nils-herrmann/clean_scirepeval huggingface dataset:
3D reconstruction of 2D crystals from projections in real space

A new algorithm for 3D reconstruction of two-dimensional crystals from projections is presented, and applied to biological macromolecules imaged using electron microscopy. Its main departure from the traditional approach is that it works in real space, rather than in Fourier space, and is iterative. This approach has the advantage of making it convenient to introduce additional constraints (such as the support of the function to be reconstructed, which may be known from alternative measurements) and has the potential of more accurately modeling the electron microscope image formation process.
Clustering of biomedical scientific papers

In this paper we present a methodology for document clustering based on Non-negative Matrix Factorization (NMF) and ensemble clustering. Thanks to the ensemble clustering the algorithm is less prone to get into a local minimum caused by the initialization of the NMF. Despite the ensemble clustering, the algorithm keeps the semantic interpretability of the NMF and constructs a coocurrence matrix that allows the projection of the documents onto a two-dimensional space suitable for visualization. The algorithm is freely available for the information retrieval community from the Bioengineering Laboratory web page.
Flexible image registration for the identification of best fitted protein models in 3D-EM maps

In this work we propose an image registration algorithm to automatically fit protein atomic domain models into medium-resolution three-dimensional electron microscopy reconstructions (3D-EM map). The approach employs a flexible registration algorithm whose optimizer controls the generation of stereo-chemically correct models from a given reference domain belonging to a super-family of proteins. The proposed algorithm generates models automatically in the correct direction until the optimizer converges to the best fitted model. A local gradient optimizer is employed for this task. Mutual Information is used as an alternative to Cross Correlation Coefficient. An additional rigid registration that uses a local gradient optimization is applied between each generated model and the 3D-EM map.
Image processing for Cellular tomography using soft X-rays

Cellular tomography using X-rays is a novel technique that allows acquisition of structural information of complete cells, in their native environment without the need of sectioning or staining. To acquire three-dimensional information, samples must be tilted inside the microscope. This acquisition technique is shared with Electron Tomography (ET), and the acquired images share with ET the limitations on the maximum tilt and the alignment problems. However, the soft X-ray microscope differs from ET in the way it produces the image. First, image is formed mostly by amplitude contrast (attenuation of the incident beam); second, since the sample is much thicker, the microscope depth of focus is clearly visible along the tilt series. The image formation model for X-rays has recently been published by our group. In this paper, we present the main image processing steps needed from the tilt series acquisition to the final interpretation of the volume. In brief these steps are normalization, alignment, 3D reconstruction explicitly considering the depth of focus differences, denoising, and segmentation. We will also present Xmipp-TomoJ, a publicly available software tool allowing the execution of all these steps.
Coarse-Graining of Volumes for Modeling of Structure and Dynamics in Electron Microscopy: Algorithm to Automatically Control Accuracy of Approximation

Coarse-graining (or granularization) of structures from transmission electron microscopy (EM volumes) has been shown to be useful for a variety of structural analysis applications. Several methods perform coarse-graining of EM volumes using hard spheres or 3D Gaussian functions but they do not allow controlling automatically the volume approximation accuracy. To tackle this problem, we recently developed such a method. It is currently used by 3DEM Loupe web server and HEMNMA software to study macromolecular dynamics based on coarse-grained representations of EM volumes. In this paper, we give a detailed description of the implemented algorithm and fully analyze its performance, which was out of scope of our previous papers. The performance is analyzed in a controlled environment, in the context of studying structure and dynamics of macromolecular complexes. We show that this technique allows computing structures that are similar to atomic structures, by analyzing intermediate-resolution volumes. Additionally, we show that it allows sharpening of intermediate-resolution volumes. The full algorithm description allows its implementation in any other software package.
Image processing and 3-D reconstruction in electron microscopy

In this article, we have presented an overview of the different approaches for structural determination of biological specimens by EM. This compendium of strategies makes EM a powerful tool in structural biology, as it is able to derive structural information from specimens in the whole spectrum from the cellular to macromolecular domain. The integration of the information obtained by these different EM approaches with well-known light microscopy and high-resolution structural techniques, such as X-ray crystallography or NMR, is expected to provide a comprehensive multiscale description of the biological hierarchy. Therefore, EM plays a key role in structural biology, with unique potential to bridge the gap between cellular and molecular biology
Volumetric constraints in 3D tomography applied to electron microscopy

