ALBERT

Description: Motivation: Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. Achieving high-resolution structural details with electron microscopy requires the identification of a large number (up to hundreds of thousands) of single particles from electron micrographs, which is a laborious task if it has to be manually done and constitutes a hurdle towards high-throughput. Automatic particle selection in micrographs is far from being settled and new and more robust algorithms are required to reduce the number of false positives and false negatives. Results: In this article, we introduce an automatic particle picker that learns from the user the kind of particles he is interested in. Particle candidates are quickly and robustly classified as particles or non-particles. A number of new discriminative shape-related features as well as some statistical description of the image grey intensities are used to train two support vector machine classifiers. Experimental results demonstrate that the proposed method: (i) has a considerably low computational complexity and (ii) provides results better or comparable with previously reported methods at a fraction of their computing time. Availability: The algorithm is fully implemented in the open-source Xmipp package and downloadable from http://xmipp.cnb.csic.es . Contact: vabrishami@cnb.csic.es or coss@cnb.csic.es Supplementary Information: Supplementary data are available at Bioinformatics online.

Description: Analysis of more than 10 yr of vertical magnetic transfer function (VTF) estimates obtained at 12 mid-latitude sites, located in different continents and tectonic settings, reveals significant temporal variations for a period range between approximately 250 and 2000 s. The most ubiquitous pattern is a seasonal modulation of the VTF element that relates the vertical to the horizontal north–south magnetic components ( Tx ), which shows a high peak around the June solstice (and a low peak around the December solstice) regardless of the location of the site. To quantify the influence of this source effect on the amplitude of VTFs, we modelled the temporal variations of VTFs using a function with dependence on season and magnetic activity indexes. The model shows that differences between VTF estimates obtained at seasonal peaks can reach 0.08 of Tx absolute values and that the effect increases with latitude and period. Seasonal variations are observed also in the VTF component relating vertical to horizontal east–west magnetic components ( Ty ), but here the pattern with respect to the geographic distribution is less clear. In addition to seasonal trends, we observe long-term modulations correlating with the 11-yr solar cycle at some sites. The influence of these external source effects should be taken into account, before attempting a geological interpretation of the VTFs. It can be misleading, for example, to combine or compare VTFs obtained from long-period geomagnetic data acquired at different seasons or years. An effective method to estimate and remove these source effects from VTFs is by comparison with temporal variations of VTFs from synchronously recorded data at sites located at similar latitude (〈5° of difference) and longitude (〈10° of difference). Source effects in temporal variations of VTFs can be identified as those patterns that exhibit similar amplitudes and significant correlation with the geomagnetic activity at all compared sites. We also provide a second-order polynomial which can be used to estimate the amplitude of the seasonal variations in the Tx component globally as a function of latitude.

Description: Motivation: Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. The reconstruction process leading to the final 3D map requires an approximate initial model. Generation of an initial model is still an open and challenging problem in single-particle analysis. Results: We present a fast and efficient approach to obtain a reliable, low-resolution estimation of the 3D structure of a macromolecule, without any a priori knowledge, addressing the well-known issue of initial volume estimation in the field of single-particle analysis. The input of the algorithm is a set of class average images obtained from individual projections of a biological object at random and unknown orientations by transmission electron microscopy micrographs. The proposed method is based on an initial non-lineal dimensionality reduction approach, which allows to automatically selecting representative small sets of class average images capturing the most of the structural information of the particle under study. These reduced sets are then used to generate volumes from random orientation assignments. The best volume is determined from these guesses using a random sample consensus (RANSAC) approach. We have tested our proposed algorithm, which we will term 3D-RANSAC, with simulated and experimental data, obtaining satisfactory results under the low signal-to-noise conditions typical of cryo-electron microscopy. Availability: The algorithm is freely available as part of the Xmipp 3.1 package [ http://xmipp.cnb.csic.es ]. Contact: jvargas@cnb.csic.es Supplementary information: Supplementary data are available at Bioinformatics online.