleaned in the second cycle as a cleaning range R2, and when the control unit does not cause the stirring part to stir the liquid in the second cycle, the control unit performing second processing that sets a range of the stirring bar to be cleaned in at least one of the first cycle and the second cycle as a cleaning range R4 that is wider than the cleaning range R2.