scholarly journals Amyloid structure determination in RELION-3.1

2019 ◽  
Author(s):  
Sjors H.W. Scheres
Keyword(s):  
Open Source ◽  
Structure Determination ◽  
Energy Landscape ◽  
Global Optimum ◽  
New Method ◽  
Data Set ◽  
Local Optima ◽  
Atomic Structures ◽  
Helical Reconstruction ◽  
The Brain

Helical reconstruction in RELION is increasingly used to determine atomic structures of amyloid filaments from electron cryo-microscopy (cryo-EM) images. However, because the energy landscape of amyloid refinements is typically fraught with local optima, amyloid structure determination is often difficult. This paper aims to help RELION users in this process. It discusses aspects of helical reconstruction that are specific to amyloids; it illustrates the problem of local optima in refinement and how to detect these; and it introduces a new method to calculate 3D initial models from reference-free 2D class averages. By providing starting models that are closer to the global optimum, this method makes amyloid structure determination easier. All methods described are open-source and distributed within RELION-3.1. Their use is illustrated using a publicly available data set on tau filaments from the brain of an individual with Alzheimer’s disease.

scholarly journals Amyloid structure determination in RELION-3.1

2020 ◽  
Vol 76 (2) ◽  
pp. 94-101 ◽  
Author(s):  
Sjors H. W. Scheres
Keyword(s):  
Open Source ◽  
Structure Determination ◽  
Energy Landscape ◽  
Global Optimum ◽  
New Method ◽  
Data Set ◽  
Local Optima ◽  
Atomic Structures ◽  
Helical Reconstruction ◽  
The Brain

Helical reconstruction in RELION is increasingly being used to determine the atomic structures of amyloid filaments from electron cryo-microscopy (cryo-EM) images. However, because the energy landscape of amyloid refinements is typically fraught with local optima, amyloid structure determination is often difficult. This paper aims to help RELION users in this process. It discusses aspects of helical reconstruction that are particularly relevant to amyloids, it illustrates the problem of local optima in refinement and how to detect them, and it introduces a new method to calculate 3D initial models from reference-free 2D class averages. By providing starting models that are closer to the global optimum, this method makes amyloid structure determination easier. All methods described are open-source and distributed within RELION-3.1. Their use is illustrated using a publicly available data set on tau filaments from the brain of an individual with Alzheimer's disease.

The Neuronal Replicator Hypothesis

2010 ◽  
Vol 22 (11) ◽  
pp. 2809-2857 ◽  
Author(s):  
Chrisantha Fernando ◽  
Richard Goldstein ◽  
Eörs Szathmáry
Keyword(s):  
Evolutionary Algorithms ◽  
Experimental Validation ◽  
Hebbian Learning ◽  
Global Optimum ◽  
Cognitive Architectures ◽  
Local Optima ◽  
The Past ◽  
Single Solution ◽  
The Stability ◽  
The Brain

We propose that replication (with mutation) of patterns of neuronal activity can occur within the brain using known neurophysiological processes. Thereby evolutionary algorithms implemented by neuro- nal circuits can play a role in cognition. Replication of structured neuronal representations is assumed in several cognitive architectures. Replicators overcome some limitations of selectionist models of neuronal search. Hebbian learning is combined with replication to structure exploration on the basis of associations learned in the past. Neuromodulatory gating of sets of bistable neurons allows patterns of activation to be copied with mutation. If the probability of copying a set is related to the utility of that set, then an evolutionary algorithm can be implemented at rapid timescales in the brain. Populations of neuronal replicators can undertake a more rapid and stable search than can be achieved by serial modification of a single solution. Hebbian learning added to neuronal replication allows a powerful structuring of variability capable of learning the location of a global optimum from multiple previously visited local optima. Replication of solutions can solve the problem of catastrophic forgetting in the stability-plasticity dilemma. In short, neuronal replication is essential to explain several features of flexible cognition. Predictions are made for the experimental validation of the neuronal replicator hypothesis.

scholarly journals Single-particle cryo-EM structures from iDPC-STEM at near-atomic resolution

2021 ◽  
Author(s):  
Ivan Lazic ◽  
Maarten Wirix ◽  
Max Leo Leidl ◽  
Felix de Haas ◽  
Maximilian Beckers ◽  
...  
Keyword(s):  
High Resolution ◽  
Structure Determination ◽  
Single Particle ◽  
3D Structure ◽  
Atomic Resolution ◽  
Biological Macromolecules ◽  
Data Set ◽  
Helical Reconstruction ◽  
Transmission Electron ◽  
Scanning Transmission

