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Rapid and accurate detection of the zoonotic nematode Anisakis is poised to control its epidemic. The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas-associated assay shows great potential in the detection of pathogenic microorganisms. The one-tube method integrated the CRISPR system with the recombinase polymerase amplification (RPA) system to avoid the risk of aerosol pollution; however, it suffers from low sensitivity due to the incompatibility of the two systems and additional manual operations. Therefore, in the present study, the agarose hydrogel boosted one-tube RPA-CRISPR/Cas12a assay was constructed by adding the CRISPR system to the agarose hydrogel, which avoided the initially low amplification efficiency of RPA caused by the cleavage of Cas12a and achieved reaction continuity. The sensitivity was 10-fold higher than that of the one-tube RPA-CRISPR/Cas12a system. This method was used for Anisakis detection within 80 min from the sample to result, achieving point-of-care testing (POCT) through a smartphone and a portable device. This study provided a novel toolbox for POCT with significant application value in preventing Anisakis infection.

The transversely isotropic media with a vertical symmetry (VTI) has been widely used in shale reservoirs, and the reflection response and prestack seismic inversion have been of interest to geophysicists. Petrophysical experiments have shown that shale reservoirs may exhibit moderate or even stronger anisotropy, causing limitations in the weak anisotropy-based reflectivity approximation. Existing weak-contrast approximations are usually highly nonlinear equations characterized by the stiffness parameters and slowness, which is not conducive to AVO analysis and prestack seismic inversion of anisotropic parameters. We proposed a novel approach for weak-contrast PP-reflectivity based on the P- and S-elastic impedance matrices and applied it to prestack seismic inversion. First, by simplifying the phase velocities and polarization vectors, the elastic impedance matrices are linearly characterized in the weak-contrast half-spaces. Then, the perturbation theory is used to derive the weak-contrast PP-reflectivity, which consists of two terms, one directly resulting from the difference between the parameters of upper and lower media, and the other resulting from the first-order perturbation of the phase velocity and polarization. It has a clear physical meaning and can degenerate to the Aki-approximation in isotropic media. In particular, the isotropic second-order terms are introduced to significantly improve its accuracy at the interfaces with high-speed contrast. Furthermore, we designed a coefficient matrix to linearize the high-order reflectivity terms and developed an improved algorithm based on the alternating direction method of multipliers (ADMMs). Finally, the applications on logging curves and field seismic data for the shale prove the feasibility and effectiveness of the proposed method.

Volatile esters are major aromas contributing to the organoleptic quality of apple fruit. However, the molecular mechanisms underlying the regulation of volatile ester biosynthesis in apple remain elusive. This study investigated the volatile profiles and transcriptomes of ‘Qinguan’ (QG) apple fruit during development and/or postharvest storage. Although the constitution of volatiles varied widely between the peel and flesh, the volatile profiles of the peel and flesh of ripening QG fruit were dominated by volatile esters. WGCNA results suggested that 19 genes belonging to ester biosynthesis pathways and 11 hub transcription factor genes potentially participated in the biosynthesis and regulation of esters. To figure out key regulators of ester biosynthesis, correlation network analysis, dual-luciferase assays, and yeast one-hybrid assay were conducted and suggested that MdMYB94 trans-activated the MdAAT2promoter and participated in the regulation of ester biosynthesis. This study provides a framework for understanding ester biosynthesis and regulation in apple.

Neural stem progenitor cell (NSPC) transplantation has been regarded as a promising therapeutic method for spinal cord injury (SCI) repair. However, different NSPCs may have different therapeutic effects, and it is therefore important to identify the optimal NSPC type. In our study, we compared the transcriptomes of human fetal brain-derived NSPCs (BNSPCs), spinal cord-derived NSPCs (SCNSPCs) and H9 embryonic stem-cell derived NSPCs (H9–NSPCs) in vitroand subsequently we transplanted each NSPC type on a collagen scaffold into a T8-9 complete SCI rat model in vivo. In vitrodata showed that SCNSPCs had more highly expressed genes involved in nerve-related functions than the other two cell types. In vivo, compared with BNSPCs and H9–NSPCs, SCNSPCs exhibited the best therapeutic effects; in fact, SCNSPCs facilitated electrophysiological and hindlimb functional recovery. This study demonstrates that SCNSPCs may be an appropriate candidate cell type for SCI repair, which is of great clinical significance.

