The Experts below are selected from a list of 114 Experts worldwide ranked by ideXlab platform
Eva Nogales - One of the best experts on this subject based on the ideXlab platform.
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the orthogonal tilt reconstruction method an approach to generating single class volumes with no missing cone for ab initio reconstruction of asymmetric particles
Journal of Structural Biology, 2006Co-Authors: Andres E Leschziner, Eva NogalesAbstract:Abstract Generating reliable initial models is a critical step in the reconstruction of asymmetric single-particles by 3D electron microscopy. This is particularly difficult to do if heterogeneity is present in the sample. The Random Conical Tilt (RCT) method, arguably the most robust presently to accomplish this task, requires significant user intervention to solve the “missing cone” problem. We present here a novel approach, termed the orthogonal tilt reconstruction method, that eliminates the missing cone altogether, making it possible for single-class volumes to be used directly as initial references in refinement without further processing. The method involves collecting Data at +45° and −45° tilts and only requires that particles adopt a relatively large number of orientations on the grid. One tilted Data Set is used for alignment and classification and the other Set—which provides views orthogonal to those in the first—is used for reconstruction, resulting in the absence of a missing cone. We have tested this method with synthetic Data and compared its performance to that of the RCT method. We also propose a way of increasing the level of homogeneity in individual 2D classes (and volumes) in a Heterogeneous Data Set and identifying the most homogeneous volumes.
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the orthogonal tilt reconstruction method an approach to generating single class volumes with no missing cone for ab initio reconstruction of asymmetric particles
Journal of Structural Biology, 2006Co-Authors: Andres E Leschziner, Eva NogalesAbstract:Generating reliable initial models is a critical step in the reconstruction of asymmetric single-particles by 3D electron microscopy. This is particularly difficult to do if heterogeneity is present in the sample. The Random Conical Tilt (RCT) method, arguably the most robust presently to accomplish this task, requires significant user intervention to solve the "missing cone" problem. We present here a novel approach, termed the orthogonal tilt reconstruction method, that eliminates the missing cone altogether, making it possible for single-class volumes to be used directly as initial references in refinement without further processing. The method involves collecting Data at +45 degrees and -45 degrees tilts and only requires that particles adopt a relatively large number of orientations on the grid. One tilted Data Set is used for alignment and classification and the other Set--which provides views orthogonal to those in the first--is used for reconstruction, resulting in the absence of a missing cone. We have tested this method with synthetic Data and compared its performance to that of the RCT method. We also propose a way of increasing the level of homogeneity in individual 2D classes (and volumes) in a Heterogeneous Data Set and identifying the most homogeneous volumes.
Andres E Leschziner - One of the best experts on this subject based on the ideXlab platform.
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the orthogonal tilt reconstruction method an approach to generating single class volumes with no missing cone for ab initio reconstruction of asymmetric particles
Journal of Structural Biology, 2006Co-Authors: Andres E Leschziner, Eva NogalesAbstract:Abstract Generating reliable initial models is a critical step in the reconstruction of asymmetric single-particles by 3D electron microscopy. This is particularly difficult to do if heterogeneity is present in the sample. The Random Conical Tilt (RCT) method, arguably the most robust presently to accomplish this task, requires significant user intervention to solve the “missing cone” problem. We present here a novel approach, termed the orthogonal tilt reconstruction method, that eliminates the missing cone altogether, making it possible for single-class volumes to be used directly as initial references in refinement without further processing. The method involves collecting Data at +45° and −45° tilts and only requires that particles adopt a relatively large number of orientations on the grid. One tilted Data Set is used for alignment and classification and the other Set—which provides views orthogonal to those in the first—is used for reconstruction, resulting in the absence of a missing cone. We have tested this method with synthetic Data and compared its performance to that of the RCT method. We also propose a way of increasing the level of homogeneity in individual 2D classes (and volumes) in a Heterogeneous Data Set and identifying the most homogeneous volumes.
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the orthogonal tilt reconstruction method an approach to generating single class volumes with no missing cone for ab initio reconstruction of asymmetric particles
Journal of Structural Biology, 2006Co-Authors: Andres E Leschziner, Eva NogalesAbstract:Generating reliable initial models is a critical step in the reconstruction of asymmetric single-particles by 3D electron microscopy. This is particularly difficult to do if heterogeneity is present in the sample. The Random Conical Tilt (RCT) method, arguably the most robust presently to accomplish this task, requires significant user intervention to solve the "missing cone" problem. We present here a novel approach, termed the orthogonal tilt reconstruction method, that eliminates the missing cone altogether, making it possible for single-class volumes to be used directly as initial references in refinement without further processing. The method involves collecting Data at +45 degrees and -45 degrees tilts and only requires that particles adopt a relatively large number of orientations on the grid. One tilted Data Set is used for alignment and classification and the other Set--which provides views orthogonal to those in the first--is used for reconstruction, resulting in the absence of a missing cone. We have tested this method with synthetic Data and compared its performance to that of the RCT method. We also propose a way of increasing the level of homogeneity in individual 2D classes (and volumes) in a Heterogeneous Data Set and identifying the most homogeneous volumes.
