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Acoustic impedance (AI) is a key parameter frequently used for characterizing reservoirs in the oil and gas industry. The absolute AI can be divided into background and high-wavenumber components, which are related to the background velocity and migrated reflectivity, respectively. The relative AI derived from the migrated images is commonly used, but it is generally limited by the wavenumber range of the migrated image. Owing to the advantage of deep learning methods in determining the relationships among data sets, a learning-based AI inversion method that fuses the background model and the migrated reflectivity is proposed to determine the absolute AI. We first acquire learning labels using a synthetic model, and the initial parameters of a convolutional neural network are obtained by training the mapping from the known background AI and the reflectivity to the labels. We then establish a transfer learning scheme to refine the mapping network parameters using the target data. Finally, the target field data are processed using the refined network. The Marmousi model is used as the label for pretraining, and the synthetic Sigsbee2A model and field data are used for validation. Numerical tests demonstrate the effectiveness of the proposed method for absolute AI fusion, and the transfer learning scheme performs well to constrain the inverted AI that has decent consistency with the background model and reflectivity of the target data.

Objective: A prognostic model for iron death-related LncRNA in lung adenocarcinoma was constructed through the TCGA cancer database, and immune correlation was analyzed for patients in the high-low risk group in the model. This study has certain guiding significance for the diagnosis and treatment of lung adenocarcinoma, and has the necessity of research. Methods: Get gene expression data and clinical data of lung adenocarcinoma tumor tissue and normal lung tissue in TCGA database, select LncRNA, adopt single and multivariate Cox risk regression model and establish the LncRNA prognosis risk prediction model, score patients according to the model, and compare high risk and low risk groups to obtain immune related differences between high and low risk groups. Results: A total of 55 differential LncRNAs related to the prognosis of iron death were obtained. Based on univariate and multivariate Cox regression analysis, a multivariate prognostic risk prediction model was established for 16 LncRNAs. A high patient risk score suggests a poor prognosis. Immunocorrelation analysis showed that in the high and low risk groups: Dendritic cells, B cells, mast cells and other immune cells (P < 0.001), APC co-inhibition, APC co-stimulation, chemotactic factor receptor, immune checkpoint and other immune functions (P < 0.01), IDO2, HHLA2, CD276, BTLA and other immune checkpoint related genes (P < 0.001) had significant statistical difference. Conclusion: The prognostic model based on 16 iron death-related LncRNA can well predict the prognosis of lung adenocarcinoma patients, and the iron death-related LncRNA involved in the model construction obtained in the study may be related to immune infiltration levels, and may even be a novel target for immune checkpoint inhibitor therapy.

Deep-learning (DL) technology has emerged as a new approach for seismic data interpolation. DL-based methods can automatically learn the mapping between regularly subsampled and complete data from a large training data set. Subsequently, the trained network can be used to directly interpolate new data. Therefore, compared with traditional methods, DL-based methods reduce the manual workload and render the interpolation process efficient and automatic by avoiding the selection of hyperparameters. However, two limitations of DL-based approaches exist. First, the generalization performance of the neural network is inadequate when processing new data with a different structure compared to the training data. Second, the interpretation of the trained networks is very difficult. To overcome these limitations, we have combined the deep neural network and classic prediction-error filter (PEF) methods, proposing a novel seismic data dealiased interpolation framework called prediction-error filters network (PEFNet). The PEFNet designs convolutional neural networks to learn the relationship between the subsampled data and the PEFs. Thus, the filters estimated by the trained network are used for the recovery of missing traces. The learning of filters enables the network to better extract the local dip of seismic data and has a good generalization ability. In addition, PEFNet has the same interpretability as traditional PEF-based methods. The applicability and the effectiveness of our method are demonstrated here by synthetic and field data examples.

Seismic images of Earth's subsurface are essential for oil and gas exploration. Gaussian beam migration is popular for seismic imaging owing to its flexibility and efficiency of implementation. However, the practical use of classic Gaussian beam migration is limited for complicated structures such as subsalt because of poor seismic illumination in those areas. We propose an illumination‐compensated Gaussian beam migration under the framework of least‐squares migration, enhancing the subsalt imaging effectively. A novel scheme based on the Born modelling and migration of Gaussian beam is developed to estimate the point spreading function, with which the illumination compensation can be efficiently implemented in the local wavenumber domain. As the aim of least‐squares migration, the proposed illumination‐compensated Gaussian beam migration produces true‐amplitude images in subsalt areas, which facilitates the seismic interpretation of subsalt structures. The total computational cost of the proposed method includes one Born modelling process and two conventional Gaussian beam migrations, and thus it is much more efficient than the classic least‐squares migration which requires multiple iterations. Numerical examples have demonstrated the effectiveness of the adaptive strategies, manifesting applicable potential in oil and gas exploration of subsalt targets.

