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Conformations of Macromolecules and their Complexes from Heterogeneous Datasets  [PDF]
P. Schwander,R. Fung,A. Ourmazd
Quantitative Biology , 2014,
Abstract: We describe a new generation of algorithms capable of mapping the structure and conformations of macromolecules and their complexes from large ensembles of heterogeneous snapshots, and demonstrate the feasibility of determining both discrete and continuous macromolecular conformational spectra. These algorithms naturally incorporate conformational heterogeneity without resort to sorting and classification, or prior knowledge of the type of heterogeneity present. They are applicable to single-particle diffraction and image datasets produced by X-ray lasers and cryo-electron microscopy, respectively, and particularly suitable for systems not easily amenable to purification or crystallization.
The use of artificial structure as an initial reference model for projection-matching three dimensional reconstruction: visualization of macromolecular structure using cryo-electron microscopy  [cached]
Soo Jin Kim,Jong-Man Jeong,Chi Hyun Kim,Seong Oak Park
Journal of Analytical Science & Technology , 2010,
Abstract: Projection-matching single particle three dimensional reconstruction following to electron microscopy is the one of common methods to visualize three dimensional structures of target macromolecules or protein-protein interaction at medium or near-atomic resolution. In this report, we demonstrated the potentiality of using artificial structure as an initial reference model for its computational processing by describing an example experiment in which we obtained ~ 2000 single particle images from cryo-electron microscopy. The reconstruction using the model provided correct matching parameters of single particle experimental images with projection images of the model, ultimately resulting in generating informative three dimensional volume. Comparative analysis confirmed that the resultant volume has a remarkably similar structure to the proposed atomic model. These results provided suggestive evidence for using this technical approach as a useful analytical tool for determining macromolecular structure in the model independence way. In addition, the finding goal can be used for evaluating high resolution structure processed from the reference model of atomic structure since it is an ideal method to eliminate any possibilities of model-bias.
Methods for Partitioning Data to Improve Parallel Execution Time for Sorting on Heterogeneous Clusters  [PDF]
Christophe Cérin,Jean-Christophe Dubacq,Jean-Louis Roch,the SafeScale Collaboration
Computer Science , 2006,
Abstract: The aim of the paper is to introduce general techniques in order to optimize the parallel execution time of sorting on a distributed architectures with processors of various speeds. Such an application requires a partitioning step. For uniformly related processors (processors speeds are related by a constant factor), we develop a constant time technique for mastering processor load and execution time in an heterogeneous environment and also a technique to deal with unknown cost functions. For non uniformly related processors, we use a technique based on dynamic programming. Most of the time, the solutions are in O(p) (p is the number of processors), independent of the problem size n. Consequently, there is a small overhead regarding the problem we deal with but it is inherently limited by the knowing of time complexity of the portion of code following the partitioning.
Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes  [PDF]
Takashi Takekawa,Tomoki Fukai
Frontiers in Neuroinformatics , 2012, DOI: 10.3389/fninf.2012.00005
Abstract: This study introduces a new spike sorting method that classifies spike waveforms from multiunit recordings into spike trains of individual neurons. In particular, we develop a method to sort a spike mixture generated by a heterogeneous neural population. Such a spike sorting has a significant practical value, but was previously difficult. The method combines a feature extraction method, which we may term “multimodality-weighted principal component analysis” (mPCA), and a clustering method by variational Bayes for Student's t mixture model (SVB). The performance of the proposed method was compared with that of other conventional methods for simulated and experimental data sets. We found that the mPCA efficiently extracts highly informative features as clusters clearly separable in a relatively low-dimensional feature space. The SVB was implemented explicitly without relying on Maximum-A-Posterior (MAP) inference for the “degree of freedom” parameters. The explicit SVB is faster than the conventional SVB derived with MAP inference and works more reliably over various data sets that include spiking patterns difficult to sort. For instance, spikes of a single bursting neuron may be separated incorrectly into multiple clusters, whereas those of a sparsely firing neuron tend to be merged into clusters for other neurons. Our method showed significantly improved performance in spike sorting of these “difficult” neurons. A parallelized implementation of the proposed algorithm (EToS version 3) is available as open-source code at http://etos.sourceforge.net/.
