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To separate seismic interference (SI) noise while ensuring high signal fidelity, we have developed a deep neural network (DNN)-based workflow applied to common-shot gathers (CSGs). In our design, a small subset of the entire to-be-processed data set is first processed by a conventional algorithm to obtain an estimate of the SI noise (from now on called the SI noise model). By manually blending the SI noise model with SI-free CSGs and a set of simulated random noise, we obtain training inputs for the DNN. The SI-free CSGs can be either real SI-free CSGs from the survey or SI-attenuated CSGs produced in parallel with the SI noise model from the conventional algorithm depending on the specific project. To enhance the DNN’s output signal fidelity, adjacent shots on both sides of the to-be-processed shot are used as additional channels of the input. We train the DNN to output the SI noise into one channel and the SI-free shot along with the intact random noise into another. Once trained, the DNN can be applied to the entire data set contaminated by the same types of SI in the training process, producing results efficiently. For demonstration, we applied our DNN-based workflow to 3D seismic field data acquired from the northern Viking Graben of the North Sea and compared it with a conventional algorithm. The studied area has a challenging SI contamination problem with no sail lines free from SI noise during the acquisition. The comparison finds that our DNN-based workflow outperformed the conventional algorithm in processing quality with less noise residual and better signal preservation. This validates its feasibility and value for real processing projects.

To streamline the fast-track processing of large data volumes, we have developed a deep learning approach to deblend seismic data in the shot domain based on a practical strategy for generating high-quality training data along with a list of data conditioning techniques to improve the performance of the data-driven model. We make use of unblended shot gathers acquired at the end of each sail line, to which the access requires no additional time or labor costs beyond the blended acquisition. By manually blending these data, we obtain training data that are fully adapted to the given survey, while having good control of their ground truth. Furthermore, we train a deep neural network using multichannel inputs that include adjacent blended shot gathers as additional channels. The prediction of the blending noise is added in as a related and auxiliary task with the main task of the network being the prediction of the primary-source events. Blending noise in the ground truth is scaled down during the training and validation process due to its excessively strong amplitudes. As part of the process, the to-be-deblended shot gathers are aligned by the blending noise. Implementation of field blended-by-acquisition data demonstrates that introducing the suggested data conditioning steps can considerably reduce the leakage of primary-source events in the deep part of the blended section. The complete proposed approach performs almost as well as a conventional algorithm in the shallow section and finds a great advantage in efficiency. It performs slightly worse for larger traveltimes, but it still removes the blending noise efficiently.

Estimating the far-field source signature has always been an important part of seismic processing. However, estimating the source signature from an air-gun array is difficult because of the complex interaction between the air bubble oscillations from each air gun, the state of the sea surface, variations in air pressure, the air guns’ geometry, etc. Removing the bubble noise is important because proper seismic imaging requires a zero-phased, spiky wavelet. Debubbling has conventionally been done by deconvolution using an (assumed) known source signature. Several methods to estimate the signature and debubble the data have been implemented, for instance, source modeling or using near-field hydrophone measurements. We describe an alternative approach using a convolutional neural network for debubbling. The network is trained on real data containing a large range of source signatures to make the network robust and adaptive to signature variations. If the signature in the test data is equal to one of the signatures used in the training, the network performs well. In addition, if the signature changes in the middle of a sail line, the network can adapt to this change. Moreover, we investigate the network’s sensitivity to changing geology within a survey and on two different surveys on the Norwegian Continental Shelf (NCS). If the test data are from geology similar to the training data, the network performs better than if not. Even when applied to a different part of the NCS, the network is still able to remove most of the bubble noise.

