Urocor, Inc.

Johns Hopkins University

University of Tulsa

Skycam, LLC

Bovaird & Co.

Actioncam. LLC

Cytodiagnostics, Inc.

Kaveh Ashenayi

Robert W Veltri

Michael F Henry

Patrick J Bennett

Alexander MacDonald

Matthew R Jones

Garret Cook

M Craig Miller

Michael P Bacus

Kerry L Sublette

Alan W Partin

Brian Bovaird

Ying Hu

Gerard J O'Dowd

Peter LoPresti

Jonathan I Epstein

Donald P Coffey

An aerial camera system including a plurality of main reels, a camera interface/safety reel and a stabilized camera head. The camera head is supported from main cables from the main reels with a safety reel cable providing power, data and video communication between the camera head and a main computer system. Each of the main reels, the camera interface/safety reel and the camera head are in communication with the main computer system, which controls the feeding and reeling in of the main cables. Further, the computer system controls the feeding and reeling in of the safety reel cable, which typically only follows the camera head as it moves in three-dimensional space, but may in emergency mode support the weight of the camera head and be used to slowly pull the camera head up and out of the way so that it does not interfere with any activity below the flight area.

Aerial camera system

An aerial camera system including a plurality of main reels, a camera interface/safety reel and a stabilized camera head. The camera head is supported from main cables from the main reels with a safety reel cable providing power, data and video communication between the camera head and a main computer system. Each of the main reels, the camera interface/safety reel and the camera head are in communication with the main computer system, which controls the feeding and reeling in of the main cables. Further, the computer system controls the feeding and reeling in of the safety reel cable, which typically only follows the camera head as it moves in three-dimensional space, but may in emergency mode support the weight of the camera head and be used to slowly pull the camera head up and out of the way so that it does not interfere with any activity below the flight area. The aerial camera system may also include a level wind assembly that keeps the cables wound on the drum of the main reels and/or the safety reel in a controlled and consistent manner. Furthermore, the camera head of the aerial camera system may use a rotating gimbal assembly with computer controlled leveling motors for keeping the camera head level as the gimbal assembly moves.

A method to sense pH of a fluid and an optical pH sensor. The method includes the steps of supplying at least a portion of an optical source through an input fiber. The optical source is passed through a sensor head having a sapphire window to a fluid. Optical power reflected from the fluid is collected in a plurality of output fibers in the sensor head. The reflected optical power in the output fibers in the sensor head is thereafter converted to an electrical signal which is used to determine the pH of the fluid.

Optical pH sensor

A neural network is used in a system to detect abnormalities in cells, including cancer in bladder tissue cells. The system has an image analysis system for generating data representative of imaging variables from an image of stained cells. The set of data is provided to a neural network which has been trained to detect abnormalities from known tissue cells with respect to the data from the same set of imaging variables. A conventional sigmoid-activated neural network, or alternatively, a hybrid neural network having a combination of sigmoid, gaussian and sinusoidal activation functions may be utilized. The trained neural network applies a set of weight factors obtained during training to the data to classify the unknown tissue cell as normal or abnormal.

Neural network for cell image analysis for identification of abnormal cells

A method for screening individuals at risk for prostate cancer progression is disclosed. The method is useful for evaluating cells from patients at risk for recurrence of prostate cancer following surgery for prostate cancer. Specifically, the method uses specific Markovian nuclear texture factors, alone or in combination with other biomarkers, to determine whether the cancer will progress or lose organ confinement. In addition, methods of predicting the development of fatal metastatic disease by statistical analysis of selected biomarkers is also disclosed. The invention also contemplates a method that uses a neural network to analyze and interpret cell morphology data. Utilizing Markovian factors and other biomarkers as parameters, the network is first trained with a sets of cell data from known progressors and known non-progressors. The trained network is then used to predict prostate cancer progression in patient samples.

Prediction of prostate cancer progression by analysis of selected

A method for screening individuals at risk for the loss of organ confinement in prostate cancer is disclosed. The method is useful for evaluating cells from patients at risk for recurrence of prostate cancer following surgery for prostate cancer. Specifically, the method uses specific Markovian nuclear texture features, alone or in combination with other biomarkers, to determine whether the cancer will progress or lose organ confinement. In addition, methods of predicting the development of fatal metastatic disease by statistical analysis of selected biomarkers is also disclosed. The invention also contemplates a method that uses a neural network to analyze and interpret cell morphology data. Utilizing Markovian factors and other biomarkers as parameters, the network is first trained with a sets of cell data from known progressors and known non-progressors. The trained network is then used to predict the loss of organ confinement by evaluating patient samples.

Sextant core biopsy predictive mechanism for non-organ confined disease

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Tulsa, OK, United States of America

Kaveh Ashenayi