Bear bile powder (BBP) is a widely used traditional Chinese medicine (TCM), and bile acids (BAs) are the main active components in BBP. Due to the scarcity of BBP resources, adulterations often occur in the market. Conventional methods to distinguish them are usually complicated and time-consuming. To enhance effectiveness and accuracy, a rapid and rough analytical method is desperately needed.In this study, a rapid strategy using chip-based nano-electrospray ionization tandem mass spectrometry (nano-ESI-MS/MS) was established to distinguish BBP from other sources of bile powder (BP). In addition, the results were further verified by ultra-high-performance liquid chromatography combined with high-resolution mass spectrometry (UPLC/MS).The precision of the chip-based nano-ESI-MS/MS method was validated to be acceptable with relative standard deviation (RSD)15%. The distinction between BBP and other sources of BP, including common adulterants of pig bile powder (PBP), cattle bile powder (CBP), sheep bile powder (SBP), and chicken bile powder (CkBP), can be observed in the spectra. By using orthogonal partial least-squares discriminant analysis (OPLS-DA), more potential m/z markers were investigated. A BAs-related m/z marker of 498.3 was discovered as a typical differential molecular ion peak and was identified as tauroursodeoxycholic acid (TUDCA) and taurochenodeoxycholic acid (TCDCA) in BBP.The proposed strategy has simple sample pretreatment steps and significantly shortened analysis time. As an emerging technology, chip-based nano-ESI-MS not only provides a reference for the rapid distinction of adulterated Chinese medicines, but also provides some insights into the identification of other chemicals and foods.

The metabolic profiles of Tanreqing injection, which is a traditional Chinese medicine recommended for complementary administration to treat a novel coronavirus, have remained unclear, which inhibit the understanding of the effective chemical compounds of Tanreqing injection. In this study, a sensitive high-performance liquid chromatography quadrupole time-of-flight mass spectrometry method was used to identify the compounds and metabolites in various biosamples, including plasma, bile, liver, lung, kidney, urine, and feces, following the intravenous administration of Tanreqing injection in rats. A total of 89 compounds were characterized in the biosamples of Tanreqing injection-treated rats including 25 precursor constituents and 64 metabolites. Nine flavonoid compounds, twelve phenolic acids, and four iridoid glycosides were identified in the rats. Their metabolites were mainly produced by glucuronidation, deglucuronidation, glycosylation, deglycosylation, methylation, demethylation, N-heterocyclisation, sulphation, dehydroxylation, decarboxylation, dehydration, hydroxylation, and corresponding recombination reactions. This study was the first to comprehensively investigate the metabolic profile of Tanreqing injection and provides a scientific basis to further elucidate the pharmacodynamic material basis and therapeutic mechanism of Tanreqing injection.

Conventional seismic migrations are often suitable for structural imaging of the subsurface, but have quantitative limitations. We proposed an imaging strategy for obtaining true-amplitude imaging results within the framework of least-squares migration. Gaussian beam migration and demigration operators are developed to produce two images, which are used to estimate the approximate inverse of the Hessian in the imaging domain by introducing a non-stationary filter operator. By applying an image correction with the estimated inverse Hessian to a conventional Gaussian beam migration result, the resulting image is much closer to the true-amplitude image. Benefiting from the Gaussian beam propagator, which is a one-way beam propagator, the proposed method has more flexibility than the method based on two-way wave equations, and it can be adapted to higher frequency imaging with less computational cost compared to a two-way wave equation method. Some numerical examples are shown to demonstrate the effectiveness and efficiency of the proposed method.

