Abstract: Disclosed herein are methods for performing assays, including general functional assays, on a biological cell. Also disclosed herein are methods of barcoding the 5? ends of RNA from a biological cell and methods of preparation of expression constructs from the barcoded RNA. The barcoded RNA can encode proteins of interest, such as B cell receptor (BCR) heavy and light chain sequences. The expression constructs can be generated individually or in a paired/multiplexed manner, allowing rapid re-expression of individual proteins or protein complexes.

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Abstract: A trailer apparatus configured to be pulled behind a first vehicle includes a first or larger trailer and a second or smaller trailer. The first or larger trailer is configured to be pulled behind the first vehicle. The second or smaller trailer is configured to fit into the first trailer. The first or larger trailer can be used to haul a smaller vehicle, such as an ATV, snowmobile or other recreational vehicle, and the second trailer can be removed from the first trailer so that it can be towed behind the smaller vehicle as a stand-alone trailer. The second trailer can also be configured to form a ramp for the first trailer to aid in loading the first trailer.

Abstract: A magnetic hammer having a handle and a hammer body coupled to the handle includes a cylindrical magnetic insert having first and second ends with the magnetic poles of the insert being aligned with the central axis of the insert. A cylindrical retainer having first and second ends includes a cylindrical chamber which is sized to receive and retain the magnetic insert. The axis of the magnetic insert is aligned with the axis of the cylindrical retainer and the first end of the magnetic insert and the first end of the cylindrical retainer form the hammering face of the hammer. The second end of the cylindrical retainer is coupled to the hammer body.