Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1093/bioinformatics/bty241 http://scihub22266oqcxt.onion/10.1093/bioinformatics/bty241 C6022599!6022599 !29950012     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\29950012 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Bioinformatics 2018 ; 34 (13 ): i284-i294 Nephropedia Template TP {{tp|p=29950012 |t=2018. DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy |pdf=|usr=}}{{29950012 }} gab.com Text DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29950012 #FOAvip MOTIVATION: Super-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures. RESULTS: Here, we propose a novel deep learning guided Bayesian inference (DLBI) approach, for the time-series analysis of high-density fluorescent images. Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image. In particular, our method contains three main components. The first one is a simulator that takes a high-resolution image as the input, and simulates time-series low-resolution fluorescent images based on experimentally calibrated parameters, which provides supervised training data to the deep learning model. The second one is a multi-scale deep learning module to capture both spatial information in each input low-resolution image as well as temporal information among the time-series images. And the third one is a Bayesian inference module that takes the image from the deep learning module as the initial localization of fluorophores and removes artifacts by statistical inference. Comprehensive experimental results on both real and simulated datasets demonstrate that our method provides more accurate and realistic local patch and large-field reconstruction than the state-of-the-art method, the 3B analysis, while our method is more than two orders of magnitude faster. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://github.com/lykaust15/DLBI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Twit Text FOAVip DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29950012 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29950012 #FOAvip English Wikipedia <ref>{{Cite pmid|29950012 }}</ref> DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy #MMPMID29950012 Li Y ; Xu F ; Zhang F ; Xu P ; Zhang M ; Fan M ; Li L ; Gao X ; Han R Bioinformatics 2018[Jul]; 34 (13 ): i284-i294 PMID29950012 show ga MOTIVATION: Super-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures. RESULTS: Here, we propose a novel deep learning guided Bayesian inference (DLBI) approach, for the time-series analysis of high-density fluorescent images. Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image. In particular, our method contains three main components. The first one is a simulator that takes a high-resolution image as the input, and simulates time-series low-resolution fluorescent images based on experimentally calibrated parameters, which provides supervised training data to the deep learning model. The second one is a multi-scale deep learning module to capture both spatial information in each input low-resolution image as well as temporal information among the time-series images. And the third one is a Bayesian inference module that takes the image from the deep learning module as the initial localization of fluorophores and removes artifacts by statistical inference. Comprehensive experimental results on both real and simulated datasets demonstrate that our method provides more accurate and realistic local patch and large-field reconstruction than the state-of-the-art method, the 3B analysis, while our method is more than two orders of magnitude faster. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://github.com/lykaust15/DLBI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |*Deep Learning [MESH] |*Microscopy, Fluorescence [MESH] |*Software [MESH] |Bayes Theorem [MESH] |Cells/ultrastructure [MESH]DLBI: deep learning guided Bayesian inference for structure reconstruc(epmc).pdf DeepDyve Pubget Overpricing