This paper examines translation and transcultural remembrance of the 1918–19 Great Influenza or the more often yet mistakenly called “Spanish flu” for lessons to combat COVID-19 in Chinese online media. It presents a case study of “covert transediting” in the Shanghai Observer, i.e., a journalistic opinion that interweaves Chinese renderings of selected excerpts from four British and American media texts. With an integrated approach to travelling memory (Erll, Astrid. 2011. Travelling memory. Parallax 17(4). 4–18) via transediting, our analysis shows that the transeditor does not only choose source texts in terms of media and content, but also selectively reframes the “Spanish flu” memory for lesson learning in conjunction with the mainstream Chinese discourse and practice of COVID-19 control. It unveils how the Great Influenza is thus transculturally remembered in more simplified and ideologically agreeable manners through omission, summary translation, and transformation, while faithful transference is not sheerly dispensed. We contend that transcultural remembrance via transediting is a purposive, socio-culturally shaped practice involving interrelations and interactions of manifold factors, and we need a more complex approach to the politics of memory in translation. Journalistic translation, which is mostly covert and transediting, is among different genres of translation that shape transcultural memory;more research in their interplay is called for. [ABSTRACT FROM AUTHOR] Copyright of Multilingua is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Developing green-safe, efficient and recyclable catalysts is crucial for the chemical industry. So far, organic photocatalysis has been proved to be an environmentally friendly and energy-efficient synthetic technology compared with traditional metal catalysis. As a versatile catalytic platform, hyper-crosslinked polymers (HCPs) with large surface area and high stability are easily prepared. In this report, we successfully constructed two porous HCP photocatalysts (TZ-HCPs) featuring s-tetrazine units and surface areas larger than 700 m2 g−1 through Friedel–Crafts alkylation reactions. The rational energy-band structures and coexisting micro- and mesopores endow TZ-HCPs with excellent activities to realize the green synthesis of benzimidazoles (28 examples, up to 99% yield, 0.5–4.0 h) in ethanol. Furthermore, at least 21 iterative catalytic runs mediated by TZ-HCP1D were performed efficiently, with 96–99% yield. This study of TZ-HCPs sheds light on the wide-ranging prospects of application of HCPs as metal-free and green photocatalysts for the preparation of fine chemicals.

Aim. Salivary gland adenoid cystic carcinoma (SACC) is the second highest incidence of malignant salivary gland tumor. The purpose of this study was to establish nomograms combined with SACC patients based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods. Patients with SACC were included in the SEER ∗ Stat Database from 2004 to 2016. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to filter potential prognostic clinical variables. Multivariate analysis from the Cox proportional hazards model was performed to determine the independent prognostic factors on overall survival (OS) and disease-specific survival (DSS), applied to develop nomograms. The Schönfeld residual test verified the proportional hazard assumption. The discrimination and consistency of nomograms was assessed and validated according to concordance index ( C -index), receiver operating characteristic (ROC) curves, and calibration curves using an internal 1,000 times bootstrap resampling. The nomogram’s net clinical benefit was assessed through decision curve analysis (DCA). Results. A total of 658 patients with SACC were included. Age, T stage, N stage, M stage, histologic grade, and surgery were independent prognostic factors for OS and DSS. Based on these independent prognostic factors, nomograms were developed to predict 3-, 5-, and 10-year OS and DSS. In the validation of 1,000 times bootstrap resampling, the C -index and ROC curves had good discriminatory ability. The calibration curves indicated excellent consistency between the predicted and actual survival results in the nomograms. The DCA curves demonstrated that the nomograms had good clinical benefit and were superior to the TNM stage and other variables. Conclusions. Two nomograms developed in this study precisely predicted the 3-, 5-, and 10-year OS and DSS rates of patients with SACC in accordance with independent prognostic factors, and their clinical value is better than TNM staging, providing a prognostic reference for other SACC patients.

The combination of conductive material and flexible material is an unavoidable problem in the preparation of flexible sensor, but uniform combination of the materials in micro- or nanostructure is difficult. Coaxial electrospinning is an effective way to realize the combination, which is very strict for the solutions and the synthesis conditions. In this paper, composite pH-sensitive sensors were developed by coaxial electrospinning of polyaniline (PANI) and polyurethane (PU) into core–shell fibers. PU improved the mechanical properties of the sensors. The mechanical properties and conductivity of the sensor can be changed through adjusting the proportion of PANI and PU. By chronopotentiometry in buffer solutions, the sensor worked linearly in the pH range of 2–7 with the sensitivity of − 60 mV/pH and pH change below 0.2 can be detected. It was also proved that the sensor can resist the influence from the changes in temperature, detection time and deformation of the sensor, ensuring the stability in pH detection. Further, by attaching to skin, our sensor showed the response to the pH of small amount of sweat on the skin surface, which suggested the possibility in wearable devices.

Given that subduction is an important driver of plate tectonics on Earth, it is notable that the effects of subduction termination are often complex and poorly understood. Northern Borneo is a prime example of a post-subduction environment, where two subduction zones have terminated within the last 20 Ma. The region however has seen very few seismic studies likely due to the low levels of seismicity in the region compared to the rest of Southeast Asia and due to the challenging deployment environment. The goal of the northern Borneo Orogeny Seismic Survey (nBOSS) network, which operated between 2018 and 2020 and consisted of 47 broadband instruments, was to provide constraints and answer first order questions about the structure of the lithosphere and asthenosphere in this post-subduction setting. Waveform data from this network were supplemented with data recorded by 33 permanent instruments operated by the Malaysian meteorological authority, METMalaysia. In this study we produce the first model of the crustal shear wave velocity structure under northern Borneo using surface wave ambient noise tomography to try and better understand the effects of subduction termination on the crust and to better understand the present day structure of the crust in this region which has not been imaged in this way before. We use a trans-dimensional tomography to produce variable resolution 2D Rayleigh wave phase velocity maps in the period range 2-30 seconds sampled every 2 seconds. Then to produce the final 3D shear wave velocity model a series of 1D inversions were used in combination with a neural network that is trained to find a generalised solution to the 1D inverse problem for this data set. This helps to prevent artefacts forming in the final model as a result of there being no lateral correlations in the 1D inversions by providing the more region specific trained neural network to perform the bulk of the 1D inversions. The result is a model that shows a detailed 3D shear wave velocity structure of the crust that matches expected velocity anomalies from known geological features. This includes the large sedimentary basins in the region, which are revealed as large slow velocity anomalies. Our new model agrees with results from other methods used to study this region, including receiver functions and surface wave tomography.