Electron cryo-microscopy (cryo-EM) is becoming one of the routine tools for structure determination of biological macromolecules. Commonly, molecular images are obtained by conventional transmission electron microcopy (CTEM) using underfocus and subsequently computationally combined into a high-resolution 3D structure. Here, we apply scanning transmission electron microscopy (STEM) using the integrated differential phase contrast mode also known as iDPC-STEM to the cryo-EM test specimen of tobacco mosaic virus (TMV). The micrographs show complete contrast transfer to high resolution and enable the cryo-EM structure determination at 3.5 Angstrom resolution using single-particle based helical reconstruction. A series of cryo-EM TMV maps was resolved at near-atomic resolution taken at different convergence semi-angle (CSA) beams and share identical features with maps obtained by CTEM of a previously acquired same-sized TMV data set. The associated map B-factors from iDPC-STEM match those obtained by CTEM recordings using 2nd generation direct electron detection devices. These data show that STEM imaging in general, and in particular the iDPC-STEM approach, can be applied to vitrified single-particle specimens to determine near-atomic resolution cryo-EM structures of biological macromolecules.

A New Approach for the Analysis of Mixture Toxicity Data

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2345-2348 ◽  
Author(s):  
C. N. Haas
Keyword(s):  
Quantitative Analysis ◽  
Mixture Toxicity ◽  
New Method ◽  
Metal Exposure ◽  
Positive Interactions ◽  
Toxicity Data ◽  
Data Set ◽  
New Approach ◽  
Negative Interactions

A new method for the quantitative analysis of multiple toxicity data is described and illustrated using a data set on metal exposure to copepods. Positive interactions are observed for Ni-Pb and Pb-Cr, with weak negative interactions observed for Ni-Cr.

scholarly journals A New Method for Landmark-Based Studies of the Dynamic Stability of Growth, with Implications for Evolutionary Analyses

2021 ◽  
Author(s):  
Fred L. Bookstein
Keyword(s):  
Growth Regulation ◽  
Pattern Analysis ◽  
Developmental Time ◽  
Dynamical Stability ◽  
New Method ◽  
Data Sets ◽  
Data Set ◽  
Landmark Data ◽  
Study Designs ◽  
Changes Over Time

AbstractA matrix manipulation new to the quantitative study of develomental stability reveals unexpected morphometric patterns in a classic data set of landmark-based calvarial growth. There are implications for evolutionary studies. Among organismal biology’s fundamental postulates is the assumption that most aspects of any higher animal’s growth trajectories are dynamically stable, resilient against the types of small but functionally pertinent transient perturbations that may have originated in genotype, morphogenesis, or ecophenotypy. We need an operationalization of this axiom for landmark data sets arising from longitudinal data designs. The present paper introduces a multivariate approach toward that goal: a method for identification and interpretation of patterns of dynamical stability in longitudinally collected landmark data. The new method is based in an application of eigenanalysis unfamiliar to most organismal biologists: analysis of a covariance matrix of Boas coordinates (Procrustes coordinates without the size standardization) against their changes over time. These eigenanalyses may yield complex eigenvalues and eigenvectors (terms involving $$i=\sqrt{-1}$$ i = - 1 ); the paper carefully explains how these are to be scattered, gridded, and interpreted by their real and imaginary canonical vectors. For the Vilmann neurocranial octagons, the classic morphometric data set used as the running example here, there result new empirical findings that offer a pattern analysis of the ways perturbations of growth are attenuated or otherwise modified over the course of developmental time. The main finding, dominance of a generalized version of dynamical stability (negative autoregressions, as announced by the negative real parts of their eigenvalues, often combined with shearing and rotation in a helpful canonical plane), is surprising in its strength and consistency. A closing discussion explores some implications of this novel pattern analysis of growth regulation. It differs in many respects from the usual way covariance matrices are wielded in geometric morphometrics, differences relevant to a variety of study designs for comparisons of development across species.

