<?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><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%">Malladi, Ravi</style></author><author><style face="normal" font="default" size="100%">Baxter, Bill</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%">A binary segmentation approach for boxing ribosome particles in cryo EM micrographs.</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%">Anisotropy</style></keyword><keyword><style  face="normal" font="default" size="100%">Automatic Data Processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Cryoelectron Microscopy</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%">Particle Size</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Automated</style></keyword><keyword><style  face="normal" font="default" size="100%">Ribosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Software Design</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2004</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/15065681</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">145</style></volume><pages><style face="normal" font="default" size="100%">142-51</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;Three-dimensional reconstruction of ribosome particles from electron micrographs requires selection of many single-particle images. Roughly 100,000 particles are required to achieve approximately 10 A resolution. Manual selection of particles, by visual observation of the micrographs on a&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;computer&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;screen, is recognized as a bottleneck in automated single-particle reconstruction. This paper describes an efficient approach for automated boxing of ribosome particles in micrographs. Use of a fast, anisotropic non-linear reaction-diffusion method to pre-process micrographs and rank-leveling to enhance the contrast between particles and the background, followed by binary and morphological segmentation constitute the core of this technique. Modifying the shape of the particles to facilitate segmentation of individual particles within clusters and boxing the isolated particles is successfully attempted. Tests on a limited number of micrographs have shown that over 80% success is achieved in automatic particle picking.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1-2</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%">Baxter, Bill</style></author><author><style face="normal" font="default" size="100%">Davidenko, J M</style></author><author><style face="normal" font="default" size="100%">Loew, L M</style></author><author><style face="normal" font="default" size="100%">Wuskell, J P</style></author><author><style face="normal" font="default" size="100%">Jalife, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Technical features of a CCD video camera system to record cardiac fluorescence data.</style></title><secondary-title><style face="normal" font="default" size="100%">Ann Biomed Eng</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Ann Biomed Eng</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Action Potentials</style></keyword><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Body Surface Potential Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Calibration</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Electric Conductivity</style></keyword><keyword><style  face="normal" font="default" size="100%">Fluorescent Dyes</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Cardiovascular</style></keyword><keyword><style  face="normal" font="default" size="100%">Sheep</style></keyword><keyword><style  face="normal" font="default" size="100%">Ventricular Function</style></keyword><keyword><style  face="normal" font="default" size="100%">Video Recording</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/1997</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/9236983</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">713-25</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;A charge-coupled device (CCD) camera was used to acquire movies of transmembrane activity from thin slices of sheep ventricular&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;epicardial&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;muscle stained with a voltage-sensitive dye. Compared with photodiodes, CCDs have high spatial resolution, but low temporal resolution. Spatial resolution in our system ranged from 0.04 to 0.14 mm/pixel; the acquisition rate was 60, 120, or 240 frames/sec. Propagating waves were readily visualized after subtraction of a background image. The optical signal had an amplitude of 1 to 6 gray levels, with signal-to-noise ratios between 1.5 and 4.4. Because CCD cameras integrate light over the frame interval, moving objects, including propagating waves, are blurred in the resulting movies. A computer model of such an integrating&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;imaging&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;system was developed to study the effects of blur, noise, filtering, and quantization on the ability to measure conduction velocity and action potential duration (APD). The model indicated that blurring, filtering, and quantization do not affect the ability to localize wave fronts in the optical data (i.e., no systematic error in determining spatial position), but noise does increase the uncertainty of the measurements. The model also showed that the low frame rates of the CCD camera introduced a systematic error in the calculation of APD: for cutoff levels &amp;gt; 50%, the APD was erroneously long. Both noise and quantization increased the uncertainty in the APD measurements. The optical measures of conduction velocity were not significantly different from those measured simultaneously with microelectrodes. Optical APDs, however, were longer than the electrically recorded APDs. This APD error could be reduced by using the 50% cutoff level and the fastest frame rate possible.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record></records></xml>