Abstract: Aspects of the present disclosure include methods for flow cytometrically sorting a sample with particles, such as cells, based on order of identification. Methods according to certain embodiments include introducing the sample into a flow cytometer; flowing the introduced sample in a flow stream; irradiating the sample in the flow stream with a light source; detecting light from cells in the sample flowing in the flow stream; identifying phenotypes of cells in the sample flowing in the flow stream based on one or more data signals generated from the detected light; and dynamically sorting into partitions cells of the sample that have a phenotype of a collection of predetermined phenotypes based on order of identification. Systems for practicing the subject methods are also provided. Non-transitory computer readable storage mediums are also described.

Abstract: Methods of classifying flow cytometer data are provided. Methods of interest include receiving a first gate and flow cytometer data, expanding the first gate to generate a second gate, and determining sets of flow cytometer data encompassed by each of the first gate and the second gate to classify the flow cytometer data. In embodiments, methods also involve recording a subset of the classified flow cytometer data and optionally adjusting the first and/or second gates based on the recorded data. In some cases, the subject methods include sorting particles associated with the classified flow cytometer data based on the first and second gates. Systems and computer-readable storage media for practicing the invention are also provided.

Abstract: Aspects of the present disclosure include methods for flow cytometrically sorting a sample with particles, such as cells, based on order of identification. Methods according to certain embodiments include introducing the sample into a flow cytometer; flowing the introduced sample in a flow stream; irradiating the sample in the flow stream with a light source; detecting light from cells in the sample flowing in the flow stream; identifying phenotypes of cells in the sample flowing in the flow stream based on one or more data signals generated from the detected light; and dynamically sorting into partitions cells of the sample that have a phenotype of a collection of predetermined phenotypes based on order of identification. Systems for practicing the subject methods are also provided. Non-transitory computer readable storage mediums are also described.

Abstract: A screw fastener (10) including an elongate shank (11) having a longitudinal axis (A), a tip (12) at one end of the shank (11), a head (13) at the opposite end of the shank (11), and a self-tapping screw thread (14) extending along at least a major portion of the shank (11). The head (13) includes a generally planar end face (17) which is generally perpendicular to the longitudinal axis (A), and which includes means (18) for cooperation with a screw driver. The head (13) further includes boring means (15) that permits the head (13) to bore into a sheeting substrate (20). Control means (19) are provided for controlling the depth of penetration of the head (13) into the sheeting substrate (20).

Abstract: A screw fastener (10) including an elongate shank (11) having a longitudinal axis (A), a tip (12) at one end of the shank (11), a head (13) at the opposite end of the shank (11), and a self-tapping screw thread (14) extending along at least a major portion of the shank (11). The head (13) includes a generally planar end face (17) which is generally perpendicular to the longitudinal axis (A), and which includes means (18) for cooperation with a screw driver. The head (13) further includes boring means (15) that permits the head (13) to bore into a sheeting substrate (20). Control means (19) are provided for controlling the depth of penetration of the head (13) into the sheeting substrate (20).