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Abstract The purpose of this study is to investigate the improvement in visual acuity when applying Rigid Gas Permeable Contact Lenses (RGPCL) compared to spectacles in patients after surgical treatment for infantile nystagmus syndrome (INS). A total of 33 patients with nystagmus who had nystagmus surgery from October 2020 to July 2021 were enrolled and all were fitted RGPCL. SPSS statistical software was used to analyze the difference between best-corrected visual acuity with spectacle(BCSVA) and best-corrected visual acuity with RGPCL (BCRVA) in these patients. According to the refractive status of each eye, it was grouped into 2 groups. We study the difference between its BCSVA and BCRVA in different groups. In our study, BCRVA was significantly better than BCSVA in all participants (P

Abstract Background Salt stress hinders plant growth and production around the world. Autophagy induced by salt stress helps plants improve their adaptability to salt stress. However, the underlying mechanism behind this adaptability remains unclear. To obtain deeper insight into this phenomenon, combined metabolomics and transcriptomics analyses were used to explore the coexpression of differentially expressed-metabolite (DEM) and gene (DEG) between control and salt-stressed wheat roots and leaves in the presence or absence of the added autophagy inhibitor 3-methyladenine (3-MA). Results The results indicated that 3-MA addition inhibited autophagy, increased ROS accumulation, damaged photosynthesis apparatus and impaired the tolerance of wheat seedlings to NaCl stress. A total of 14,759 DEGs and 554 DEMs in roots and leaves of wheat seedlings were induced by salt stress. DEGs were predominantly enriched in cellular amino acid catabolic process, response to external biotic stimulus, regulation of the response to salt stress, reactive oxygen species (ROS) biosynthetic process, regulation of response to osmotic stress, ect. The DEMs were mostly associated with amino acid metabolism, carbohydrate metabolism, phenylalanine metabolism, carbapenem biosynthesis, and pantothenate and CoA biosynthesis. Further analysis identified some critical genes (gene involved in the oxidative stress response, gene encoding transcription factor (TF) and gene involved in the synthesis of metabolite such as alanine, asparagine, aspartate, glutamate, glutamine, 4-aminobutyric acid, abscisic acid, jasmonic acid, ect.) that potentially participated in a complex regulatory network in the wheat response to NaCl stress. The expression of the upregulated DEGs and DEMs were higher, and the expression of the down-regulated DEGs and DEMs was lower in 3-MA-treated plants under NaCl treatment. Conclusion 3-MA enhanced the salt stress sensitivity of wheat seedlings by inhibiting the activity of the roots and leaves, inhibiting autophagy in the roots and leaves, increasing the content of both H2O2 and O2•—, damaged photosynthesis apparatus and changing the transcriptome and metabolome of salt-stressed wheat seedlings.

Picking the reflection horizon is an important step in velocity inversion and seismic interpretation. Manual picking is time-consuming and no longer suitable for current large-scale seismic data processing. Automatic algorithms using different seismic attributes such as instantaneous phase or dip attributes have been proposed. However, the computed attributes are usually inaccurate near discontinuities. The waveforms in the horizontal direction often change dramatically, which makes it difficult to track a horizon using the similarity of attributes. In this paper, we propose a novel method for automatic horizon picking using multiple seismic attributes and the Markov decision process (MDP). For the design of the MDP model, the decision time and state are defined as the horizontal and vertical spatial position on a seismic image, respectively. The reward function is defined in multi-dimensional feature attribute space. Multiple attributes can highlight different aspects of a seismic image and therefore overcome the limitations of the single-attribute MDP through the cross-constraint of multiple attributes. The optimal decision is made by searching the largest state value function in the reward function space. By considering cumulative reward, the lateral continuity of a seismic image can be effectively considered, and the impacts of abnormal waveform changes or bad traces in local areas for automatic horizon picking can be effectively avoided. An effective implementation scheme is designed for picking multiple reflection horizons. The proposed method has been successfully tested on both synthetic and field data.