Limiting factors in single particle cryo electron tomography  [cached]
Mikhail Kudryashev,Daniel Casta?o-Díez,Henning Stahlberg
Computational and Structural Biotechnology Journal , 2012,
Abstract: Modern methods of cryo electron microscopy and tomography allow visualization of protein nanomachines in their native state at the nanometer scale. Image processing methods including sub-volume averaging applied to repeating macromolecular elements within tomograms allow exploring their structures within the native context of the cell, avoiding the need for protein isolation and purification. Today, many different data acquisition protocols and software solutions are available to researchers to determine average structures of macromolecular complexes and potentially to classify structural intermediates. Here, we list the density maps reported in the literature, and analyze each structure for the chosen instrumental settings, sample conditions, main processing steps, and obtained resolution. We present conclusions that identify factors currently limiting the resolution gained by this approach.
Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data  [PDF]
Laura Anderlucci,Cinzia Viroli
Statistics , 2014, DOI: 10.1214/15-AOAS816
Abstract: We propose a novel approach for modeling multivariate longitudinal data in the presence of unobserved heterogeneity for the analysis of the Health and Retirement Study (HRS) data. Our proposal can be cast within the framework of linear mixed models with discrete individual random intercepts; however, differently from the standard formulation, the proposed Covariance Pattern Mixture Model (CPMM) does not require the usual local independence assumption. The model is thus able to simultaneously model the heterogeneity, the association among the responses and the temporal dependence structure. We focus on the investigation of temporal patterns related to the cognitive functioning in retired American respondents. In particular, we aim to understand whether it can be affected by some individual socio-economical characteristics and whether it is possible to identify some homogenous groups of respondents that share a similar cognitive profile. An accurate description of the detected groups allows government policy interventions to be opportunely addressed. Results identify three homogenous clusters of individuals with specific cognitive functioning, consistent with the class conditional distribution of the covariates. The flexibility of CPMM allows for a different contribution of each regressor on the responses according to group membership. In so doing, the identified groups receive a global and accurate phenomenological characterization.
Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare  [PDF]
Florence Duchene,Catherine Garbay,Vincent Rialle
Computer Science , 2004,
Abstract: For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home.

HUANG Guang-lin,FENG Yu-ding,HE Jian-ye,

高分子学报 , 1992,
Abstract: Cryo-hydrogel-rubbery elastomer, containing above 85% water was prepared by freezing aqueous solution of poly (vinyl alcohol) (PVA). Structure of cryo-PVA-hydrogel was studied by TEM. Experimental results showed that the cryo-hydrogel was a non-covalent bonded gel formed due to linking of polymer "clusters".Electron microscopy demonstrates the heterogeneous porous structure of the cryogel. It was found that the porous structure become more and more perfect with increasing the freezing time and polymer concentration. It is considered that phase separation plays an important role in gelation. The freezing is to encourage the phase separation and reinforce the mechanical strength.The results of DSC are in accordance with the results above.
Capturing RNA-dependent pathways for cryo-EM analysis  [cached]
Justin R Tanner,Katherine Degen,Brian L Gilmore,Deborah F Kelly
Computational and Structural Biotechnology Journal , 2012,
Abstract: Cryo-Electron Microscopy (EM) is a powerful technique to visualize biological processes at nanometer resolution. Structural studies of macromolecular assemblies are typically performed on individual complexes that are biochemically isolated from their cellular context. Here we present a molecular imaging platform to capture and view multiple components of cellular pathways within a functionally relevant framework. We utilized the bacterial protein synthesis machinery as a model system to develop our approach. By using modified Affinity Grid surfaces, we were able to recruit multiple protein assemblies bound to nascent strands of mRNA. The combined use of Affinity Capture technology and single particle electron microscopy provide the basis for visualizing RNA-dependent pathways in a remarkable new way.
Sorting in Lattices  [PDF]
Jens Gerlach
Computer Science , 2013,
Abstract: In a totally ordered set the notion of sorting a finite sequence is defined through a suitable permutation of the sequence's indices. In this paper we prove a simple formula that explicitly describes how the elements of a sequence are related to those of its sorted counterpart. As this formula relies only on the minimum and maximum functions we use it to define the notion of sorting for lattices. A major difference of sorting in lattices is that it does not guarantee that sequence elements are only rearranged. However, we can show that other fundamental properties that are associated with sorting are preserved.
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