Graph Convolutional Network (GCN) has achieved extraordinary success in learning representations of nodes in graphs. However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore all possible meta-paths and extract the most useful ones for each target object, which hinders both effectiveness and interpretability; (2) before performing aggregation, they often require some additional time-consuming pre-processing operations, which increase the computational complexity. To address the above issues, we propose an interpretable and efficient Heterogeneous Graph Convolutional Network (ie-HGCN) to learn the representations of objects in HINs. It is designed as a hierarchical aggregation architecture, i.e., object-level aggregation and type-level aggregation. The new architecture can automatically evaluate all possible meta-paths within a length limit, and discover and exploit the most useful ones for each target object, i.e., at fine granularity. It also reduces the computational cost by avoiding additional time-consuming pre-processing operations. Theoretical analysis shows its ability to evaluate the usefulness of all possible meta-paths, its connection to the spectral graph convolution on HINs, and its quasi-linear time complexity. Extensive experiments on four real network datasets demonstrate its interpretability, efficiency as well as its superiority against thirteen baselines.

Periodic horizontal thin interlayers, or formations developing horizontally layered fractures in a homogeneous background, are commonly studied as a transverse isotropy with a vertical axis of symmetry (VTI) media. Anisotropy needs to be considered in predicting such special reservoirs and can be inferred from the amplitude variation with offset (AVO) by the exact or linear reflectivity equations. However, the linear approximation is usually derived based on the assumptions of weak anisotropy and weak contrast, and the accuracy suffers when the anisotropy or the contrast of two adjacent media increases. In addition, due to the high nonlinearity of the Graebner equation, the stability and efficiency of the exact inversion are limited. To address these issues, we propose a robust prestack seismic inversion for VTI media using quadratic PP-reflectivity approximation. First, by deriving quadratic approximations for phase velocity and polarization, we analyze the advantage of the second-order terms at a higher contrast interface. Then, based on perturbation theory, we derive a quadratic PP-reflectivity approximation from the exact equation. It consists of first- and second-order terms with isotropic and anisotropic components, and the accuracy is significantly improved even with the enhanced anisotropy. Finally, we introduce the Hadamard product operator to linearly characterize the objective function and develop an improved alternating direction method of multipliers (ADMM) algorithm to handle the nonlinearity of the quadratic equations. The feasibility and stability of the proposed method are tested on two datasets, that is, the logging curves used as numerical experiments and a field dataset from a shale reservoir.

Viscoelastic theory-based frequency-dependent amplitude variation with offset (AVO) inversions are a useful tool for identifying fluids based on their dispersion properties. When used to identify reservoir oil and gas qualities, the quality factors ( $Q$ -factors) derived from the inversion have produced good results. But currently, the majority of $Q$ -factor inversions are linear inversions based on linear approximations, whereas nonlinear inversions based on exact equations with higher precision and fewer assumptions are only occasionally carried out. Meanwhile, in broadband seismic inversion, the low-frequency model estimated by complex frequency inversion can fully utilize seismic data’s low-frequency information and provide improved robustness and rationality for inversion. Therefore, we derive a frequency-dependent reflection coefficient equation that varies with angle by replacing the nearly constant $Q$ model suggested by Aki and Richards into the exact Zoeppritz equation and simplifying. Additionally, using the Bayesian framework as a foundation, we created a novel two-stage broadband frequency-dependent nonlinear inversion approach for $Q$ -factors that can estimate $Q$ -factors, produce accurate fluid identification results, and serve as a solid foundation for oil and gas exploration and reservoir identification. Utilizing examples from both synthetic and real-world data, we verify the applicability of oil and gas indicators.

