Technical design: data processing pipeline in eHealth

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.

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Probabilistic Approaches to Sensor Layout Design, Data Processing, and Damage Detection

Encyclopedia of Structural Health Monitoring ◽

10.1002/9780470061626.shm072 ◽

2008 ◽

Author(s):

Sankaran Mahadevan ◽

Xiaomo Jiang ◽

Robert F. Guratzsch

Keyword(s):

Data Processing ◽

Damage Detection ◽

Layout Design ◽

Design Data

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

Microorganisms ◽

10.3390/microorganisms7100388 ◽

2019 ◽

Vol 7 (10) ◽

pp. 388 ◽

Cited By ~ 1

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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