Background China Professional Football League has been in operation for nearly 30 years and has attracted more and more attention from the public and society. China Professional Football League has promoted the professional and commercial development of Chinese football and attracted a large number of fans. At present, Chinese professional football clubs are struggling, but they still get the support of many fans. The development of any club is inseparable from the support of loyal fans, who are the basis of the development of the club. Only with many loyal fans can the club have long-term and strong vitality. Only by making outstanding achievements, excavating the economic benefits of the club, establishing and maintaining the club culture and cultivating a large number of loyal fans, can the club bring profound heritage and solid economic benefits to the club. Football club has a large number of loyal fans, which is the cornerstone of the sustainable and vigorous development of professional football league. However, due to many factors, the loyalty of fans has decreased, which is related to the evaluation of pursuit by the Internet. Subjects and Methods Based on this, this paper studies the loyalty of Chinese football fans, takes the fans of China Henan sslm football club as the research object, and analyzes the current situation of China Henan sslm football club and its fans. Through the research, it is found that with the continuous development of the Internet, public emotional behavior will have an important impact on football public opinion. There are two directions: one is the top-down impact, the other is the bottom-up impact; It is mainly manifested in two typical ways: social mobilization and emotional social struggle in football public opinion. The main expressions of their emotional behavior are: weakness, anger, sadness and anger, etc. On the basis of combing the collective behavior and emotional struggle, the research finds that the communication framework of emotional behavior mainly includes the communication paths of discourse co meaning, identity co meaning and emotional co meaning; Functional analysis includes target function, attribution function and ideographic function. From the tendency of public sentiment, football public opinion shows criticism, populism, nationalism, pragmatism, patriotism and justice. The social expression of public sentiment in microblog public opinion includes the spiral phenomenon of silence, butterfly effect, herd effect, resentment and so on. From the perspective of psychology, the public emotions in football public opinion are fear, anxiety, anger and sadness, while in terms of expression, the public express their feelings through direct expression and folk language. Results Using the methods of literature review, questionnaire survey and mathematical statistics, this paper analyzes the dimension of fan loyalty and constructs the fan loyalty model of sslm football club in Henan Province. Conclusion According to the research, fan participation is divided into three dimensions: pleasure participation, symbolic participation and central participation; Product attributes related to the club, such as team performance, star players, head coach and team management, are positively correlated with club fan loyalty; Logo design, stadium, game quality and team tradition are also positively correlated with club fan loyalty; There is a significant positive correlation between fans' attitude loyalty and fans' behavior loyalty. The author believes that club brand communication is an important factor for fans to participate in the development of football clubs. It is necessary for football clubs to strengthen the publicity of fans, let more fans know the real situation of the club, increase the exposure of football clubs and improve the satisfaction of fans. Secondly, the importance of the performance of the club team in the correlation of product related attributes is self-evident. The club needs to constantly improve the performance of the team in the game and win more attention from the outside world. Therefore, the club must actively train star players, select excellent coaches and lead the team to achieve better results. The club needs to strengthen the development of Lenovo products again, which have attributes unrelated to products. The design of club logo, the construction and maintenance of competition venues, the quality of competition and the tradition of the team are the basis of influencing the loyalty of fans. The club needs to continuously strengthen the product research and development of Lenovo's non product related attributes. Work can not only bring economic benefits to the club, but also shape the club's fan culture and further improve the club's fan loyalty. Finally, the club needs to provide a space for fans to communicate: there is a positive correlation between fans' escape, recognition, acceptance, nostalgia, regional glory and fans' loyalty. Let fans find suitable community organizations, form common interests and help improve the loyalty of club fans. Acknowledgements This research was supported by the general project of philosophy and Social Sciences Planning of Zhejiang Province (20NDJC184YB), and the Fundamental Research Funds for Zhejiang Provincial Universities and Research Institutes (2021R005).

Ethnopharmacological relevance Natural bear bile powder (NBBP) is a traditional Chinese medicine used for treating liver dysfunction. Cultured bear bile powder (CBBP), which is produced using biotransformation of chicken bile, acts as an appropriate substitute for NBBP when treating cholestatic liver injury. Aim of the study To investigate the molecular mechanisms underlying the hepatoprotective effects of CBBP in an α-naphthylisothiocyanate (ANIT)-induced cholestatic mouse model. Materials and methods Cholestatic mice were pretreated with CBBP or NBBP via oral gavage once a day for two weeks. Their blood biochemistry and liver histopathology were then evaluated using standard protocols. Western blot analyses, real-time polymerase chain reaction, and immunohistochemistry were used to evaluate changes in the protein levels and gene expression profiles of factors associated with hepatic inflammation and apoptosis in cholestatic mice. Results CBBP significantly decreased the serum indices of liver injury, and ameliorated neutrophil infiltration and hepatocyte necrosis within liver tissue of cholestatic mice. Expression of the inflammatory factors, such as tumor necrosis factor-α, interleukin-1β (IL-1 β), IL-6, monocyte chemoattractant protein-1, and intercellular adhesion molecule 1, was significantly reduced in CBBP-treated cholestatic mice. Moreover, proteins involved in the toll-like receptor 4/myeloid differentiation factor 88/nuclear factor-kappa B (TLR4/Myd88/NF-κB) signaling pathway, such as CD14, TLR4, Myd88, and NF-κB, that were increased in cholestatic mice, were downregulated by CBBP. Meanwhile, increased expression of the apoptosis-related proteins, caspase-3 and Bax, in cholestatic mice was reversed by CBBP treatment. Conclusion CBBP treatment alleviates ANIT-induced cholestasis and liver injury by reducing hepatocyte inflammation and apoptosis.