There has been a surge of researchers’ interest in building predictive models over graphs. However, the overwhelming complexity of graph space often makes it challenging to extract interpretable and discriminative structural features for graph classification. In this work, we propose a new graph neural network model called Substructure Assembling Network (SAN) to learn graph representations for classification. The key innovation is a unified Substructure Assembling Unit (SAU), which is a variant of Recurrent Neural Network (RNN) designed to hierarchically assemble useful pieces of graph components so as to fabricate discriminative substructures. A key challenge is that SAUs need to process the neighbors of a node sequentially while no natural order is defined therein. SAN tries to make the model insensitive to neighborhood orders by randomly shuffling neighborhood sequences in training. However, this could suffer high variance, especially when the neighborhood size is large. Hence, we further propose to equip SAN with a novel module named Soft Sequence with Context Attention (SSCA). SAN-SSCA employs the proposed context attention technique to learn the best “soft” permutation of the neighbors w.r.t. classification. It helps the model achieve higher accuracy as well as lower variance. Experiments confirm the effectiveness of SAN-SSCA.

Attention-based neural networks, such as Transformers, have become ubiquitous in numerous applications, including computer vision, natural language processing, and time-series analysis. In all kinds of attention networks, the attention maps are crucial as they encode semantic dependencies between input tokens. However, most existing attention networks perform modeling or reasoning based on representations, wherein the attention maps of different layers are learned separately without explicit interactions. In this paper, we propose a novel and generic evolving attention mechanism, which directly models the evolution of inter-token relationships through a chain of residual convolutional modules. The major motivations are twofold. On the one hand, the attention maps in different layers share transferable knowledge, thus adding a residual connection can facilitate the information flow of inter-token relationships across layers. On the other hand, there is naturally an evolutionary trend among attention maps at different abstraction levels, so it is beneficial to exploit a dedicated convolution-based module to capture this process. Equipped with the proposed mechanism, the convolution-enhanced evolving attention networks achieve superior performance in various applications, including time-series representation, natural language understanding, machine translation, and image classification. Especially on time-series representation tasks, Evolving Attention-enhanced Dilated Convolutional (EA-DC-) Transformer outperforms state-of-the-art models significantly, achieving an average of 17% improvement compared to the best SOTA. To the best of our knowledge, this is the first work that explicitly models the layer-wise evolution of attention maps. Our implementation is available at https://github.com/pkuyym/EvolvingAttention.

In the absence of a well-log-based low-frequency model, a broadband inversion strategy with a low-frequency model calculated in complicated frequency-domain inversion can successfully mine low-frequency seismic information and decrease the inversion results’ reliance on the starting model to some extent. Furthermore, from the standpoint of the equation, prestack nonlinear inversion based on the nonlinear reflection coefficient equation may meet the needs of greater precision inversion. Nonetheless, most prestack inversion approaches to subsurface elastic parameters have been based on linear time-domain approximations, which cannot meet the needs of high-precision underground reservoir prediction and fluid identification in the increasingly complex geological backgrounds of exploration areas. Given the aforementioned issues, we execute broadband prestack nonlinear elastic impedance (EI) inversion using a more accurate nonlinear reflection coefficient equation in conjunction with the Lévy flight Markov chain Monte Carlo (LFMCMC) stochastic inversion approach. Throughout this process, we also consider the reduction in seismic wave amplitude caused by transmission loss in the nonlinear forward operator, and we develop a modified convolution model to mitigate the impact of transmission loss in layered media on seismic amplitude. In comparison to the traditional linear elastic impedance inversion method, the new method not only improves inversion precision and adaptability to strong impedance changes but it also reduces the transmission loss in the case of sand and mudstone interbedding and protects the underlying strata’s weak amplitude changes, resulting in more reasonable inversion results for real changes in underground impedance. Using synthetic and field data samples, we assess the approach’s reliability and applicability.

Inventory is the core link of enterprise operation and an essential resource for enterprises to occupy the market. Therefore, optimizing the control and management of inventory is a necessary measure for enterprises to maintain their core competitiveness. However, at present, the optimization of the enterprise’s inventory is basically still at the single inventory level, which seriously shortens the life cycle of the enterprise’s products and reduces the speed of the enterprise’s capital flow. Digital supply chain is the digitization of traditional supply chain services, which can change the traditional thinking of inventory control and management, break down the barriers between enterprises and modern logistics, and build a complete management system for enterprises. Smart logistics is a product of the new era, and it is also a product of the development of the digital supply chain. To this end, this paper proposes an inventory optimization algorithm based on digital supply chain and smart logistics. The algorithm is optimized on the basis of the original inventory optimization scheme, so that the improved algorithm can be more suitable for actual inventory management needs. The experimental results of this paper show that the improved new inventory optimization algorithm improves the comprehensive benefit by 39.1% on the original basis, which can effectively reduce the error in the actual use process.

