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10.3233/ACP-CLO-2010-0551 http://scihub22266oqcxt.onion/10.3233/ACP-CLO-2010-0551 C4605732!4605732!21127378     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Anal+Cell+Pathol+(Amst) 2010 ; 33 (5-6): 271-85 Nephropedia Template TP {{tp|p=21127378|t=2010. Ultra-Fast Processing of Gigapixel Tissue MicroArray Images Using High Performance Computing |pdf=|usr=}}{{21127378}} gab.com Text Ultra-Fast Processing of Gigapixel Tissue MicroArray Images Using High Performance Computing JO: Anal Cell Pathol (Amst) http://www.kidney.de/pget.php?&tw=1&pmid=21127378 #FOAvip Background: Tissue MicroArrays (TMAs) are a valuable platform for tissue based translational research and the discovery of tissue biomarkers. The digitised TMA slides or TMA Virtual Slides, are ultra-large digital images, and can contain several hundred samples. The processing of such slides is time-consuming, bottlenecking a potentially high throughput platform.Methods: A High Performance Computing (HPC) platform for the rapid analysis of TMA virtual slides is presented in this study. Using an HP high performance cluster and a centralised dynamic load balancing approach, the simultaneous analysis of multiple tissue-cores were established. This was evaluated on Non-Small Cell Lung Cancer TMAs for complex analysis of tissue pattern and immunohistochemical positivity.Results: The automated processing of a single TMA virtual slide containing 230 patient samples can be significantly speeded up by a factor of circa 22, bringing the analysis time to one minute. Over 90 TMAs could also be analysed simultaneously, speeding up multiplex biomarker experiments enormously.Conclusion: The methodologies developed in this paper provide for the first time a genuine high throughput analysis platform for TMA biomarker discovery that will significantly enhance the reliability and speed for biomarker research. This will have widespread implications in translational tissue based research. Twit Text FOAVip Ultra-Fast Processing of Gigapixel Tissue MicroArray Images Using High Performance Computing ????Anal Cell Pathol (Amst) http://www.kidney.de/pget.php?&tw=1&pmid=21127378 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=21127378 #FOAvip English Wikipedia <ref>{{Cite pmid|21127378}}</ref> Ultra-Fast Processing of Gigapixel Tissue MicroArray Images Using High Performance Computing #MMPMID21127378 Wang Y; McCleary D; Wang CW; Kelly P; James J; Fennell DA; Hamilton P Anal Cell Pathol (Amst) 2010[]; 33 (5-6): 271-85 PMID21127378show ga Background: Tissue MicroArrays (TMAs) are a valuable platform for tissue based translational research and the discovery of tissue biomarkers. The digitised TMA slides or TMA Virtual Slides, are ultra-large digital images, and can contain several hundred samples. The processing of such slides is time-consuming, bottlenecking a potentially high throughput platform.Methods: A High Performance Computing (HPC) platform for the rapid analysis of TMA virtual slides is presented in this study. Using an HP high performance cluster and a centralised dynamic load balancing approach, the simultaneous analysis of multiple tissue-cores were established. This was evaluated on Non-Small Cell Lung Cancer TMAs for complex analysis of tissue pattern and immunohistochemical positivity.Results: The automated processing of a single TMA virtual slide containing 230 patient samples can be significantly speeded up by a factor of circa 22, bringing the analysis time to one minute. Over 90 TMAs could also be analysed simultaneously, speeding up multiplex biomarker experiments enormously.Conclusion: The methodologies developed in this paper provide for the first time a genuine high throughput analysis platform for TMA biomarker discovery that will significantly enhance the reliability and speed for biomarker research. This will have widespread implications in translational tissue based research. äUltra-Fast Processing of Gigapixel Tissue MicroArray Images Using High(epmc).pdf DeepDyve Pubget Overpricing