Sequence variation and selection of small RNAs in domesticated rice

<p>Abstract</p> <p>Background</p> <p>Endogenous non-coding small RNAs (21-24 nt) play an important role in post-transcriptional gene regulation in plants. Domestication selection is the most important evolutionary force in shaping crop genomes. The extent of polymorphism at small RNA loci in domesticated rice and whether small RNA loci are targets of domestication selection have not yet been determined.</p> <p>Results</p> <p>A polymorphism survey of 94 small RNA loci (88 <it>MIRNAs</it>, four <it>TAS3 </it>loci and two miRNA-like long hairpins) was conducted in domesticated rice, generating 2 Mb of sequence data. Many mutations (substitution or insertion/deletion) were observed at small RNA loci in domesticated rice, e.g. 12 mutation sites were observed in the mature miRNA sequences of 11 <it>MIRNAs </it>(12.5% of the investigated <it>MIRNAs</it>). Several small RNA loci showed significant signals for positive selection and/or potential domestication selection.</p> <p>Conclusions</p> <p>Sequence variation at miRNAs and other small RNAs is higher than expected in domesticated rice. Like protein-coding genes, non-coding small RNA loci could be targets of domestication selection and play an important role in rice domestication and improvement.</p

Nonlinear Magneto-Electro-Mechanical Response of Physical Cross-Linked Magneto-Electric Polymer Gel

This work reports on a novel magnetorheological polymer gel with carbon nanotubes and carbonyl iron particles mixed into the physical cross-linked polymer gel matrix. The resulting composites show unusual nonlinear magneto-electro-mechanical responses. Because of the low matrix viscosity, effective conductive paths formed by the CNTs were mobile and high-performance sensing characteristics were observed. In particular, due to the transient and mutable physical cross-linked bonds in the polymer gel, the electromechanical behavior acted in a rate-dependent manner. External stimulus at a high rate significantly enhanced the electrical resistance response during mechanical deformation. Meanwhile, the rheological properties were regulated by the external magnetic field when magnetic particles were added. This dual enhancement mechanism further contributes to the active control of electromechanical performance. These polymer composites could be adopted as electromechanical sensitive sensors to measure impact and vibration under different frequencies. There is great potential for this magnetorheological polymer gel in the application of intelligent vibration controls

Genome-wide selection footprints and deleterious variations in young Asian allotetraploid rapeseed

Brassica napus (AACC, 2n=38) is an important oilseed crop grown worldwide. However, little is known about the population evolution of this species, the genomic difference between its major genetic groups, such as European and Asian rapeseed, and the impacts of historical large-scale introgression events on this young tetraploid. In this study, we reported the de novo assembly of the genome sequences of an Asian rapeseed (B. napus), Ningyou 7 and its four progenitors and compared these genomes with other available genomic data from diverse European and Asian cultivars. Our results showed that Asian rapeseed originally derived from European rapeseed but subsequently significantly diverged, with rapid genome differentiation after hybridization and intensive local selective breeding. The first historical introgression of B. rapa dramatically broadened the allelic pool but decreased the deleterious variations of Asian rapeseed. The second historical introgression of the double-low traits of European rapeseed (canola) has reshaped Asian rapeseed into two groups (double-low and double-high), accompanied by an increase in genetic load in the double-low group. This study demonstrates distinctive genomic footprints and deleterious SNP (Single Nucleotide Polymorphism) variants for local adaptation by recent intra- and interspecies introgression events and provides novel insights for understanding the rapid genome evolution of a young allopolyploid cro

GSCtool: A Novel Descriptor that Characterizes the Genome for Applying Machine Learning in Genomics

Machine learning (ML) is one of the core driving forces for the next breeding stage, and Breeding 4.0. Genotype matrix based on single‐nucleotide polymorphisms (SNPs) is often used in ML for genome‐to‐phenotype prediction. Genotype matrix has an inherent defect, as the feature spaces it generates across different individuals or groups are inconsistent, and this hinders the application of ML. To overcome the challenge, a genome descriptor, Genic SNPs Composition Tool (GSCtool) is developed, which counts the number of SNPs in each gene of the genome so the dimension of the feature vectors equals the number of annotated genes in a species. Compared to using the genotype matrix, using GSCtool significantly decreases the model training time and has a higher accuracy of phenotype prediction. GSCtool also achieves good performance in variety identification, which is useful in crop variety protection. In general, GSCtool will help facilitate the application and study of genomic ML. The source code and test data of GSCtool are freely available at https://github.com/SZJhacker/GSCtool and https://gitee.com/shenzijie/GSCtool

