scholarly journals Real-time object locator for cryo-EM data collection --- You only navigate EM once ---

2021 ◽  
Author(s):  
Koji Yonekura ◽  
Saori Maki-Yonekura ◽  
Hisashi Naitow ◽  
Tasuku Hamaguchi ◽  
Kiyofumi Takaba
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Electron Diffraction ◽  
Data Collection ◽  
Real Time ◽  
Single Particle ◽  
Detection System ◽  
Target Object ◽  
Electron Crystallography ◽  
Cryo Electron Microscopy

In cryo-electron microscopy (cryo-EM) data collection, locating a target object is the most error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation showed its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and for locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection.

scholarly journals Machine learning-based real-time object locator/evaluator for cryo-EM data collection

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Koji Yonekura ◽  
Saori Maki-Yonekura ◽  
Hisashi Naitow ◽  
Tasuku Hamaguchi ◽  
Kiyofumi Takaba
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Data Collection ◽  
Real Time ◽  
Detection System ◽  
Target Object ◽  
Accurate Data ◽  
Electron Crystallography ◽  
Cryo Electron Microscopy ◽  
Diffraction Patterns

AbstractIn cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation shows its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and in locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection. The proposed approach will advance high-throughput and accurate data collection of images and diffraction patterns with minimal human operation.

scholarly journals Single-particle cryo-EM—How did it get here and where will it go

Science ◽  
2018 ◽  
Vol 361 (6405) ◽  
pp. 876-880 ◽  
Author(s):  
Yifan Cheng
Keyword(s):  
Electron Microscopy ◽  
Structural Biology ◽  
Single Particle ◽  
Electron Crystallography ◽  
The Past ◽  
Cryo Electron Microscopy ◽  
Technological Advances ◽  
The Future ◽  
Electron Cryotomography

Cryo–electron microscopy, or simply cryo-EM, refers mainly to three very different yet closely related techniques: electron crystallography, single-particle cryo-EM, and electron cryotomography. In the past few years, single-particle cryo-EM in particular has triggered a revolution in structural biology and has become a newly dominant discipline. This Review examines the fascinating story of its start and evolution over the past 40-plus years, delves into how and why the recent technological advances have been so groundbreaking, and briefly considers where the technique may be headed in the future.

scholarly journals High-resolution cryo-EM using beam-image shift at 200 keV

2020 ◽  
Author(s):  
Jennifer N. Cash ◽  
Sarah Kearns ◽  
Yilai Li ◽  
Michael A. Cianfrocco
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Data Collection ◽  
Data Quality ◽  
Single Particle ◽  
Aberration Correction ◽  
Cryo Electron Microscopy ◽  
Beam Image ◽  
Recent Advances ◽  
Shift Data

ABSTRACTRecent advances in single-particle cryo-electron microscopy (cryo-EM) data collection utilizes beam-image shift to improve throughput. Despite implementation on 300 keV cryo-EM instruments, it remains unknown how well beam-image shift data collection affects data quality on 200 keV instruments and how much aberrations can be computationally corrected. To test this, we collected and analyzed a cryo-EM dataset of aldolase at 200 keV using beam-image shift. This analysis shows that beam tilt on the instrument initially limited the resolution of aldolase to 4.9Å. After iterative rounds of aberration correction and particle polishing in RELION, we were able to obtain a 2.8Å structure. This analysis demonstrates that software correction of microscope aberrations can provide a significant improvement in resolution at 200 keV.

scholarly journals Live analysis and reconstruction of single-particle cryo-electron microscopy data with CryoFLARE

2019 ◽  
Author(s):  
Andreas D. Schenk ◽  
Simone Cavadini ◽  
Nicolas H. Thomä ◽  
Christel Genoud
Keyword(s):  
Electron Microscopy ◽  
Data Collection ◽  
Single Particle ◽  
Integrated Reporting ◽  
Seamless Integration ◽  
High Resolution Data ◽  
User Input ◽  
Cryo Electron Microscopy ◽  
New Processing ◽  
Set Up

AbstractEfficient, reproducible and accountable single-particle cryo-electron microscopy structure determination is tedious and often impeded by lack of a standardized procedure for data analysis and processing. To address this issue, we have developed the FMI Live Analysis and Reconstruction Engine (CryoFLARE). CryoFLARE is a modular open-source platform offering easy integration of new processing algorithms developed by the cryo-EM community. It provides a user-friendly interface that allows fast setup of standardized workflows, serving the need of pharmaceutical industry and academia alike who need to optimize throughput of their microscope. To consistently document how data is processed, CryoFLARE contains an integrated reporting facility to create reports.Live analysis and processing parallel to data acquisition are used to monitor and optimize data quality. Problems at the level of the sample preparation (heterogeneity, ice thickness, sparse particles, areas selected for acquisition, etc.) or misalignments of the microscope optics can quickly be detected and rectified before data collection is continued. Interfacing with automated data collection software for retrieval of acquisition metadata reduces user input needed for analysis, and with it minimizes potential sources of errors and workflow inconsistencies. Local and remote export support in Relion-compatible job and data format allows seamless integration into the refinement process. The support for non-linear workflows and fine-grained scheduling for mixed workflows with separate CPU and GPU based calculation steps ensures optimal use of processing hardware. CryoFLARE’s flexibility allows it to be used for all types of image acquisitions, ranging from sample screening to high-resolution data collection, and offers a new alternative for setting up image processing workflows. It can be used without modifications of the hardware/software delivered by the microscope supplier. As it is running on a server in parallel to the hardware used for acquisition, it can easily be set up for remote display connections and fast control of the acquisition status.

