scholarly journals AutoMicroED: A semi-automated MicroED processing pipeline

2021 ◽  
Author(s):  
Samantha M Powell ◽  
Irina V Novikova ◽  
Doo Nam Kim ◽  
James E Evans
Keyword(s):  
Data Processing ◽  
Small Molecule ◽  
Structure Determination ◽  
Quality Data ◽  
Immediate Feedback ◽  
Processing Pipeline ◽  
Image Conversion ◽  
Multiple Structures ◽  
Automation Tools ◽  
Protein Datasets

Despite rapid adaptation of micro-electron diffraction (MicroED) for protein and small molecule structure determination to sub-angstrom resolution, the lack of automation tools for easy MicroED data processing remains a challenge for expanding to the broader scientific community. In particular, automation tools, which are novice user friendly, compatible with heterogenous datasets and can be run in unison with data collection to judge the quality of incoming data (similar to cryosparc LIVE for single particle cryoEM) do not exist. Here, we present AutoMicroED, a cohesive and semi-automatic MicroED data processing pipeline that runs through image conversion, indexing, integration and scaling of data, followed by merging of successful datasets that are pushed through phasing and final structure determination. AutoMicroED is compatible with both small molecule and protein datasets and creates a straightforward and reproducible method to solve single structures from pure samples, or multiple structures from mixed populations. The immediate feedback on data quality, data completeness and more parameters, aids users to identify whether they have collected enough data for their needs. Overall, AutoMicroED permits efficient structure elucidation for both novice and experienced users with comparable results to more laborious manual processing.

scholarly journals Extending theFermi-LAT Data Processing Pipeline to the Grid

2012 ◽  
Vol 396 (3) ◽  
pp. 032121 ◽  
Author(s):  
S Zimmer ◽  
L Arrabito ◽  
T Glanzman ◽  
T Johnson ◽  
C Lavalley ◽  
...  
Keyword(s):  
Data Processing ◽  
Processing Pipeline

scholarly journals A Wireless Sensor Network with Enhanced Power Efficiency and Embedded Strain Cycle Identification for Fatigue Monitoring of Railway Bridges

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Glauco Feltrin ◽  
Nemanja Popovic ◽  
Kallirroi Flouri ◽  
Piotr Pietrzak
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Processing ◽  
Monitoring System ◽  
Life Assessment ◽  
Wireless Sensor ◽  
Quality Data ◽  
Railway Bridges ◽  
Strain Cycle

Wireless sensor networks have been shown to be a cost-effective monitoring tool for many applications on civil structures. Strain cycle monitoring for fatigue life assessment of railway bridges, however, is still a challenge since it is data intensive and requires a reliable operation for several weeks or months. In addition, sensing with electrical resistance strain gauges is expensive in terms of energy consumption. The induced reduction of battery lifetime of sensor nodes increases the maintenance costs and reduces the competitiveness of wireless sensor networks. To overcome this drawback, a signal conditioning hardware was designed that is able to significantly reduce the energy consumption. Furthermore, the communication overhead is reduced to a sustainable level by using an embedded data processing algorithm that extracts the strain cycles from the raw data. Finally, a simple software triggering mechanism that identifies events enabled the discrimination of useful measurements from idle data, thus increasing the efficiency of data processing. The wireless monitoring system was tested on a railway bridge for two weeks. The monitoring system demonstrated a good reliability and provided high quality data.

scholarly journals Discovery of a first-in-class inhibitor of the PRMT5-substrate adaptor interaction

2021 ◽  
Author(s):  
David C McKinney ◽  
Brian J McMillan ◽  
Matthew Ranaghan ◽  
Jamie A Moroco ◽  
Merissa Brousseau ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Small Molecule ◽  
Structure Determination ◽  
Mode Of Action ◽  
Covalent Bond ◽  
Adaptor Proteins ◽  
Binding Motif ◽  
Lead Compound ◽  
Synthetic Lethal ◽  
Compound Series