Random noise and missing seismic traces are common in field seismic data, which seriously affects the subsequent seismic processing flow. The complete noise-free high-dimensional seismic dataset in the frequency–space (f-x) domain under the local linear assumption are regarded as a low-rank tensor, and each high dimensional seismic dataset containing only one linear event is a rank-1 tensor. The tensor CANDECOM/PARAFAC decomposition (CPD) method estimates complete noise-free seismic signals by characterizing high-dimensional seismic signals as the sum of several rank-1 tensors. In order to improve the stability and effect of the tensor CPD algorithm, this paper proposes a linear Radon transform–constrained tensor CPD method (RCPD) by using the sparsity of factor matrix in the Radon domain after high-dimensional seismic signal tensor CPD and uses alternating direction multiplier method (ADMM) to solve the established optimization problem. This proposed method is an essential realization of the high-dimensional linear Radon transform, and the results of synthetic and field data reconstruction prove the effectiveness of the proposed method.

Based on the physical quantity of log data, the accurate identification of oil- and gas-bearing properties may be caused by the prestack inversion of fluid prediction, which will affect the success rate of exploration and development. Prestack data contain more information of amplitude and frequency. Using the frequency-dependent viscoelastic impedance equation and Bayesian inversion framework, the objective function of frequency-dependent elastic impedance inversion can be established to realize the frequency-dependent impedance inversion at different angles. According to the elastic impedance equation of the frequency-varying viscoelastic fluid factor, the relationship between elastic impedance and the frequency-dependent viscoelastic fluid factor is established, and the prestack seismic inversion method of the frequency-dependent viscoelastic fluid factor is studied. However, one of the important factors easily neglected is that we have been using logging data to establish fluid-sensitive parameters and the lithophysical version for fluid identification, so there are differences between logging and seismic frequency bands for fluid identification. The indicator factors with higher sensitivity to fluid can be selected by laboratory measurements. This article applies this method on Luojia oilfield data and verifies this method with log interpretation results, based on the sample of rock physics obtained in a low-frequency rock physics experiment; the technique of dispersion and fluid-sensitive parameters is studied, and the fluid prediction technology of a multifrequency band rock physics template is adopted, which can build the relationship between rock physical elastic parameters and fluid properties by the multifrequency broadband impedance method.

In seismic exploration, obtaining accurate bandwidth of the reflected signal is essential for determining seismic resolution. A portion of the stored energy is attenuated during signal propagation, narrowing the received seismic signal bandwidth. Therefore, compensating the energy attenuation is important for improving the seismic resolution. The current method of compensating absorption and attenuation based on a single channel can only compensate the post-stack data (self-exciting and self-receiving), whereas in practice, seismic waves do not propagate along the self-exciting and self-receiving seismic wave path; the propagation path is complex. The absorption and attenuation depend on the propagation path. The primary methods used for Q-compensation along the propagation paths are one-way wave extrapolation in the Cartesian coordinate system and Gaussian beam Q-compensation migration in the ray-centered coordinate system. However, the large angle limits the one-way wave method, and the Gaussian beam method refers to the high-frequency approximation solution of the two-way wave equation. Therefore, a 15-degree equation in the ray-centered coordinate system is proposed. Seismic waves extrapolate along the ray, which compensates the absorption and attenuation along the real propagation path. The 15-degree equation in the ray-centered coordinate system does not perform high-frequency approximation in the ray beam and has no large angle limit, facilitating the accurate description of local wavefields in the ray beam.

The estimation of the subsurface acoustic impedance (AI) model is an important step of seismic data processing for oil and gas exploration. The full waveform inversion (FWI) is a powerful way to invert the subsurface parameters with surface acquired seismic data. Nevertheless, the strong nonlinear relationship between the seismic data and the subsurface model will cause nonconvergence and unstable problems in practice. To divide the nonlinear inversion into some more linear steps, a 2D AI inversion imaging method is proposed to estimate the broadband AI model based on a broadband reflectivity. Firstly, a novel scheme based on Gaussian beam migration (GBM) is proposed to produce the point spread function (PSF) and conventional image of the subsurface. Then, the broadband reflectivity can be obtained by implementing deconvolution on the image with respect to the calculated PSF. Assuming that the low-wavenumber part of the AI model can be deduced by the background velocity, we implemented the AI inversion imaging scheme by merging the obtained broadband reflectivity as the high-wavenumber part of the AI model and produced a broadband AI result. The developed broadband migration based on GBM as the computational hotspot of the proposed 2D AI inversion imaging includes only two GBM and one Gaussian beam demigraton (Born modeling) processes. Hence, the developed broadband GBM is more efficient than the broadband imaging using the least-squares migrations (LSMs) that require multiple iterations (every iteration includes one Born modeling and one migration process) to minimize the objective function of data residuals. Numerical examples of both synthetic data and field data have demonstrated the validity and application potential of the proposed method.