For genetic identification of soybean [ (L.) Merrill] cultivars, insertions/deletions (InDel) markers have been preferred currently because they are easy to use, co-dominant and relatively abundant. Despite their biological importance, the investigation of InDels with proven quality and reproducibility has been limited. In this study, we described soybean barcode system approach based on InDel makers, each of which is specific to a dense variation block (dVB) with non-random recombination due to many variations. Firstly, 2,274 VBs were mined by analyzing whole genome data in six soybean cultivars (Backun, Sinpaldal 2, Shingi, Daepoong, Hwangkeum, and Williams 82) for transferability to dVB-specific InDel markers. Secondly, 73,327 putative InDels in the dVB regions were identified for the development of soybean barcode system. Among them, 202 dVB-specific InDels from all soybean cultivars were selected by gel electrophoresis, which were converted as 2D barcode types according to comparing amplicon polymorphisms in the five cultivars to the reference cultivar. Finally, the polymorphism of the markers were assessed in 147 soybean cultivars, and the soybean barcode system that allows a clear distinction among soybean cultivars is also detailed. In addition, the changing of the dVBs in a chromosomal level can be quickly identified due to investigation of the reshuffling pattern of the soybean cultivars with 27 maker sets. Especially, a backcross-inbred offspring, "Singang" and a recurrent parent, "Sowon" were identified by using the 27 InDel markers. These results indicate that the soybean barcode system enables not only the minimal use of molecular markers but also comparing the data from different sources due to no need of exploiting allele binning in new varieties.
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http://dx.doi.org/10.3389/fpls.2017.00520 | DOI Listing |
Sensors (Basel)
December 2024
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
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December 2024
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Liuxia Street, Hangzhou 310023, China.
Broadcast ephemeris data are essential for the precision and reliability of the BeiDou Navigation Satellite System (BDS) but are highly susceptible to anomalies caused by various interference factors, such as ionospheric and tropospheric effects, solar radiation pressure, and satellite clock biases. Traditional threshold-based methods and manual review processes are often insufficient for detecting these complex anomalies, especially considering the distinct characteristics of different satellite types. To address these limitations, this study proposes an automated anomaly detection method using the IF-TEA-LSTM model.
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Applied Neuromechanics Research Group, Laboratory of Neuromechanics, Federal University of Pampa (Unipampa), P.O. Box 118, Uruguaiana 97500-970, RS, Brazil.
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