Enterocytozoon bieneusiis the most common microsporidian species in humans and can affect over 200 animal species. Considering possible increasing risk of human E. bieneusiinfection due to close contact with pet dogs and identification of zoonotic E. bieneusigenotypes, 589 fresh fecal specimens of pet dogs were collected from Yunnan Province, China to determine the occurrence of E. bieneusi, characterize dog-derived E. bieneusiisolates, and assess their zoonotic potential at the genotype level. Enterocytozoon bieneusiwas identified and genotyped by PCR and sequencing of the internal transcribed spacer (ITS) region of the ribosomal RNA (rRNA) gene. Twenty-nine specimens (4.9%) were positive. A statistical difference was observed in occurrence rates of E. bieneusiin pet dogs among 11 sampling sites by Fisher’s exact test. Fifteen genotypes were identified and all of them phylogenetically belonged to zoonotic group 1, including four known genotypes (EbpC, D, Peru 8, and Henan-III) and 11 novel genotypes. Genotype Henan-III was reported in dogs for the first time. The finding of known genotypes found previously in humans and novel genotypes falling into zoonotic group 1 indicates that dogs may play a role in the transmission of E. bieneusito humans in the investigated areas.

Nestin is expressed extensively in neural stem/progenitor cells during neural development, but its expression is mainly restricted to the ependymal cells in the adult spinal cord. After spinal cord injury (SCI), Nestin expression is reactivated and Nestin-positive (Nestin+) cells aggregate at the injury site. However, the derivation of Nestin+cells is not clearly defined. Here, we found that Nestin expression was substantially increased in the lesion edge and lesion core after SCI. Using a tamoxifen inducible CreER(T2)-loxP system, we verified that ependymal cells contribute few Nestin+cells either to the lesion core or the lesion edge after SCI. In the lesion edge, GFAP+astrocytes were the main cell type that expressed Nestin; they then formed an astrocyte scar. In the lesion core, Nestin+cells expressed αSMA or Desmin, indicating that they might be derived from pericytes. Our results reveal that Nestin+cells in the lesion core and edge came from various cell types and rarely from ependymal cells after complete transected SCI, which may provide new insights into SCI repair.

As an indispensable component of lithium-ion batteries (LIBs), the application of porous copper (Cu) foil current collector is an effective method to improve the performance and energy density of LIBs. In this work, a porous Cu (PCu) foil was fabricated by acid washing the Cu-Fe3O4foil prepared by the electro-codeposition in a stirred CuSO4solution containing the magnetic Fe3O4nanoparticles. The effect of the stirring speed on the properties of PCu foils and the battery performance of PCu foil as current collectors were investigated. The surface roughness of the PCu foil increases, while its volume density decreases as the stirring speed rises. The volume density of PCu foil (6.63 g cm−3) prepared at 400 rpm (PCu400) is reduced by 22.4% than that of the commercial Cu foil (8.55 g cm−3). The specific capacity (241.2 mAh g−1) of the cell with PCu400 current collector is 31.3% higher than that of the cell with commercial Cu current collector (183.7 mAh g−1) after 300 charge-discharge cycles at 2 C. The electro-codeposition of porous Cu foil current collectors offers a promising method for high-performance lightweight LIBs.

