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Auxin response factors (ARFs) are transcription factors that regulate the transcription of auxin-responsive genes during plant growth and development. In this study, 29 and 30 ARF members were identified from the two wild peanut species, A. duranensis and A. ipaensis , respectively. The ARFs, including their classifications, conserved domains and evolutionary relationships were characterized. RNA-seq analyses revealed that some of the ARF genes were responsive to abiotic stress, particularly high salinity. In addition to abiotic stress, the expression of 2 ARF members was also regulated by biotic stress, specifically Bradyrhizobium infection in A. duranensis. The ARF gene Arahy.7DXUOK was predicted to be a potential target of miR160. Overexpression of miR160 could cause degradation of the Arahy.7DXUOK target gene transcript and increased salt tolerance in miR160OX transgenic plants. Therefore, these molecular characterization and expression profile analyses provide comprehensive information on ARF family members and will help to elucidate their functions to facilitate further research on peanuts. • 29 and 30 ARF members were identified from the two wild peanut species. • Some of the ARF genes were responsive to abiotic and biotic stress in A. duranensis. • miR160 overexpression protects seedling development against the effects of ROS under salt stress. [ABSTRACT FROM AUTHOR]

Ocean‐bottom node exploration has developed rapidly in marine seismic exploration. However, due to no illumination for the seafloor, and the observation system of the 'less traces but more shots' mode, the conventional surface‐related multiples elimination meets challenges for the application in the ocean‐bottom node exploration. This paper develops a new three‐dimensional surface‐related multiples elimination technique for ocean‐bottom node data. First, a three‐dimensional Kirchhoff pre‐stack time mirror migration algorithm is proposed to realize the accurate imaging for the seabed. Second, the time domain Kirchhoff demigration method is used to construct the contribution traces for multiples prediction. Finally, the surface multiples can be predicted based on the equation derived in the paper. Model tests and field data processing prove that our method can accurately predict the surface‐related multiples for ocean‐bottom node data and be helpful to the multiple elimination, which shows that the proposed technique has potential in actual ocean‐bottom node data processing. [ABSTRACT FROM AUTHOR]

The source vessel noise is a very common noise type in offshore seismic surveys. The state‐of‐art deep learning‐based methods provide an end‐to‐end framework for seismic data denoising. The denoising performance of a pretrained network is, however, highly dependent on the completeness of the training set. When training a denoising network with only field data, especially for attenuating erratic noise, it is hard to obtain a noise‐free data as the training target for the network. Transfer learning, by combining the synthetic and field data, is an alternative solution for improving the generalization capabilities of the network, although being able to model such erratic noise represents also a challenge. Although the denoising results by traditional methods are not accurate enough for creating a complete training set, the features in residual noise by subtracting the denoised data from noisy data are enough for the network to learn. Considering the aforementioned factors, we develop a deep learning‐based workflow for the attenuation of the erratic source vessel noise from ocean bottom node 4C data. Instead of using denoising results directly, we use the conventional methods to extract noise and add them to the high signal‐to‐ratio region of the field data. The created noisy dataset is different from the original noisy data in noise regions; thus, the pretrained network can also be used for predicting the same original data. The denoising results of synthetic and field data all show that even the network is trained on a noisy labelled dataset, we still can obtain high signal‐to‐noise ratio denoising result. Besides, when compare with the results by filtering‐based methods, our proposed method can attenuate the vessel noise more effectively and preserve the near offsets reflections. [ABSTRACT FROM AUTHOR]

Blended acquisition, which allows multiple sources almost simultaneously fired, has become an effective way for accelerating seismic data acquisition. In order to use conventional processing methods for imaging, deblending is necessary for this special acquisition. Convolutional neural network‐based deblending methods provide a novel end‐to‐end framework for source separation. We proposed a field‐data‐based augmentation method that uses shuffled deblending noise as the features to be learned and take the inaccurate labels as the output of the network. Synthetic data experiments show that a network trained on data set with the proposed data augmentation method has higher accuracy for deblending even if the labelled data are noisy. Besides, 2D discrete wavelet transform, which has the advantage of multiscale decomposition and dimensionality reduction, is introduced to accelerate the computation of the network. The data augmentation method for data set generation and the computational speedup method for network training/predicting are also applied to field data. The results from synthetic and field data all confirm the performance of our methods. [ABSTRACT FROM AUTHOR]

The supervised neural network-based method provides an effective way for seismic data denoising. The noise level of seismic erratic noise, i.e., outlier, varies from traces, time windows, and shot gathers. The networks with popular structures may damage reflections because the network cannot learn the exact location of the noises. In order to accommodate the characteristics of erratic noise, we propose using an attention-based network that focuses more on noisy regions. The network outputs an attention map, which shows the spatial distribution probability of erratic noise and a raw noise that may have leakage reflections. To train the network with two kinds of outputs, we use two different loss functions to optimize the network. The erratic noises in the specified area can be extracted by multiplying the attention map and the raw noise, and the reflections in areas without noise are preserved. For generating the training set, we proposed a shuffled noise strategy, which starts from the inaccurate denoised data with the conventional method. Only the noisy data are available in the whole denoising workflow. The network can learn the noise features effectively with the shuffle noise strategy and can achieve better denoising effect than that based on the conventional method. In addition, the denoising capability of the network can be controlled manually by filtering the attention map with probability, and the denoising effect can be further improved through recovering the leaked signals. Synthetic and field data examples show that the proposed method has potential for field erratic noise attenuation. [ABSTRACT FROM AUTHOR]

Nonspecific lipid transfer proteins (nsLTPs) play vital roles in lipid metabolism, cell apoptosis and biotic and abiotic stresses in plants. However, the distribution of nsLTPs in Arachis duranensis has not been fully characterized. In this study, we identified 64 nsLTP genes in A. duranensis (designated AdLTPs), which were classified into six subfamilies and randomly distributed along nine chromosomes. Tandem and segmental duplication events were detected in the evolution of AdLTPs. The K s and ω values differed significantly between Types 1 and D subfamilies, and eight AdLTPs were under positive selection. The expression levels of AdLTPs were changed after salinity, PEG, low-temperature and ABA treatments. Three AdLTPs were associated with resistance to nematode infection, and DOF and WRI1 transcription factors may regulate the AdLTP response to nematode infection. Our results may provide valuable genomic information for the breeding of peanut cultivars that are resistant to biotic and abiotic stresses. • AdLTPs were classified into 6 subfamilies and were randomly distributed along 9 chromosomes. • The tandem and segmental duplication events were detected in the evolution of AdLTPs. • The K s and ω values were significantly different between Type 1 and D subfamilies. • The expression level of AdLTPs was changed after salinity, PEG, low temperature and ABA treatments. • DOF and WRI1 transcription factors may regulate the AdLTP response to nematode infection. [ABSTRACT FROM AUTHOR]