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.

Fault interpretation is an important part of seismic structural interpretation and reservoir characterization. In the conventional approach, faults are detected as reflection discontinuity or abruption and are manually tracked in post-stack seismic data, which is time-consuming. In order to improve efficiency, a variety of automatic fault detection methods have been proposed, among which widespread attention has been given to deep learning-based methods. However, deep learning techniques require a large amount of marked seismic samples as a training dataset. Although the amount of synthetic seismic data can be guaranteed and the labels are accurate, the difference between synthetic data and real data still exists. To overcome this drawback, we apply a transfer learning strategy to improve the performance of automatic fault detection by deep learning methods. We first pre-train a deep neural network with synthetic seismic data. Then we retrain the network with real seismic samples. We use a random sample consensus (RANSAC) method to obtain real seismic samples and generate corresponding labels automatically. Three real 3D examples are included to demonstrate that the fault detection accuracy of the pre-trained network models can be greatly improved by retraining the network with a few amount of real seismic samples.

Fault interpretation is a key step for seismic structural interpretation and reservoir characterization. In conventional approach, faults are identified as reflection discontinuity or abruption in post-stack seismic data. In order to highlight fault, several fault attributes by measuring waveform similarity or diversity have been proposed. However, noise and stratigraphic features can also be highlighted in a fault attribute image. We propose a forward and backward diffusion method to enhance the fault image so that the noisy features which unrelated to faults are suppressed and the fault features are sharpened. We first estimate the planarity (linearity in 2D) and fault orientation (strike and dip) of every sample in fault image by using a PCA-based algorithm. Combining the planarity and fault orientation, we then construct fault probability map based on the assumption that faults can be seemed as relatively steep planes in a small spatial window. Thanks to the fault probability map, we can adaptively switch the diffusion process from a forward mode to a backward mode. Forward diffusion force is used to suppress the non-fault features within the low fault probability area and backward diffusion force is used to enhance the fault features within the high fault probability area. A real 3D example is included to demonstrate that this method can effectively suppress the noise and sharpen the faults. Furthermore, automatic fault tracking on an enhanced fault image can get more accurate result than on an original fault image.

The generation of angle-domain common imaging gathers (ADCIGs) from reverse time migration (RTM) is essential for velocity updates, migration quality control and amplitude variation with an...

Bear bile is a valuable medicinal material used in traditional Chinese medicine for over 2000 years. However, developing a substitute has become necessary because of protection measures for this endangered species. The ingredients of in vitro cultured bear bile powder (CBBP) include tauroursodeoxycholic acid (TUDCA), taurochenodeoxycholic acid (TCDCA), ursodeoxycholic acid (UDCA) and chenodeoxycholic acid (CDCA, and it has pharmacological properties that are similar to those of natural bear bile powder (NBBP). In this study, the pharmacokinetic parameters of both CBBP and NBBP were measured in rats with a new surrogate analyte LC-MS method using stable isotopes as surrogate analytes (D4-TUDCA, D4-TCDCA, D4-UDCA and D4-CDCA) with response factors validated in authentic matrix (plasma) for simultaneously monitoring the authentic analytes (TUDCA, TCDCA, UDCA and CDCA). The method validation was satisfactory for the linear regression (r, 0.9975-0.9994), precision (RSD intra-day, 0.72-9.35%; inter-day, 3.82-9.02%), accuracy (RE, -12.42-5.67%) and matrix effect (95.53-99.80%), along with analyte recovery (95.90-98.82%) and stability (89.48-101.81%) of surrogate analytes, and precision (RSD intra-day, 1.06- 11.51%; inter-day, 2.23- 11.38%), accuracy (RE, -7.40-10.76%) and stability (87.37-111.70%) of authentic analytes. We successfully applied this method to evaluate the pharmacokinetics of CBBP and NBBP in rats, which revealed the critical in vivo properties of both bear bile preparations.