Deep learning has shown a considerable potential to significantly improve processing efficiency but has not yet been widely deployed to production projects of seismic signal separation such as seismic interference attenuation. The main reasons are: First, the industry has high standards for signal fidelity, which are critical for the success of subsequent seismic imaging, and deep neural network methods have not yet matched the required level; second, the network's interpretability issue has affected many geophysicists and sponsors’ trust in the deep learning technique. To develop deep neural network methods towards the end of benefiting real-world production, we first attempt to better understand their performance, especially in how they make use of local and global features of the data. A novel quantitative research of the overall network model behaviour on synthetic data is conducted. We simulate three types of coherent seismic data components in the shot domain, blend them together and then train a network to separate them. In this process, random noise, a component having only learnable local features, is selectively injected into the network's training pairs. Three network models sharing the same architecture are trained individually, and they show distinctive behaviours when applied to the same test data. Step-by-step analysis of each of them reveals that training the network with additional random noise injected into both the input and the output channel of the desired signal can lead to a decent prediction of the coherent noise based on good learning of the global features and, in the meantime, preserve almost all the data information from being lost. We propose this key lesson we learnt as a new method to improve the network's signal fidelity for shot-domain seismic interference attenuation, which is essentially a signal separation task. Its effectiveness is demonstrated on field data from Africa with a comparison to a conventional physics-based seismic interference attenuation method used in production.

Reduced mitochondrial ATP synthase (ATPS) capacity plays crucial roles in the pathogenesis of metabolic disorders. However, there is currently no effective strategy for synchronously stimulating the expressions of ATPS key subunits to restore its assembly. This study determined the roles of mitochondrial protein FAM3A in regulating the activity and assembly of ATPS in hepatocytes. FAM3A is localized in mitochondrial matrix, where it interacts with F1-ATPS to initially activate ATP synthesis and release, and released ATP further activates P2 receptor-Akt-CREB pathway to induce FOXD3 expression. FOXD3 synchronously stimulates the transcriptions of ATPS key subunits and assembly genes to increase its assembly and capacity, augmenting ATP synthesis and inhibiting ROS production. FAM3A, FOXD3 and ATPS expressions were reduced in livers of diabetic mice and NAFLD patients. FOXD3 expression, ATPS capacity and ATP content were reduced in various tissues of FAM3A-deficient mice with dysregulated glucose and lipid metabolism. Hepatic FOXD3 activation increased ATPS assembly to ameliorate dysregulated glucose and lipid metabolism in obese mice. Hepatic FOXD3 inhibition or knockout reduced ATPS capacity to aggravate HFD-induced hyperglycemia and steatosis. In conclusion, FAM3A is an active ATPS component, and regulates its activity and assembly by activating FOXD3. Activating FAM3A-FOXD3 axis represents a viable strategy for restoring ATPS assembly to treat metabolic disorders.

Development of new bacterial biofilm inhibitors as antibacterial synergists is an effective strategy to solve the resistance of

Mitochondrial FAM3A has been revealed to be a viable target for treating diabetes and nonalcoholic fatty liver disease (NAFLD). However, its distinct mechanism in ameliorating hepatic steatosis remained unrevealed. High-throughput RNA sequencing revealed that carnitine palmityl transferase 2 (CPT2), one of the key enzymes for lipid oxidation, is the downstream molecule of FAM3A signaling pathway in hepatocytes. Intensive study demonstrated that FAM3A-induced ATP release activated P2 receptor to promote the translocation of calmodulin (CaM) from cytoplasm into nucleus, where it functioned as a co-activator of forkhead box protein A2 (FOXA2) to promote the transcription of CPT2, increasing free fatty acid oxidation and reducing lipid deposition in hepatocytes. Furthermore, antidepressant imipramine activated FAM3A-ATP-P2 receptor-CaM-FOXA2-CPT2 pathway to reduce lipid deposition in hepatocytes. In FAM3A-deficient hepatocytes, imipramine failed to activate CaM-FOXA2-CPT2 axis to increase lipid oxidation. Imipramine administration significantly ameliorated hepatic steatosis, hyperglycemia and obesity of obese mice mainly by activating FAM3A-ATP-CaM-FOXA2-CPT2 pathway in liver and thermogenesis in brown adipose tissue (BAT). In FAM3A-deficient mice fed on high-fat-diet, imipramine treatment failed to correct the dysregulated lipid and glucose metabolism, and activate thermogenesis in BAT. In conclusion, imipramine activates FAM3A-ATP-CaM-FOXA2-CPT2 pathway to ameliorate steatosis. For depressive patients complicated with metabolic disorders, imipramine may be recommended in priority as antidepressive drug.