3D electron microscopy aims at the reconstruction of density volumes corresponding to the mass distribution of macromolecules imaged with an electron microscope. There are many factors limiting the resolution achievable when this technique is applied to biomolecules: microscope transfer function, molecule flexibility, lack of projections from certain directions, unknown angular distribution, image noise, etc. In this communication we propose the use of a priori information such as particle symmetry, occupied volume, known surface, density nonnegativity and similarity to a known volume in order to improve the quality of the reconstruction. When a series expansion of the reconstructed volume is done, all these constraints are expressed as a set of equations which the expansion coefficients must satisfy. In this work, this equation set is specified and the effect of each one on the reconstruction of a realistic phantom is explored.
Electric-potential reconstructions of single particles using L2-gradient flows

In this paper, we present a stable, reliable and robust method for reconstructing a three dimensional density function from a set of two dimensional electron microscopy images. By minimizing an energy functional consisting of a fidelity term and a regularization term, a L2-gradient flow is derived. The flow is integrated by a finite element method in the spatial direction and an explicit Euler scheme in temporal direction. The experimental results show that the proposed method is efficient and effective.
Molecular image registration using mutual information and differential evolution optimization

In this work we propose a novel rigid image registration approach to determine the position of high-resolution molecular structures in medium-resolution macromolecular complexes. Mutual information similarity measure is used as an alternative to the cross-correlation coefficient commonly applied in this context. The optimum of the objective function is sought by means of differential evolution algorithm. This global optimization technique yields robust registration, exhibits fast convergence and is easy to use. In order to additionally improve its accuracy we combine it with a local gradient search strategy. The registration framework is tested both on simulated and experimental data sets forcing large rotations and translations. Results in terms of success rate and execution time, indicate the suitability of the proposed approach
AMON: A Software System for Automatic Generation of Ontology Mappings

Some of the most outstanding problems in computer science (e. g. access to heterogeneous information sources, use of different e-commerce standards, ontology translation, etc.) are often approached through the identification of ontology mappings. A manual mapping generation slows down, or even makes unfeasible, the solution of particular cases of the aforementioned problems via ontology mappings. Some algorithms and formal models for partial tasks of automatic generation of mappings have been proposed. However, an integrated system to solve this problem is still missing. In this paper, we present AMON, a platform for automatic ontology mapping generation. First of all, we show the general structure. Then, we describe the current version of the system, including the ontology in which it is based, the similarity measures that it uses, the access to external sources, etc
An image processing approach to the simulation of electron microscopy volumes of atomic structures

The goal of this paper is to construct accurate three-dimensional models from atomic structures that can be compared to Electron Microscopy volumes. They, in their turn, can be used to solve fitting and docking problems as well as to generate ground-truth models for the development of image processing algorithms. We use Electron Atomic Scattering Factor (EASF) along with a careful filter design to greatly accelerate the current use of these functions and strongly reduce computer memory requirements. We show that our method is 3 orders of magnitude faster than existing implementations of EASFs in Fourier space as well as more accurate than their standard implementations. The algorithm is freely available in the software package Xmipp (http://xmipp.cnb.csic.es) .
Undergraduate Students Compete in the IEEE Signal Processing Cup: Part 2 [sp Education]

T his is the second part to , which summarized the first two editions of the IEEE Signal Processing Cup. The perspectives of the competition organizers and the student finalists are given in this column. TC)  forwarded an e-mail to the members of the committee calling for proposals for the IEEE Signal Processing Society's (SPS's) First Signal Processing Cup. The idea was to promote signal processing , particularly its applications to solve real-world problems, among undergraduate students. I work at the Instruct Image Processing Center  in Madrid, Spain, a reference center for image processing in structural biology. The call for proposals was a perfect match between the need to achieve higher resolution in the three-dimensional (3-D) reconstructions of biological macromole-cules and the possibility to explore the recently introduced ideas in the areas of superresolution, image restoration, and denoising. The possibility of having students all over the world trying to tackle this problem sounded attractive and challenging. So, with the support of the BISP TC, the Instruct Image Processing Center submitted a proposal for enhancing the resolution achieved by electron microscopes in the elucidation of the structure of single particles. Students could be as creative as they wished as long as the resolution limit imposed by the microscope could be pushed toward atomic resolution. In August 2013, the proposal was approved by the SPS and, in November, we already had training and testing data sets available on the Internet. We also set up an online system [4] that allowed participants to self-evaluate their performances: they only had to upload their results to a web page and the system returned immediate feedback about the quality of their 3-D reconstructions. This system has been most valuable to empower the challengers and let them explore multiple directions while pursuing the most promising algorithms. The website activity, as well as the interaction of students with the organizers of the challenge, was quite high in the following months. algoriThm FinaliSTS The objective of the challenge was to enhance the resolution of macromolecular structures reconstructed from images taken by an electron microscope (EM) . The best algorithm (Algorithm 1) consisted of three steps: 1) estimating the point spread function from the molecule background, 2) deconvolving with the Richardson–Lucy iterative algorithm, and 3) automatically detecting the location of the macromolecule and masking the output to produce a background-free volume. The second-best algorithm (Algorithm 2) …
Iterative reconstruction for pet scanners with continuous scintillators