Ethnopharmacological relevance Natural bear bile powder (NBBP) has been used to treat seizures for thousands of years, but its application is greatly restricted due to ethical reasons. Cultured bear bile powder (CBBP), which is produced by biotransformation, may be an appropriate substitute for NBBP. However, the anti-convulsant effects of CBBP and its mechanisms remain unclear. Aim of the study This study aimed to investigate the anti-convulsant effects and possible mechanisms of CBBP in a febrile seizure (FS) rat model. Materials and methods FS was induced by placing the rats in a warm water bath (45.5 °C). The incidence rate and latency of FS, and hematoxylin-eosin staining (HE) were conducted for neurological damage. The levels of 4 bile acids and 8 main neurotransmitters in vivo were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The expression of bile acid related transports, neurotransmitter receptors, inflammatory factors, neurotrophic factors and glial fibrillary acidic protein (GFAP) in hippocampal tissues were detected by real-time PCR, western blotting, and immunohistochemistry. Results Pre-treatments with CBBP and similarly, NBBP, significantly reduced the incidence rate and prolonged the latency of FS. Additionally, CBBP alleviated the histological injury induced by FS in the rat hippocampus tissue. LC-MS/MS analyses revealed that CBBP markedly increased the levels of tauroursodeoxycholic acid (TUDCA), taurochenodeoxycholic acid (TCDCA), ursodeoxycholic acid (UDCA), and chenodeoxycholic acid (CDCA) in FS rats. Furthermore, the content of gamma-aminobutyric acid (GABA) was up-regulated in rats pre-treated with CBBP whereas GFAP was down-regulated. CBBP also significantly suppressed the expression of interleukin -1β (IL-1β), tumor necrosis factor α (TNF-α), nuclear factor kappa B (NF-κB), and brain-derived neurotrophic factor (BDNF) and its TrkB receptors, and improved the expression of GABA type A receptors (GABAAR) and farnesoid X receptors (FXR). Conclusions The present study demonstrated that CBBP had anti-convulsant effects in a FS rat model. CBBP may protect rats against FS, probably by up-regulating FXR, which was activated by increasing brain bile acids, up-regulating GABAergic transmission by inhibiting BDNF-TrkB signaling, and suppressing neuroinflammation by inhibiting the NF-κB pathway.

Ray-based wave propagators are widely applied in seismic migration due to implementation flexibility and computational efficiency. The classic ray theory that under the high-frequency assumption requires sufficiently smooth velocity models, which limits the applications of ray-based methods because seismic waves are band-limited. Besides, it is desirable to extend seismic propagation and migration to general anisotropic case since the reality of subsurface. We adapt a wavelength-dependent smoothing (WDS) operator for transversely isotropic media with a vertical symmetry axis (VTI), which considers both characteristics of band-limited wave propagation and local anisotropic heterogeneity. Frequency-dependent traveltimes are computed with the WDS models by using an anisotropic dynamic programming approach. Then, a wavelength-dependent Fresnel beam propagator is constructed based on the frequency-dependent traveltimes. Analysis of traveltime fields demonstrates that wavelength-dependent Fresnel beam propagator can provide accurate wave propagating directions and traveltimes. We develop a wavelength-dependent Fresnel beam migration (WDFBM) method based on the wavelength-dependent Fresnel beam propagator for VTI media, which generates angle domain common imaging gathers (ADCIGs) efficiently. Numerical examples are included to demonstrate the effectiveness of our approach, which shows its potential application for subsequent velocity estimation via ADCIGs.

The ray-tracing technique under the high-frequency assumption has been widely used in seismic wave propagation and migration. However, the practical use of conventional ray tracing is limited in complicated media especially when seismic data are band limited. Besides, the ray-tracing method also suffers from shadow zones in complex media. To alleviate these problems, we have developed a band-limited beam propagator and we apply it in seismic wave propagation and migration, which is flexible to implement and can be friendly to extract angle gathers. To derive the band-limited beam propagator, the band-limited ray-tracing method is adopted to compute the central ray of the beam. These rays in the first Fresnel zone are weighted to obtain the band-limited ray based on the assumption of a local plane wave. Then, the band-limited ray is extended to the band-limited beam propagator using the paraxial approximation. Because the beam propagator has a certain beam width perpendicular to the central ray, it has better illumination than the conventional ray-tracing method, and it could partially alleviate the problem of shadow zones. Finally, we use the band-limited beam propagator to develop a band-limited beam migration and analyze the angle gathers in complicated areas. Numerical examples on synthetic models indicate that the proposed band-limited beam propagator outperforms the conventional ray method in terms of illumination. Its applications in migration determine that it could enhance the imaging quality and produce better angle gathers in a complex area.