AbstractPRMT5 and its substrate adaptor proteins (SAPs), pICln and Riok1, are synthetic lethal dependencies in MTAP-deleted cancer cells. SAPs share a conserved PRMT5 binding motif (PBM) which mediates binding to a surface of PRMT5 distal to the catalytic site. This interaction is required for methylation of several PRMT5 substrates, including histone and spliceosome complexes. We screened for small molecule inhibitors of the PRMT5-PBM interaction and validated a compound series which binds to the PRMT5-PBM interface and directly inhibits binding of SAPs. Mode of action and structure determination studies revealed that these compounds form a covalent bond between a halogenated pyridazinone group and cysteine 278 of PRMT5. Optimization of the starting hit produced a lead compound, BRD0639, which engages the target in cells, disrupts the PRMT5-RIOK1 complex, and reduces substrate methylation. BRD0639 is a first-in-class PBM-competitive small molecule that can support studies of PBM-dependent PRMT5 activities and the development of novel PRMT5 inhibitors that selectively target these functions.

scholarly journals THE GWAC DATA PROCESSING AND MANAGEMENT SYSTEM

2021 ◽  
Vol 53 ◽  
pp. 174-179
Author(s):  
Y. Xu ◽  
L. P. Xin ◽  
X. H. Han ◽  
H. B. Cai ◽  
L. Huang ◽  
...  
Keyword(s):  
Data Processing ◽  
User Interfaces ◽  
Management System ◽  
Scientific Data ◽  
Data Archiving ◽  
Processing Pipeline ◽  
Advanced Technologies ◽  
Single Frame ◽  
Moonless Night ◽  
Processing Requirement

GWAC will have been built an integrated FOV of 5,000 degree2 and have already built 1,800 square degree2. The limit magnitude of a 10-second exposure image in the moonless night is 16R. In each observation night, GWAC produces about 0.7TB of raw data, and the data processing pipeline generates millions of single frame alerts. We describe the GWAC Data Processing and Management System (GPMS), including hardware architecture, database, detection-filtering-validation of transient candidates, data archiving, and user interfaces for the check of transient and the monitor of the system. GPMS combines general technology and software in astronomy and computer field, and use some advanced technologies such as deep learning. Practical results show that GPMS can fully meet the scientific data processing requirement of GWAC. It can online accomplish the detection, filtering and validation of millions of transient candidates, and feedback the final results to the astronomer in real-time. During the observation from October of 2018 to December of 2019, we have already found 102 transients.

GENERAL FRAMEWORK FOR SIMULATING POWER QUALITY DATA PROCESSING

2021 ◽  
Author(s):  
A. Eisenmann ◽  
T. Streubel ◽  
C. Kattmann ◽  
K. Rudion
Keyword(s):  
Data Processing ◽  
Power Quality ◽  
General Framework ◽  
Quality Data

scholarly journals Optimizing a Whole-Genome Sequencing Data Processing Pipeline for Precision Surveillance of Health Care-Associated Infections

2019 ◽  
Vol 7 (10) ◽  
pp. 388 ◽  
Author(s):  
Weihua Huang ◽  
Guiqing Wang ◽  
Changhong Yin ◽  
Donald Chen ◽  
Abhay Dhand ◽  
...  
Keyword(s):  
Health Care ◽  
Data Processing ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Structural Changes ◽  
Essential Element ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Infection Control Program ◽  
Processing Pipeline

The surveillance of health care-associated infection (HAI) is an essential element of the infection control program. While whole-genome sequencing (WGS) has widely been adopted for genomic surveillance, its data processing remains to be improved. Here, we propose a three-level data processing pipeline for the precision genomic surveillance of microorganisms without prior knowledge: species identification, multi-locus sequence typing (MLST), and sub-MLST clustering. The former two are closely connected to what have widely been used in current clinical microbiology laboratories, whereas the latter one provides significantly improved resolution and accuracy in genomic surveillance. Comparing to a broadly used reference-dependent alignment/mapping method and an annotation-dependent pan-/core-genome analysis, we implemented our reference- and annotation-independent, k-mer-based, simplified workflow to a collection of Acinetobacter and Enterococcus clinical isolates for tests. By taking both single nucleotide variants and genomic structural changes into account, the optimized k-mer-based pipeline demonstrated a global view of bacterial population structure in a rapid manner and discriminated the relatedness between bacterial isolates in more detail and precision. The newly developed WGS data processing pipeline would facilitate WGS application to the precision genomic surveillance of HAI. In addition, the results from such a WGS-based analysis would be useful for the precision laboratory diagnosis of infectious microorganisms.

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