Several technical developments have led to a comeback of the continuous scintillators in positron emission tomography (PET). Important differences exist between the resurgent continuous scintillators and the prevailing pixelated devices, which can translate into certain advantages of the former over the latter. However, if the peculiarities of the continuous scintillators are not considered in the iterative reconstruction in which the measured data is converted to images, these advantages will not be fully exploited. In this paper, we review which those peculiarities are and how they have been considered in the literature of PET reconstruction. In light of this review, we propose a new method to compute one of the key elements of the iterative schemes, the system matrix. Specifically, we substitute the traditional Gaussian approach to the so-called uncertainty term by a more general Monte Carlo estimation, and account for the effect of the optical photons, which cannot be neglected in continuous-scintillators devices. Finally, we gather in a single scheme all the elements of the iterative reconstruction that have been individually reformulated, in this or previous works, for continuous scintillators, providing the first reconstruction framework fully adapted to this type of detectors. The preliminary images obtained for a commercially available PET scanner show the benefits of adjusting the reconstruction to the nature of the scintillators.
3-D reconstruction of 2-D crystals in real space

A new algorithm for three-dimensional reconstruction of two-dimensional crystals from projections is presented, and its applicability to biological macromolecules imaged using transmission electron microscopy (TEM) is investigated. Its main departures from the traditional approach is that it works in real space, rather than in Fourier space, and it is iterative. This has the advantage of making it convenient to introduce additional constraints (such as the support of the function to be reconstructed, which may be known from alternative measurements) and has the potential of more accurately modeling the TEM image formation process. Phantom experiments indicate the superiority of the new approach even without the introduction of constraints in addition to the projection data.
Elastic image registration to fully explore macromolecular dynamics by electron microscopy

Structural changes are critical for biological functions of proteins and describing conformational changes in large macromolecular complexes is a major challenge. We have recently developed a hybrid method (HEMNMA) combining transmission electron microscopy (EM), normal mode analysis (NMA), and image analysis to study macromolecular dynamics. NMA is traditionally used to study macromolecular motions while HEMNMA provides insight into actual conformational changes seen by EM. HEMNMA uses normal modes to elastically align EM images with a reference structure in order to determine the conformations present in images and evaluate their pertinence. In this paper, we show how HEMNMA can be used with an atomic-resolution reference structure, using as an example the study of the conformational dynamics of Tomato Bushy Stunt Virus.
Effects of the downsampling scheme on three-dimensional electron microscopy of single particles

Electron microscopy of single particles aims at the elucidation of the three-dimensional structure of macromolecular complexes. This information is crucial for the more complete understanding of how these nanomachines perform their functions in the cell. Electron microscopy images are recorded in digital form at a resolution much higher than needed from the signal theory point of view. Then, they are downsampled to the required resolution. The electron microscopy community has paid little attention to this step assuming that even a trivial filter, as ”bining”, does not affect the posterior image processing steps. In this paper we show that this hypothesis is not correct.
Weakly Supervised Deep Metric Learning for Community-Contributed Image Retrieval