A fractured area, such as a fault area, usually induces orthorhombic anisotropy. Ignoring orthorhombic anisotropy may degrade the subsurface image by creating a well mistie and blurring the image. Traveltime computation is essential for many processing techniques, such as depth imaging and tomography. Solving the ray-tracing system and eikonal equation are two popular methods for traveltime computation in isotropic media. However, because the ray-tracing system becomes complex and the eikonal equation becomes highly nonlinear, their applications in orthorhombic media become complex. We have developed an alternative 3D traveltime computation method in orthorhombic media based on dynamic programming. To avoid solving the complex ray-tracing system and nonlinear eikonal equation, it adopts an explicitly expressed group velocity from the moveout approximation to describe the propagation of the wavepath and computes the traveltime by Fermat’s principle. Similar to depth extrapolation, it computes the traveltime from one depth to the next depth and does not suffer from a shadow zone. Besides, three strategies of traveltime computation are proposed to deal with different geologic scenarios. Because classic dynamic programming (i.e., the first strategy) computes all possible wavepaths (i.e., 24 wavepaths) across one spatial location, it may be computationally intensive. Based on the idea of wavefield decomposition (e.g., upgoing and downgoing), the second and third strategies simplify the traveltime computation and reduce the computational cost. Numerical examples on the vertical and tilted orthorhombic models indicate that the traveltime contour obtained by our method matches well with the wavefront extrapolated from the wave equation. Our method can be applied in depth imaging and tomography.

Full-waveform inversion (FWI) reconstructs the subsurface properties from acquired seismic data via minimization of the misfit between observed and simulated data. However, FWI suffers from considerable computational costs resulting from the numerical solution of the wave equation for each source at each iteration. To reduce the computational burden, constructing supershots by combining several sources (aka source encoding) allows mitigation of the number of simulations at each iteration, but it gives rise to crosstalk artifacts because of interference between the individual sources of the supershot. A modified Gauss–Newton FWI (MGNFWI) approach showed that as long as the difference between the initial and true models permits a sparse representation, the $$\ell _1$$ -norm constrained model updates suppress subsampling-related artifacts. However, the spectral-projected gradient $$\ell _1$$ (SPG $$\ell _1$$ ) algorithm employed by MGNFWI is rather complicated that makes its implementation difficult. To facilitate realistic applications, we adapt a linearized Bregman (LB) method to sparsity-promoting FWI (SPFWI) because of the efficiency and simplicity of LB in the framework of $$\ell _1$$ -norm constrained optimization problem and compressive sensing. Numerical experiments performed with the BP Salt model, the Marmousi model and the BG Compass model verify the following points. The FWI result with LB solving $$\ell _1$$ -norm sparsity-promoting problem for the model update outperforms that generated by solving $$\ell _2$$ -norm problem in terms of crosstalk elimination and high-fidelity results. The simpler LB method performs comparably and even superiorly to the complicated SPG $$\ell _1$$ method in terms of computational efficiency and model quality, making the LB method a viable alternative for realistic implementations of SPFWI.

Anelastic attenuation often causes amplitude decrease and phase distortion in seismic data. Seismic migration of such attenuated data often yields poor images of subsurface structures, especially under high-attenuation areas. Previous studies have been focusing on the compensation of attenuation to obtain higher resolution images. However, it is still challenging to achieve a good ray-based Q-compensated migration for seismic data. In this paper, we firstly introduce the multiscale Gabor transform to obtain a local time-frequency data for Q-dependent compensation, where the local lengths of filtering windows varied from frequencies are computed automatically. Then, we propose an efficient Q-compensated beam migration based on the multiscale Gabor transform. The attenuation of seismic wave is compensated based on Q-dependent traveltimes before imaging loop, and the Q-dependent traveltimes are purchased using the spatial variant Q models within imaging loop. The proposed Q-compensated migration has similar computational cost as the traditional beam migrations, because the frequency integral loop in the imaging process is solved in our implementation scheme. Both synthetic and real data examples show the effectiveness and efficiency of the proposed method.