Population growth and increasing food and energy demands have led to nitrogen pollution. The nitrogen footprint is a relatively new index of quantitative determination of nitrogen emissions used to measure the environmental pollution caused by human activities. In this study, the nitrogen footprint in the Min River Basin from 2000 to 2015 was estimated based on the N-calculator model. The dynamic change characteristics and the impact of social and economic influencing factors on the nitrogen footprint were analyzed. Our results revealed that from 2000 to 2015, the total N footprint in the Min River Basin increased from 173,609 Mg to 275,803 Mg, with an increase from 20.3 kg N/capita/yr to 26.0 kg N/capita/yr. The increase in the N footprint was mainly due to high urbanization, and the per capita food N footprint changes in rural and urban areas were positively correlated with changes in food consumption patterns. The nitrogen footprint of the urban residents increased at a stable average rate of 18.0 kg N/capita/yr, but the nitrogen footprint of rural residents initially decreased and then increased, eventually reaching an average value of 16.9 kg N/capita/yr. These data revealed the gradual narrowing of the rural-urban gap of the nitrogen footprint. Foods with a high-nitrogen content accounted for a high proportion (61%) of the per capita nitrogen footprint of the urban residents, whereas the proportion of the high-nitrogen foods (e.g., animal meat and aquatic products) of the rural residents increased from 39% to 57% over the time period of the study. In addition, the analysis of the correlation between food nitrogen footprint and social and economic factors showed that the nitrogen footprint was influenced by income and consumption levels, family size, education degree, and regional dietary habits. The food nitrogen footprints of the urban and rural residents in the basin were positively correlated with per capita disposable income and resident consumption level but negatively correlated with the Engel’s coefficient and the average family size. Our results provide evidence that basin residents can effectively reduce their N footprint by increasing nitrogen utilization in food production, optimizing their dietary structure, maintaining reasonable low nitrogen dietary habits, and using clean energy sources.

This study investigates the formation of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/F) in oxidation of 4,4′-dichlorobiphenyl (4,4′-DCB) under gas phase conditions over a temperature range of 400–800°C. The experiments involved an isothermal flow reactor equipped with a sample collection system to adsorb PCDD/F. We coated the walls of the reactor, made of high purity quartz, with boric oxide to remove catalytic effects and employed the gas chromatography-ion trap mass spectrometry (GC-ITMS) to quantitate PCDD/F. Trace analysis by GC-ITMS identified all four MCDF and up to five DCDF isomers, of which 3,7-DCDF constitutes the dominant species, formed directly from 4,4′-DCB. MCDD and DCDD appeared at lower concentration, generated from condensation of chlorophenoxy and chlorobenzenes. Our results show significant differences from the measurements of the thermal decomposition of polychlorinated biphenyls (PCB) performed in sealed ampoules, indicating strong surface effects present in earlier studies. Gas-phase reactions (> 600°C) lead to selective formation of a small number of lower-chlorinated pollutants, whereas reactive surfaces, as evident from literature, engender catalytic (but non-selective) appearance of a large number of higher-chlorinated and toxic chloroaryl congeners, even at temperature of less than 300°C. Results from the density functional theory (DFT) calculations indicate that, majority of the detected congeners of PCDF arise from the oxidative transformation of the parent 4,4′-DCB; i.e., not from the commonly suggested precursors of chlorinated phenols and benzenes. Our findings have significant importance to understanding the emission of PCB and PCDD/F in combustion processes.

The objective of this research was to explore the dose-effect relationships of dicentric plus ring (dic + r), micronucleus (MN) and nucleoplasmic bridges (NPB) induced by carbon ions in human lymphocytes.Venous blood samples were collected from three healthy donors.The frequencies of dic + r, MN and NPB showed significantly increases in a dose-depended manner after exposure toThe dose-response curves of carbon ions-induced dic + r, MN and NPB were constructed. These results could be helpful to improve radiation risk assessment and dose estimation after exposed to carbon ions irradiation.