Abstract: The various aspects disclosed herein relate to bone fixation or fusion devices, including fixation or fusion devices that are implanted around the target bone. Certain device aspects relate to devices that can be bent or otherwise deformed to replicate the natural or desired curve of the bone being treated. In addition, other aspects relate to implantation devices that can be used to implant or position the bone fixation or fusion devices. Aspects related to the making of the device and its use are discussed.

Abstract: The various embodiments disclosed herein relate to bone fixation or fusion devices, including intramedullary fixation or fusion devices that are implanted around the target bone. Certain device embodiments relates to devices that can be bent or otherwise deformed to replicate the natural or desired curve of the bone or joint being treated. In addition, other embodiments relate to implantation devices that can be used to implant or position the bone fixation or fusion devices.

Abstract: The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject. The disclosure also provides methods of training, testing, and validating artificial neural networks.

Abstract: The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject.

Abstract: The various embodiments disclosed herein relate to bone fixation or fusion devices, including intramedullary fixation or fusion devices that are implanted around the target bone. Certain device embodiments relates to devices that can be bent or otherwise deformed to replicate the natural or desired curve of the bone or joint being treated. In addition, other embodiments relate to implantation devices that can be used to implant or position the bone fixation or fusion devices.

Abstract: The various embodiments disclosed herein relate to bone fixation or fusion devices, including intramedullary fixation or fusion devices that are implanted around the target bone. Certain device embodiments relates to devices that can be bent or otherwise deformed to replicate the natural or desired curve of the bone or joint being treated. In addition, other embodiments relate to implantation devices that can be used to implant or position the bone fixation or fusion devices.

Abstract: The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject. The disclosure also provides methods of training, testing, and validating artificial neural networks.

Abstract: The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject. The disclosure also provides methods of training, testing, and validating artificial neural networks.

Abstract: The various embodiments disclosed herein relate to bone fixation or fusion devices, including intramedullary fixation or fusion devices that are implanted around the target bone. Certain device embodiments relates to devices that can be bent or otherwise deformed to replicate the natural or desired curve of the bone or joint being treated. In addition, other embodiments relate to implantation devices that can be used to implant or position the bone fixation or fusion devices.

Abstract: The various embodiments disclosed herein relate to bone fixation or fusion devices, including intramedullary fixation or fusion devices that are implanted around the target bone. Certain device embodiments relates to devices that can be bent or otherwise deformed to replicate the natural or desired curve of the bone or joint being treated. In addition, other embodiments relate to implantation devices that can be used to implant or position the bone fixation or fusion devices.

Abstract: The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject. The disclosure also provides methods of training, testing, and validating artificial neural networks.

Abstract: The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject.

Abstract: The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject.

Abstract: The present disclosure provides devices and systems for diagnosing and characterizing cancer in a subject. Devices include microfilters and microfiltration systems useful for the isolation and characterization of cells from a subject.

Abstract: The various embodiments disclosed herein relate to bone fixation or fusion devices, including intramedullary fixation or fusion devices that are implanted around the target bone. Certain device embodiments relates to devices that can be bent or otherwise deformed to replicate the natural or desired curve of the bone or joint being treated. In addition, other embodiments relate to implantation devices that can be used to implant or position the bone fixation or fusion devices.

Abstract: Screen (10) for a user interface of a television schedule system and process consists of an array (24) of irregular cells (26), which vary in length, corresponding to different television program lengths of one half hour to one-and-one half hours or more. The array is arranged as three columns (28) of one-half hour in duration, and twelve rows (30) of program listings. Some of the program listings overlap two or more of the columns (28) because of their length. Because of the widely varying length of the cells (26), if a conventional cursor used to select a cell location were to simply step from one cell to another, the result would be abrupt changes in the screen (10) as the cursor moved from a cell (26) of several hours length to an adjacent cell in the same row. An effective way of taming the motion is to assume that behind every array (24) is an underlying array of regular cells. By restricting cursor movements to the regular cells, abrupt screen changes will be avoided.