Reservoir parameter estimation is one of the goals of amplitude-versus-angle (AVA) inversion and angle-domain common image gathers are the basis of AVA inversion. Therefore, the accuracy of kinematic and kinetic information on angle gathers is very important for reservoir characterization. Reverse time migration is one of the most physically accurate migration method. Generating angle gathers from reverse time migration with the Poynting vector method is very efficient. However, due to inaccurate angle measurement and uneven illumination, angle gathers calculated by the Poynting vector method are often not suitable for AVA inversion. In this paper, we propose an efficient method of angle gathers with accurate angular information and amplitude from reverse time migration. We firstly decompose source and receiver wavefield to their up-going and down-going parts by using analytic wavefield. We calculate propagation directions for source down-going wavefield and receiver up-going wavefield by the Poynting vector method and form the angle gathers with these angle information and decomposed wavefield. To reduce memory storage and improve computational efficiency, we decompose wavefield at excitation amplitude time by using a local spatial Fourier transform. We also use a spatial smoothed Poynting vector to improve the stability of angle measurement. We apply an illumination compensation image condition to recover the true amplitude. Numerical examples on Marmousi model and the SEAM two-dimensional (2D) model demonstrate the advantages of our proposed method. The angle gathers based on our method are cleaner with more focus on events energy and better continuity, suffering from less low-frequency noise in the shallow parts and with a distinct cutoff at large angle where reflection terminates. At last, we demonstrate the effectiveness of proposed method on a 2D marine field data example.

Natural bear bile powder (NBBP) is a famous traditional medicine and has been widely used in clinic. However, access to the sources of bear bile is restricted; hence, it is essential to discover new substitutes for NBBP. Cultured bear bile powder (CBBP) is transformed from chicken bile and contains main ingredients as to NBBP. In the present study, the effect and potential mechanism of action of CBBP on cholestatic liver injury in-naphthylisothiocyanate (ANIT)-induced mouse model was explored using metabolomics. CBBP treatment ameliorated impaired hepatic dysfunction and tissue damage that induced by ANIT. Metabolomics showed there were 28 different metabolites induced by ANIT as compared with control mice, and 18 of which was reversed by CBBP. Pathway analysis revealed that those 18 metabolites are mainly involved in bile acid (BA) biosynthesis and D-glutamine and D-glutamate metabolism. Further LC-MS/MS analysis showed that CBBP and NBBP both reduced serum and liver levels of BAs, but increased their biliary levels. Additionally, CBBP and NBBP upregulated expression of BA efflux transporters, Mrp2, Mrp3, and Mrp4, and metabolic enzymes, Cyp2b10 and Ugt1a1 of liver tissue of cholestatic mice, increased the BA excretion and metabolism. Moreover, CBBP and NBBP treatment upregulated GCLc/GCLm expression, and restored glutathione metabolism. In conclusion, the protective effects of CBBP against cholestatic liver injury were similar to those of NBBP. Mechanistically, both CBBP and NBBP reversed the disruption in homeostasis of BAs and glutathione, alleviating damage to hepatocytes.

Natural bear bile has been used for liver disease in East Asia for thousands of years. However, its use has restrictions. In the current study, the therapeutic effects and potential mechanisms of cultured bear bile powder (CBBP) against hepatic fibrosis were evaluated in a dimethylnitrosamine (DMN)-induced rat model. CBBP treatment significantly improved DMN-induced hepatic necrosis and inflammatory infiltration. Additionally, CBBP remarkably alleviated the increased hepatic collagen content and expression of alpha-smooth muscle actin. Serum metabolomics revealed that 14 serum metabolites, including docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) were decreased in DMN-treated rats, which was reversed by CBBP. Pathway analyses revealed that the main metabolic pathways affected by CBBP were related to fatty acid biosynthesis and metabolism, and biosynthesis of unsaturated fatty acids. EPA and DHA are ligands of peroxisome proliferator activated receptors (PPARs). CBBP treatment significantly stimulated liver mRNA and protein expression of PPARα and PPARγ. CBBP also markedly increased liver expression of PPARα target genes, which are involved in fatty acid β-oxidation, and down-regulated IL-6, a downstream inflammatory gene of PPARγ. In conclusion, CBBP has the potential to attenuate liver fibrosis and its mechanism involves the promotion of the liver expression of PPARα and PPARγ. Our results may help in the development of a novel substitute for bear bile and therapeutic strategies for fibrotic liver diseases.

Tanreqing capsule (TRQC) is a formulation frequently used in traditional Chinese medicine to treat pyrexia, cough, expectoration and pharyngalgia. Since the pharmacological action of traditional Chinese medicines is closely related to their complex and diverse constituents, understanding the exact composition of TRQC is important to elucidate its clinical effectiveness and mechanism of action as well as to establish quality control methods and resolve safety issues. Herein, we employed high-performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry for the simultaneous quantitation of 23 bioactive compounds in five batches of TRQC; the analytes could be categorized into five types: organic acids (seven compounds), flavonoids (10 compounds), iridoids (two compounds), phenylethanoid glycosides (two compounds) and bile acids (two compounds). The calibration curves for all analytes showed good linearity (r > 0.9953), and the inter- and intra-day precisions did not exceed 4.94 and 4.97%, respectively. The recoveries varied from 90.47% to 109.80%; the corresponding relative standard deviations (RSDs) did not exceed 4.94%; and the repeatability (RSD < 4.72%) and stability (RSD < 4.88%) were also within acceptable limits. Thus, this study can be viewed as a fundamental reference for setting comprehensive TRQC quality standards.