A Novel K-Harmonic Means Clustering Based on Multiple Initial Centers

2013 ◽  
Vol 321-324 ◽  
pp. 1947-1950
Author(s):  
Lei Gu ◽  
Xian Ling Lu
Keyword(s):  
New Method ◽  
Final Solution ◽  
Data Set ◽  
Number Of Clusters ◽  
Multiple Groups ◽  
Harmonic Means

In the initialization of the traditional k-harmonic means clustering, the initial centers are generated randomly and its number is equal to the number of clusters. Although the k-harmonic means clustering is insensitive to the initial centers, this initialization method cannot improve clustering performance. In this paper, a novel k-harmonic means clustering based on multiple initial centers is proposed. The number of the initial centers is more than the number of clusters in this new method. The new method with multiple initial centers can divide the whole data set into multiple groups and combine these groups into the final solution. Experiments show that the presented algorithm can increase the better clustering accuracies than the traditional k-means and k-harmonic methods.

Structure Determination of the Nisi2(111) Surface Using Medium Energy Ion Scattering with Monolayer Resolution

1990 ◽  
Vol 202 ◽  
Author(s):  
J. Vrijmoeth ◽  
P.M. Zagwijn ◽  
J.W.M. Frenken ◽  
J.F. van der Veen
Keyword(s):  
Surface Structure ◽  
Structure Determination ◽  
New Method ◽  
Medium Energy ◽  
Ion Scattering ◽  
Triple Layer ◽  
Medium Energy Ion Scattering

ABSTRACTThe surface structure of epitaxial NiSi2 films grown on Si (111) has been determined using a new method. The backscattering signals from subsequent Ni layers in the NiSi2 (111) surface are resolved.The topology of the NiSi2 (111) surface is concluded to be bulklike, i.e., it is terminated by a Si – Ni – Si triple layer.

A Comparative Study of Genetic Algorithms and Gradient Methods for RM12 Turbofan Engine Diagnostics and Performance Estimation

2004 ◽  
Author(s):  
Tomas Gro¨nstedt ◽  
Markus Wallin
Keyword(s):  
Gas Turbine ◽  
Performance Test ◽  
Gradient Methods ◽  
Performance Testing ◽  
Performance Estimation ◽  
Data Sets ◽  
Data Set ◽  
Turbofan Engine ◽  
Local Optima ◽  
Bypass Ratio

Recent work on gas turbine diagnostics based on optimisation techniques advocates two different approaches: 1) Stochastic optimisation, including Genetic Algorithm techniques, for its robustness when optimising objective functions with many local optima and 2) Gradient based methods mainly for their computational efficiency. For smooth and single optimum functions, gradient methods are known to provide superior numerical performance. This paper addresses the key issue for method selection, i.e. whether multiple local optima may occur when the optimisation approach is applied to real engine testing. Two performance test data sets for the RM12 low bypass ratio turbofan engine, powering the Swedish Fighter Gripen, have been analysed. One set of data was recorded during performance testing of a highly degraded engine. This engine has been subjected to Accelerated Mission Testing (AMT) cycles corresponding to more than 4000 hours of run time. The other data set was recorded for a development engine with less than 200 hours of operation. The search for multiple optima was performed starting from more than 100 extreme points. Not a single case of multi-modality was encountered, i.e. one unique solution for each of the two data sets was consistently obtained. The RM12 engine cycle is typical for a modern fighter engine, implying that the obtained results can be transferred to, at least, most low bypass ratio turbofan engines. The paper goes on to describe the numerical difficulties that had to be resolved to obtain efficient and robust performance by the gradient solvers. Ill conditioning and noise may, as illustrated on a model problem, introduce local optima without a correspondence in the gas turbine physics. Numerical methods exploiting the special problem structure represented by a non-linear least squares formulation is given special attention. Finally, a mixed norm allowing for both robustness and numerical efficiency is suggested.

Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication

2021 ◽  
pp. 1-8
Author(s):  
Yi-Bin Xi ◽  
Xu-Sha Wu ◽  
Long-Biao Cui ◽  
Li-Jun Bai ◽  
Shuo-Qiu Gan ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
First Episode ◽  
Age Difference ◽  
Healthy Controls ◽  
Data Set ◽  
Brain Ageing ◽  
First Episode Schizophrenia ◽  
Brain Age ◽  
The Brain ◽  
Age Prediction

Background Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear. Aims To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age. Method The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed. Results The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014). Conclusions The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

2021 ◽  
Vol 12 (4) ◽  
pp. 98-116
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Local Optima ◽  
Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

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