Carlos L Liesa - One of the best experts on this subject based on the ideXlab platform.
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reliability of methods to separate stress tensors from Heterogeneous fault slip Data
Journal of Structural Geology, 2004Co-Authors: Carlos L Liesa, Richard John LisleAbstract:The reliability of methods for separating palaeostress tensors from Heterogeneous fault-slip Data is evaluated. The methods of Etchecopar et al. (1981), Yamaji (2000), and the cluster procedure of Nemcok and Lisle (1995) are assessed but the results can probably be extrapolated to other methods based on similar assumptions. Heterogeneous fault-slip Data Sets, artificially generated by mixing two natural homogeneous Data Sets, have been used to evaluate both the role of the relative dominance (in number of faults taken from each tensor) and the difference between the parent tensors. The results obtained from a natural Heterogeneous Data Set were compared with additional field Data to evaluate and constrain the tensor separation process as well. Results suggest that attempts to devise a fully automatic separation procedure for distinguishing homogeneous Data Sets from Heterogeneous ones will be unsuccessful because the researcher will always be required to take some part in the correct choice of the tensors. In this sense, additional structural Data such as geometrical characteristics of the faults (e.g. conjugate or quasi-conjugate Andersonian systems), stylolites or tension gashes will be very useful for the correct separation of stress tensors from fault-slip Data.
Richard John Lisle - One of the best experts on this subject based on the ideXlab platform.
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reliability of methods to separate stress tensors from Heterogeneous fault slip Data
Journal of Structural Geology, 2004Co-Authors: Carlos L Liesa, Richard John LisleAbstract:The reliability of methods for separating palaeostress tensors from Heterogeneous fault-slip Data is evaluated. The methods of Etchecopar et al. (1981), Yamaji (2000), and the cluster procedure of Nemcok and Lisle (1995) are assessed but the results can probably be extrapolated to other methods based on similar assumptions. Heterogeneous fault-slip Data Sets, artificially generated by mixing two natural homogeneous Data Sets, have been used to evaluate both the role of the relative dominance (in number of faults taken from each tensor) and the difference between the parent tensors. The results obtained from a natural Heterogeneous Data Set were compared with additional field Data to evaluate and constrain the tensor separation process as well. Results suggest that attempts to devise a fully automatic separation procedure for distinguishing homogeneous Data Sets from Heterogeneous ones will be unsuccessful because the researcher will always be required to take some part in the correct choice of the tensors. In this sense, additional structural Data such as geometrical characteristics of the faults (e.g. conjugate or quasi-conjugate Andersonian systems), stylolites or tension gashes will be very useful for the correct separation of stress tensors from fault-slip Data.
P K Gupta - One of the best experts on this subject based on the ideXlab platform.
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hga a genetic algorithm method for direct estimation of paleostress states from Heterogeneous fault slip Data
Journal of Structural Geology, 2020Co-Authors: Prithvi Thakur, Deepak Srivastava, P K GuptaAbstract:Abstract Field Data on fault-slip observations is commonly Heterogeneous. Paleostress estimation from such Data Sets is, in general, carried out in two steps: (i) the classification of the Heterogeneous Data Set into homogeneous subSets and (ii) an inversion of each homogeneous subSet. This study gives a new approach, the HGA, that combines the two issues in a single step process and gives the stress tensors directly. The given Heterogeneous Data are directly operated upon by the genetic algorithm operators, initialization, elitism, selection, encoding, crossover and mutation. These operations simulate such a guided search that finds successively fitter solutions, the stress tensors, until the globally fittest solution is obtained. We first explain the basic steps of the algorithm on a working example and then demonstrate its veracity using several synthetic and two natural examples. The proposed genetic algorithm method obviates the necessity of having first to classify the Heterogeneous Data into homogeneous Sets. It directly estimates different stress states by inversion of the given Heterogeneous fault-slip Data. In contrast to the existing linear methods, the method is not vulnerable to entrapment of the solution in a local optimum. Although the method requires an a priori estimate of the maximum number of expected homogeneous Sets in a given population, this estimate does not control the final results. Like any other method, the genetic algorithm method too has its merits and limitations and these are discussed.