Increasing performance parameters of hard disk drive (HDD) such as higher capacity and faster data access speed with decreasing physical size make HDD more susceptible to thermal effects. Contact temperature measurement using thermocouple is not suitable for the rotating platter of HDD. Heat analysis using simulation software requires accurate initial parameter setting such as thermal (initial & boundary) conditions of certain regions. Temperature measurement using infrared (IR) system avoids these limitations; it is non-contact, responsive and does not require initial parameter setting. Thermal pattern distribution can be studied from the thermal images. However, emissivity of the target has to be known and calibration of the system is essential for accurate temperature reading. This paper showed that temperature within the HDD increases with ambient temperature and time, but the thermal distribution pattern in the HDD was not affected by different ambient temperatures. Three wall boundary conditions were conducted to study the thermal distribution pattern in the HDD. A solution was then proposed based on the results obtained from the experiments to improve the heat transfer rate and steady state temperature, and reduce the detrimental effects from high thermal generation in future prototypes. Another important finding was that the averaged temperature of the head cap was generally higher compared to that of the disk, as the spindle motor is the primary heat source within the HDD. Heat source analysis of HDD with IR system allows designers to have better visibility of the temperature generated in different components of the HDD. Proper cooling may enhance disk life as well as ensure the stability and integrity of the system.

A series of well-defined conjugated-liquid crystalline (LC) block copolymers containing oligofluorene and side-chain liquid crystalline polymers with cyanobiphenyl moieties were successfully synthesized by atom transfer radical polymerization. The block copolymers were prepared with number-averaged molecular weights (Mn) ranging from 8000 to 16 000 and narrow molecular weight distribution less than 1.20. The chemical structures of these block copolymers were confirmed by 1H NMR, 13C NMR, and FTIR studies. All of the block copolymers exhibited the smectic mesophase as illustrated by differential scanning calorimetry, polarized optical microscopy, and wide-angle X-ray diffraction. A bilayer structure of mesogens was formed in the smectic layer of block copolymers with a thickness of 3.5 nm. The isotropization of the smectic phase increased with the molecular weight and leveled off at Mn = 14 000. The optical properties of these block copolymers in solution and solid-films were investigated comparatively by ...

The severe variations of the time move-out among traces and the low SNR are the direct causes of difficulties in rugged topography field data processing. In this study, we first analysis the causes of move-out among traces and the difference effect on velocity estimation and seismic imaging caused by difference components of the move-out. Then we come to some conclusions: (1) The high wavenumber move-out cannot be dealt with by PSDM and it should be eliminated before imaging (2) The low wavenumber move-out can be incorporated into subsequent velocity estimation and imaging process. Numerical experiment results verify the correctness of our ideas and the effectiveness of our method.

The characteristic wave decomposition (CWD) method is presented, which takes full advantages of the local linear characteristics of the original seismic data. The CWD is performed by double beam forming for a compressed seismic wavefield in the characteristic ray parameter domain. Based on the beam-formed wavefield in characteristic domain, a beam-based characteristic wave imaging method (CWI) is put forward. Due to the flexibility and efficiency to output angle gathers for velocity model building, the CWI is a useful alternative to Kirchhoff and wave-equation migrations. It alters the application of Kirchhoff migration which smears the seismic data in imaging domain along quasi-ellipsoid trajectories and it has the capacity for steep dip reflector imaging with turning waves. In this paper, the CWI method is applied to prestack depth migration in transverse isotropic with a vertical symmetry (VTI) media. Compared with the conventional Kirchhoff migration methods, the proposed CWI method has a theoretical speedup of 1–2 orders. Besides, it can handle low signal-to-noise-ratio (SNR) data and target oriented imaging conveniently, and angle gathers can be produced naturally by CWI. Consequently, the CWI is an efficient technique for large scale seismic imaging and angle gather outputting. The direct mapping scheme from data space to model space establishes the relation between the characteristic data and the subsurface reflector, which can be used for the subsequent tomography conveniently. Some benchmark tests demonstrate the effectiveness of this method.