<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shaikh, Tanvir R</style></author><author><style face="normal" font="default" size="100%">Trujillo, Ramon</style></author><author><style face="normal" font="default" size="100%">LeBarron, Jamie</style></author><author><style face="normal" font="default" size="100%">Baxter, Bill</style></author><author><style face="normal" font="default" size="100%">Frank, Joachim</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification.</style></title><secondary-title><style face="normal" font="default" size="100%">J Struct Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Struct. Biol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Enhancement</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Microscopy, Electron</style></keyword><keyword><style  face="normal" font="default" size="100%">Multivariate Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Ribosomes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/18619547</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">164</style></volume><pages><style face="normal" font="default" size="100%">41-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;As collection of electron microscopy data for single-particle reconstruction becomes more efficient, due to electronic image capture, one of the principal limiting steps in a reconstruction remains particle-verification, which is especially costly in terms of user input. Recently, some algorithms have been developed to window particles automatically, but the resulting particle sets typically need to be verified manually. Here we describe a procedure to speed up verification of windowed particles using multivariate data analysis and classification. In this procedure, the particle set is subjected to multi-reference alignment before the verification. The aligned particles are first binned according to orientation and are binned further by K-means classification. Rather than selection of particles individually, an entire class of particles can be selected, with an option to remove outliers. Since particles in the same class present the same view, distinction between good and bad images becomes more straightforward. We have also developed a graphical interface, written in Python/Tkinter, to facilitate this implementation of particle-verification. For the demonstration of the particle-verification scheme presented here, electron micrographs of ribosomes are used.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adiga, Umesh</style></author><author><style face="normal" font="default" size="100%">Baxter, Bill</style></author><author><style face="normal" font="default" size="100%">Hall, Richard J</style></author><author><style face="normal" font="default" size="100%">Rockel, Beate</style></author><author><style face="normal" font="default" size="100%">Rath, Bimal K</style></author><author><style face="normal" font="default" size="100%">Frank, Joachim</style></author><author><style face="normal" font="default" size="100%">Glaeser, Robert M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Particle picking by segmentation: a comparative study with SPIDER-based manual particle picking.</style></title><secondary-title><style face="normal" font="default" size="100%">J Struct Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Struct. Biol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Aminopeptidases</style></keyword><keyword><style  face="normal" font="default" size="100%">Cryoelectron Microscopy</style></keyword><keyword><style  face="normal" font="default" size="100%">Dipeptidyl-Peptidases and Tripeptidyl-Peptidases</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Imaging, Three-Dimensional</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Particle Size</style></keyword><keyword><style  face="normal" font="default" size="100%">Ribosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Serine Endopeptidases</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Software Validation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2005</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/16330229</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">152</style></volume><pages><style face="normal" font="default" size="100%">211-20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Boxing hundreds of thousands of particles in low-dose electron micrographs is one of the major bottle-necks in advancing toward achieving atomic resolution reconstructions of biological macromolecules. We have shown that a combination of pre-processing operations and segmentation can be used as an effective, automatic tool for identifying and boxing single-particle images. This paper provides a brief description of how this method has been applied to a large data set of micrographs of ice-embedded ribosomes, including a comparative analysis of the efficiency of the method. Some results on processing micrographs of tripeptidyl peptidase II particles are also shown. In both cases, we have achieved our goal of selecting at least 80% of the particles that an expert would select with less than 10% false positives.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>