scholarly journals High-resolution cryo-EM using beam-image shift at 200 keV

IUCrJ ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 1179-1187 ◽  
Author(s):  
Jennifer N. Cash ◽  
Sarah Kearns ◽  
Yilai Li ◽  
Michael A. Cianfrocco
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Data Collection ◽  
Single Particle ◽  
Particle Motion ◽  
Data Set ◽  
Cryo Electron Microscopy ◽  
Beam Image ◽  
Shift Data ◽  
Resolution Structure

Recent advances in single-particle cryo-electron microscopy (cryo-EM) data collection utilize beam-image shift to improve throughput. Despite implementation on 300 keV cryo-EM instruments, it remains unknown how well beam-image-shift data collection affects data quality on 200 keV instruments and the extent to which aberrations can be computationally corrected. To test this, a cryo-EM data set for aldolase was collected at 200 keV using beam-image shift and analyzed. This analysis shows that the instrument beam tilt and particle motion initially limited the resolution to 4.9 Å. After particle polishing and iterative rounds of aberration correction in RELION, a 2.8 Å resolution structure could be obtained. This analysis demonstrates that software correction of microscope aberrations can provide a significant improvement in resolution at 200 keV.

scholarly journals Optimized data acquisition workflow by sample thickness determination

2020 ◽  
Author(s):  
Jan Rheinberger ◽  
Gert Oostergetel ◽  
Guenter P Resch ◽  
Cristina Paulino
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Data Collection ◽  
Data Acquisition ◽  
Data Storage ◽  
Single Particle ◽  
Collection Efficiency ◽  
Sample Thickness ◽  
Cryo Electron Microscopy ◽  
Key Parameter

AbstractSample thickness is a known key parameter in cryo-electron microscopy (cryo-EM) and can affect the amount of high-resolution information retained in the image. Yet, common data acquisition approaches in single particle cryo-EM do not take it into account. Here, we demonstrate how the sample thickness can be determined before data acquisition, allowing to identify optimal regions and restrict data collection to images with preserved high-resolution details. This quality over quantity approach, almost entirely eliminates the time- and storage-consuming collection of suboptimal images, which are discarded after a recorded session or during early image processing due to lack of high-resolution information. It maximizes data collection efficiency and lowers the electron microscopy time required per dataset. This strategy is especially useful, if the speed of data collection is restricted by the microscope hardware and software, or if data transfer, data storage and computational power are a bottleneck.SynopsisDetermining sample thickness, a key parameter in single particle cryo-electron microscopy, before data acquisition, and targeting only optimal areas, maximizes the data output from a single particle cryo-electron microscopy session. Scripts and optimized workflows for EPU and SerialEM are presented utilizing this concept.

Enhanced information from biological materials through technological improvements in cryo-electron microscopy

1992 ◽  
Vol 50 (1) ◽  
pp. 788-789
Author(s):  
Marc J.C. de Jong ◽  
Wim M. Busing ◽  
Max T. Otten
Keyword(s):  
Electron Microscopy ◽  
Electron Beam ◽  
Biological Materials ◽  
Electron Crystallography ◽  
Cryo Electron Microscopy ◽  
Transmission Electron ◽  
The Many ◽  
Technological Improvements ◽  
Beam Damage

Biological materials damage rapidly in the electron beam, limiting the amount of information that can be obtained in the transmission electron microscope. The discovery that observation at cryo temperatures strongly reduces beam damage (in addition to making it unnecessaiy to use chemical fixatives, dehydration agents and stains, which introduce artefacts) has given an important step forward to preserving the ‘live’ situation and makes it possible to study the relation between function, chemical composition and morphology.Among the many cryo-applications, the most challenging is perhaps the determination of the atomic structure. Henderson and co-workers were able to determine the structure of the purple membrane by electron crystallography, providing an understanding of the membrane's working as a proton pump. As far as understood at present, the main stumbling block in achieving high resolution appears to be a random movement of atoms or molecules in the specimen within a fraction of a second after exposure to the electron beam, which destroys the highest-resolution detail sought.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

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