Neural stem cells (NSCs) in the spinal cord hold great potential for repair after spinal cord injury (SCI). The ependyma in the central canal (CC) region has been considered as the NSCs source in the spinal cord. However, the ependyma function as NSCs after SCI is still under debate. We used Nestin as a marker to isolate potential NSCs and their immediate progeny, and characterized the cells before and after SCI by single-cell RNA-sequencing (scRNA-seq). We identified two subgroups of NSCs: the subgroup located within the CC cannot prime to active NSCs after SCI, while the subgroup located outside the CC were activated and exhibited the active NSCs properties after SCI. We demonstrated the comprehensive dynamic transcriptome of NSCs from quiescent to active NSCs after SCI. This study reveals that Nestin+cells outside CC were NSCs that activated upon SCI and may thus serve as endogenous NSCs for regenerative treatment of SCI in the future.

Modeling dependence structure among various risks, especially the measure of tail dependence and the aggregation of risks, is crucial for risk management. In this article, we present an extension to the traditional one-parameter Archimedean copulas by integrating the log-gamma-generated (LGG) margins. This class of novel multivariate distribution can better capture the tail dependence. The distortion effect on the classic one-parameter Archimedean copulas is well exhibited and the analytical expression of the sum of bivariate margins is proposed. The model provides a flexible way to capture tail risks and aggregate portfolio losses. Sufficient conditions for constructing a legitimate d-dimensional LGG Archimedean copula as well as the simulation framework are also proposed. Furthermore, two applications of this model are presented using concrete insurance datasets.

Reflection feature-constrained inversion (RCI) is a multichannel strategy that can effectively improve the stability and continuity of seismic inversion in complex geology, especially for pre-stack inversion with higher uncertainty. To address the limitations of conventional RCI to 2D profiles and high computational costs, a 3D pre-stack seismic inversion constrained by spatial reflection features was developed. First, a new approach to characterize seismic reflection features by decomposing seismic events along the vertical and lateral directions was proposed, which significantly reduces the size of the reflection feature operators. Second, we constructed reflection feature regularization terms and designed a fast reflection feature-constrained inversion (FRCI) for 2D seismic profiles. Finally, the proposed FRCI was extended to 3D by considering the spatial reflection features in two mutually perpendicular directions. Fast spatial reflection feature-constrained inversion (FSRCI) was proposed by constructing spatial reflection feature operators in a 3D coordinate system. The key to this method is that the parameter estimation is transformed into solving the 3D Sylvester equation such that the model parameters can be estimated directly from 3D seismic data. The inversion results of the overthrust model and field data showed that the proposed method can improve efficiency while ensuring spatial continuity. In summary, this study developed a fast pre-stack seismic inversion constrained by spatial reflection features based on the characterization of stratum spatial features, which is effective in complex geology, such as high-dipping and faults.

Pesticides remain an integral part of agricultural activities worldwide. Although there have been a number of studies over the last two decades concerning the adverse effects of pesticide poisoning and chronic long term exposures on neurobehavioral function, the impact of recent pesticide poisoning and long term pesticide exposure on neurobehavioral function in Chinese farm workers has not been reported. China is the largest user of pesticides worldwide and figures suggest 53,300–123,000 Chinese people are poisoned every year. A case control study was conducted to examine the impact of recent pesticide poisoning on neurobehavioral function and the relationship between years worked in agriculture and lower performance on neurobehavioral tests. A total of 121 farm workers who self-reported recent pesticide poisonings within the previous 12 months (case group) and 80 farm workers who reported no pesticide poisoning in the previous 12 months (control group) were recruited from three areas of Jiangsu Province, China. The World Health Organization (WHO) recommended neurobehavioral core test battery (NCTB) was used to assess neurobehavioral functioning among cases and controls. Student's ttests and two-way covariance analysis (ANCOVA) were used to test for significant differences in the neurobehavioral test results between the groups. Scores on the Profile of Mood States (POMS) in the recently poisoned group were significantly higher for anger-hostility, depression-dejection, tension-anxiety and lower for vigor-activity compared to controls (p < .05). Digit span, digit symbol, Benton visual retention and pursuit aiming scores were all significantly lower among the recently poisoned group compared to the controls (p < .05). Two-way ANCOVA indicated significantly lower performance in correct pursuit aiming and higher error pursuit aiming amongst the recently poisoned group and those who had worked for more than 30 years in agriculture (p < .05). These findings provide important preliminary epidemiological evidence regarding the association between occupational pesticide exposure and neurobehavioral functioning in Chinese farm workers.