Artificial Neural System for Real-Time Flow Cytometry       Analysis in Ocean Research
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{'SignBlockName': 'Ritchie B. Coryell', 'PO_EMAI': None, 'PO_PHON': None}
This proposal deals with the relatively new field of flow                       cytometry, in which lasers are used to excite unique but                        complex signal patterns of individual cells and particles in                    order to quantify and characterize the identification of                        cellular organisms.  This technique obviates the need for time                  consuming, laborious identification by microscope.  Flow                        cytometers have been introduced to the biological oceanography                  field to help identify the presence and extent of phytoplankton                 species.  Oceanic phytoplankton may well be the most important                  element in the global food chain.  It is also one of the major                  photosynthetic producers of CO2, the critical component                         affecting the global carbon cycle.  It also is a prime                          contributor to the build up of CO2 in the atmosphere.  Thus the                 measurement and quantification of phytoplankton is critical to                  our understanding of life on earth.  Although flow cytometry                    has moved the scientist from the microscope to the world of                     rapid quantification, the data produced by flow cytometers is                   copious and must normally be processed ashore.                                                                                                                  This proposal is to apply artificial neural systems to flow                     cytometry to provide real time, at sea, data analysis, hardware                 and software to significantly ease the burden of analysis of                    the data and accelerate research into the life processes in the                 oceans.  ANS systems are capable of rapidly recognizing                         patterns such as those produced by flow cytometry.  KTAADN                      intends to use the services of James Bay Inc. a firm                            specializing in ANS to help define the necessary software.  In                  particular they propose to use, NetMaster, an ANS system,                       developed by James Bay as a basis for the system architecture.
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{'Code': '5371', 'Text': 'SBIR Phase I'}
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