scholarly journals New tools for automated cryo-EM single-particle analysis in RELION-4.0

2021 ◽  
Author(s):  
Dari Kimanius ◽  
Liyi Dong ◽  
Grigory Sharov ◽  
Takanori Nakane ◽  
Sjors H.W. Scheres
Keyword(s):  
Structure Determination ◽  
Gradient Descent ◽  
Particle Analysis ◽  
Data Sets ◽  
Variable Metric ◽  
Gradient Descent Algorithm ◽  
Potential Applications ◽  
Flexible Framework ◽  
Robust Procedures ◽  
Selection Of

We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in the fourth major release of the RELION software. In particular, we introduce VDAM, a Variable-metric gradient Descent algorithm with Adaptive Moments estimation, for image refinement; a convolutional neural network for unsupervised selection of 2D classes; and a flexible framework for the design and execution of multiple jobs in pre-defined workflows. In addition, we present a stand-alone utility called MDCatch that links the execution of jobs within this framework with metadata gathering during microscope data acquisition. The new tools are aimed at providing fast and robust procedures for unsupervised cryo-EM structure determination, with potential applications for on-the-fly processing and the development of flexible, high-throughput structure determination pipelines. We illustrate their potential on twelve publicly available cryo-EM data sets.

scholarly journals New tools for automated cryo-EM single-particle analysis in RELION-4.0

2021 ◽  
Author(s):  
Dari Kimanius ◽  
Liyi Dong ◽  
Grigory Sharov ◽  
Takanori Nakane ◽  
Sjors H.W. Scheres
Keyword(s):  
Structure Determination ◽  
Gradient Descent ◽  
Particle Analysis ◽  
Data Sets ◽  
Variable Metric ◽  
Gradient Descent Algorithm ◽  
Potential Applications ◽  
Flexible Framework ◽  
Robust Procedures ◽  
Selection Of

We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in the fourth major release of the RELION software. In particular, we introduce VDAM, a Variable-metric gradient Descent algorithm with Adaptive Moments estimation, for image refinement; a convolutional neural network for unsupervised selection of 2D classes; and a flexible framework for the design and execution of multiple jobs in pre-defined workflows. In addition, we present a stand-alone utility called MDCatch that links the execution of jobs within this framework with metadata gathering during microscope data acquisition. The new tools are aimed at providing fast and robust procedures for unsupervised cryo-EM structure determination, with potential applications for on-the-fly processing and the development of flexible, high-throughput structure determination pipelines. We illustrate their potential on twelve publicly available cryo-EM data sets.

scholarly journals A Self-Supervised Workflow for Particle Picking in Cryo-EM

2020 ◽  
Author(s):  
Donal M. McSweeney ◽  
Sean M. McSweeney ◽  
Qun Liu
Keyword(s):  
Data Analysis ◽  
High Resolution ◽  
Single Particle ◽  
Particle Analysis ◽  
Data Sets ◽  
Particle Sizes ◽  
User Input ◽  
Class Average ◽  
Resolution Single ◽  
Selection Of

AbstractHigh-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, we have developed a self-supervised workflow. Our workflow includes an iterative strategy to use the 2D class average to improve training particles and a progressively improved convolutional neural network (CNN) for particle picking. To automate the selection of particles, we define a threshold (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. Our workflow has been tested using six publicly available data sets with different particle sizes and shapes, and is able to automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. Our workflow offers a way toward automated single-particle Cryo-EM data analysis at the stage of particle picking. The workflow may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE, and EMAN2.

scholarly journals Parameter calibration with stochastic gradient descent for interacting particle systems driven by neural networks

2021 ◽  
Author(s):  
Simone Göttlich ◽  
Claudia Totzeck
Keyword(s):  
Gradient Descent ◽  
Interacting Particle Systems ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Force Model ◽  
Data Sets ◽  
Optimal Controls ◽  
Parameter Calibration ◽  
Descent Algorithm ◽  
Gradient Descent Algorithm

AbstractWe propose a neural network approach to model general interaction dynamics and an adjoint-based stochastic gradient descent algorithm to calibrate its parameters. The parameter calibration problem is considered as optimal control problem that is investigated from a theoretical and numerical point of view. We prove the existence of optimal controls, derive the corresponding first-order optimality system and formulate a stochastic gradient descent algorithm to identify parameters for given data sets. To validate the approach, we use real data sets from traffic and crowd dynamics to fit the parameters. The results are compared to forces corresponding to well-known interaction models such as the Lighthill–Whitham–Richards model for traffic and the social force model for crowd motion.