Recent years have witnessed the explosive growth of community-contributed images with rich context information, which is beneficial to the task of image retrieval. It can help us to learn a suitable metric to alleviate the semantic gap. In this paper, we propose a new distance metric learning algorithm, namely weakly-supervised deep metric learning (WDML), under the deep learning framework. It utilizes a progressive learning manner to discover knowledge by jointly exploiting the heterogeneous data structures from visual contents and user-provided tags of social images. The semantic structure in the textual space is expected to be well preserved while the problem of the noisy, incomplete or subjective tags is addressed by leveraging the visual structure in the original visual space. Besides, a sparse model with the l2,1 mixed norm is imposed on the transformation matrix of the first layer in the deep architecture to compress the noisy or redundant visual features. The proposed problem is formulated as an optimization problem with a well-defined objective function and a simple yet efficient iterative algorithm is proposed to solve it. Extensive experiments on real-world social image datasets are conducted to verify the effectiveness of the proposed method for image retrieval. Encouraging experimental results are achieved compared with several representative metric learning methods.
Unsupervised Video Summaries Using Multiple Features and Image Quality

It is important to generate both interesting and representative video summary for massive videos. This work proposes a new method to generate dynamic video summary using multiple features and image quality without human's involvement in the whole procedure. Specifically, we first split a video into several video clips. Second, a set of features including visual attention, exposure of light, saturation, hue, rule of thirds, contrast and directionality is computed and the qualities of video clips are also estimated. Then, the importance of each video clip is obtained based on these features and the estimated quality. Finally, based on the importance value, we sort clip values in descending order and select an optimal subset to generate video summary. Experimental results demonstrate that the proposed method enables to generate high-quality video summary.
Constructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features

This paper aims at constructing a good graph to discover the intrinsic data structures under a semisupervised learning setting. First, we propose to build a nonnegative low-rank and sparse (referred to as NNLRS) graph for the given data representation. In particular, the weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse reconstruction coefficients matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph captures both the global mixture of subspaces structure (by the low-rankness) and the locally linear structure (by the sparseness) of the data, hence it is both generative and discriminative. Second, as good features are extremely important for constructing a good graph, we propose to learn the data embedding matrix and construct the graph simultaneously within one framework, which is termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive NNLRS experiments on three publicly available data sets demonstrate that the proposed method outperforms the state-of-the-art graph construction method by a large margin for both semisupervised classification and discriminative analysis, which verifies the effectiveness of our proposed method.
Weakly-Supervised Dual Clustering for Image Semantic Segmentation

In this paper, we propose a novel Weakly-Supervised Dual Clustering (WSDC) approach for image semantic segmentation with image-level labels, i.e., collaboratively performing image segmentation and tag alignment with those regions. The proposed approach is motivated from the observation that super pixels belonging to an object class usually exist across multiple images and hence can be gathered via the idea of clustering. In WSDC, spectral clustering is adopted to cluster the super pixels obtained from a set of over-segmented images. At the same time, a linear transformation between features and labels as a kind of discriminative clustering is learned to select the discriminative features among different classes. The both clustering outputs should be consistent as much as possible. Besides, weakly-supervised constraints from image-level labels are imposed to restrict the labeling of super pixels. Finally, the non-convex and non-smooth objective function are efficiently optimized using an iterative CCCP procedure. Extensive experiments conducted on MSRC and Label Me datasets demonstrate the encouraging performance of our method in comparison with some state-of-the-arts.
Learning Discriminative Dictionary for Group Sparse Representation

In recent years, sparse representation has been widely used in object recognition applications. How to learn the dictionary is a key issue to sparse representation. A popular method is to use l1 norm as the sparsity measurement of representation coefficients for dictionary learning. However, the l1 norm treats each atom in the dictionary independently, so the learned dictionary cannot well capture the multisubspaces structural information of the data. In addition, the learned subdictionary for each class usually shares some common atoms, which weakens the discriminative ability of the reconstruction error of each subdictionary. This paper presents a new dictionary learning model to improve sparse representation for image classification, which targets at learning a class-specific subdictionary for each class and a common subdictionary shared by all classes. The model is composed of a discriminative fidelity, a weighted group sparse constraint, and a subdictionary incoherence term. The discriminative fidelity encourages each class-specific subdictionary to sparsely represent the samples in the corresponding class. The weighted group sparse constraint term aims at capturing the structural information of the data. The subdictionary incoherence term is to make all subdictionaries independent as much as possible. Because the common subdictionary represents features shared by all classes, we only use the reconstruction error of each class-specific subdictionary for classification. Extensive experiments are conducted on several public image databases, and the experimental results demonstrate the power of the proposed method, compared with the state-of-the-arts.