Contamination Evaluation and Source Identification of Heavy Metals in the Sediments from the Lishui River Watershed, Southern China

Seven heavy metals (Cr, Mn, Co, Ni, Cu, Zn, Pb) were measured in surface sediments from the Lishui River watershed, an area with increased soil erosion in China. The mean concentrations of heavy metals were 61.20 mg/kg (Cr), 757.15 mg/kg (Mn), 9.39 mg/kg (Co), 25.31 mg/kg (Ni), 22.84 mg/kg (Cu), 91.66 mg/kg (Zn), and 40.19 mg/kg (Pb), respectively. The spatial distribution of heavy metals was site-specific, exhibiting a remarkably high level in the sampling stations with intense agricultural activities (Lixian) and industrial activities (Jinshi). Contamination indexes including contamination factor, pollution load index, nemerow multi-factor index, potential ecological risk index, and human health risk were used to assess the pollution degree of the river sediments. The results indicated the pollution degree of heavy metals decreased in the order of Mn &gt; Pb &gt; Zn &gt; Cr &gt; Cu &gt; Ni &gt; Co. Heavy metals resulted in non-pollution to moderate pollution, with low ecological risk and an acceptable carcinogenic risk caused by Cr and Ni for children and adults. Person&rsquo;s correlation analysis and principal component analysis, coupled with cluster analysis, revealed that the sediments from the Lishui River were mainly influenced by two sources. Cr, Co, Ni, and Cu were mainly derived from natural sources, while Mn, Zn, and Pb originated from agricultural and industrial activities, mining, and vehicular traffic

New Models for Vertical Distribution and Variation of Tropospheric Water Vapor—A Case Study for China

For better modeling the vertical distribution and variations of water vapor, a 10-year time-series of a newly derived water vapor parameter (termed IRPWV), defined as the ratio of water vapor density (WVD) to the total precipitable water vapor (TPWV), was statistically analyzed. This research showed that the vertical distribution of IRPWV presents a periodic pattern and is highly correlated with the relative magnitude of its corresponding TPWV when compared with the other TPWVs in the same time range. Six TPWV ranges were first chosen to determine the relative magnitude and then used to classify the IRPWV vertical distributions of TPWV. For the periodic variations in each of the six classified IRPWV vertical distribution time-series, a temporal IRPWV model was developed accordingly with six sets of coefficients. The new models were validated by comparing their predictions against the reference values from sounding data at 12 radiosonde stations in China, and their performance was also evaluated against that of the commonly used exponential model. Results showed that, first, the proportions of the height range that had reduced annual root mean square error (RMSE) of WVD in all height ranges within all TPWV ranges were over 75% at the 12 stations. Then, the annual RMSEs of the WVD for all the stations were reduced by at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28% in each of the 10 selected height ranges, respectively

New Models for Vertical Distribution and Variation of Tropospheric Water Vapor&mdash;A Case Study for China

For better modeling the vertical distribution and variations of water vapor, a 10-year time-series of a newly derived water vapor parameter (termed IRPWV), defined as the ratio of water vapor density (WVD) to the total precipitable water vapor (TPWV), was statistically analyzed. This research showed that the vertical distribution of IRPWV presents a periodic pattern and is highly correlated with the relative magnitude of its corresponding TPWV when compared with the other TPWVs in the same time range. Six TPWV ranges were first chosen to determine the relative magnitude and then used to classify the IRPWV vertical distributions of TPWV. For the periodic variations in each of the six classified IRPWV vertical distribution time-series, a temporal IRPWV model was developed accordingly with six sets of coefficients. The new models were validated by comparing their predictions against the reference values from sounding data at 12 radiosonde stations in China, and their performance was also evaluated against that of the commonly used exponential model. Results showed that, first, the proportions of the height range that had reduced annual root mean square error (RMSE) of WVD in all height ranges within all TPWV ranges were over 75% at the 12 stations. Then, the annual RMSEs of the WVD for all the stations were reduced by at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28% in each of the 10 selected height ranges, respectively