The Design of Strapdown Inertial AHRS Based on MEMS

2013 ◽  
Vol 756-759 ◽  
pp. 518-522
Author(s):  
Qing Hui Wang ◽  
Dan Li ◽  
Lei Chang ◽  
Wen Zhou Wang
Keyword(s):  
Real Time ◽  
Reference System ◽  
Gradient Descent ◽  
Low Cost ◽  
Proper Selection ◽  
Descent Algorithm ◽  
The Real ◽  
The Core ◽  
Gradient Descent Algorithm ◽  
Selection Of

A method of low cost strapdown inertial Attitude and Heading Reference System based on MEMS is implemented in this paper. Based on the analysis of the modules, the proper selection of the core processor and the inertial devices, the hardware components of the system is presented; Using the gradient-descent algorithm which is based on quaternion, it can finish the calculation of the attitude; This AHRS can realize the real-time extraction, calculation and the output of the information.

A function fitting method

2020 ◽  
Vol 26 (1) ◽  
pp. 59-65
Author(s):  
Rajesh Dachiraju
Keyword(s):  
Minimization Problem ◽  
Gradient Descent ◽  
Convex Minimization Problem ◽  
Descent Algorithm ◽  
Function Fitting ◽  
Fitting Method ◽  
Actual Solution ◽  
Gradient Descent Algorithm ◽  
Potential Applications ◽  
Fitting Problem

AbstractIn this article, we describe a function fitting method that has potential applications in machine learning and also prove relevant theorems. The described function fitting method is a convex minimization problem which can be solved using a gradient descent algorithm. We also provide qualitative analysis on fitness to data of this function fitting method. The function fitting problem is also shown to be a solution of a linear, weak partial differential equation (PDE). We describe a simple numerical solution using a gradient descent algorithm that converges uniformly to the actual solution. As the functional of the minimization problem is a quadratic form, there also exists a numerical method using linear algebra.

scholarly journals A self-supervised workflow for particle picking in cryo-EM

IUCrJ ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 719-727 ◽  
Author(s):  
Donal M. McSweeney ◽  
Sean M. McSweeney ◽  
Qun Liu
Keyword(s):  
Data Analysis ◽  
High Resolution ◽  
Single Particle ◽  
Particle Analysis ◽  
Data Sets ◽  
Particle Sizes ◽  
User Input ◽  
Class Average ◽  
Resolution Single ◽  
Selection Of

High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE and EMAN2.

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

Selection of one-dimensional sedimentation: models for on-line use

1995 ◽  
Vol 31 (2) ◽  
pp. 193-204 ◽  
Author(s):  
Koen Grijspeerdt ◽  
Peter Vanrolleghem ◽  
Willy Verstraete
Keyword(s):  
Steady State ◽  
Selection Criteria ◽  
Data Sets ◽  
Concentration Profiles ◽  
A Posteriori ◽  
One Dimensional ◽  
On Line ◽  
Dynamic Concentration ◽  
Selection Of ◽  
Modelling Task

A comparative study of several recently proposed one-dimensional sedimentation models has been made. This has been achieved by fitting these models to steady-state and dynamic concentration profiles obtained in a down-scaled secondary decanter. The models were evaluated with several a posteriori model selection criteria. Since the purpose of the modelling task is to do on-line simulations, the calculation time was used as one of the selection criteria. Finally, the practical identifiability of the models for the available data sets was also investigated. It could be concluded that the model of Takács et al. (1991) gave the most reliable results.

Real-time Approximation of Photometric Polygonal Lights

2020 ◽  
Vol 3 (1) ◽  
pp. 1-18
Author(s):  
Christian Luksch ◽  
Lukas Prost ◽  
Michael Wimmer
Keyword(s):  
Real Time ◽  
Specular Reflection ◽  
Near Field ◽  
Measurement Data ◽  
Data Sets ◽  
Photometric Measurement ◽  
Integration Technique ◽  
Time Approximation ◽  
Light Emitter ◽  
Selection Of

We present a real-time rendering technique for photometric polygonal lights. Our method uses a numerical integration technique based on a triangulation to calculate noise-free diffuse shading. We include a dynamic point in the triangulation that provides a continuous near-field illumination resembling the shape of the light emitter and its characteristics. We evaluate the accuracy of our approach with a diverse selection of photometric measurement data sets in a comprehensive benchmark framework. Furthermore, we provide an extension for specular reflection on surfaces with arbitrary roughness that facilitates the use of existing real-time shading techniques. Our technique is easy to integrate into real-time rendering systems and extends the range of possible applications with photometric area lights.

scholarly journals Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

2021 ◽  
Author(s):  
Marco Mele ◽  
Cosimo Magazzino ◽  
Nicolas Schneider ◽  
Floriana Nicolai
Keyword(s):  
Economic Growth ◽  
Gradient Descent ◽  
Statistical Data ◽  
Stochastic Gradient Descent ◽  
Policy Recommendations ◽  
Dynamic Interactions ◽  
Gradient Descent Algorithm ◽  
The Relationship ◽  
Main Strand ◽  
Yearly Data

AbstractAlthough the literature on the relationship between economic growth and CO2 emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960–2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.

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