Search Results (31)

Cervical cancer remains a major global burden largely caused by persistent infection with high risk human papillomavirus (HPV). Biological differences between HPV clade A7 and HPV clade A9 may influence tumor programs and clinical outcomes. To propose pharmacological candidates for repositioning, we applied an expression-based drug repurposing approach using the OCTAD (Open Cancer Therapeutic Discovery) framework. Disease transcriptional signatures were constructed for both HPV clades and compared with drug perturbation profiles to identify compounds showing inverse associations with the tumor related expression patterns, restricting the analysis to Food and Drug Administration (FDA) approved agents. The screening identified 41 and 52 candidates for HPV clade A7 and HPV clade A9, respectively, and stronger transcriptomic reversal was associated with higher drug sensitivity in relevant cell lines. These candidates were enriched for pharmacologic classes such as histone deacetylase inhibitors, estrogen pathway modulators, and statins. Additional enriched categories also emerged, including antimetabolites, protein kinase inhibitors, proteasome inhibitors, antimalarials, and antimicrobial agents, several of which already show experimental activity in cervical cancer models. These findings reveal both shared and clade-associated vulnerabilities in HPV-driven cervical cancer and demonstrate the utility of expression-based repurposing for generating actionable hypotheses. The resulting drug lists provide a concise, biologically grounded resource to guide preclinical validation and rational exploration in cervical cancer HPV positive models. Full article

Porcine adenovirus serotype 5 (PAdV-5) is an emerging viral vector platform for veterinary vaccines; however, its genomic plasticity and essential replication elements remain incompletely characterized. This study reports the isolation and reverse genetic manipulation of a novel PAdV-5 strain (GD84) from diarrheic piglets in China. PCR screening of 167 clinical samples revealed a PAdV-5 detection rate of 38.3% (64/167), with successful isolation on ST cells after three blind passages. The complete GD84 genome is 32,620 bp in length and exhibited 99.0% nucleotide identity to the contemporary strain Ino5, but only 97.0% to the prototype HNF-70. It features an atypical GC content of 51.0% and divergent structural genes—most notably the hexon gene (89% identity to HNF-70)—suggesting altered immunogenicity. Using Red/ET recombineering, we established a rapid (less than 3 weeks) reverse genetics platform and generated four E3-modified recombinants: ΔE3-All-eGFP, ΔE3-12.5K-eGFP, ΔE3-12.5K+ORF4-eGFP, and E3-Insert-eGFP. Crucially, the ΔE3-All-eGFP construct (complete E3 deletion) failed to be rescued, while constructs preserving the 12.5K open reading frame (ORF) yielded replication-competent viruses with sustained eGFP expression over three serial passages and titers over 10^7.0 TCID₅₀/mL. Fluorescence intensity was inversely correlated with genome size, as the full-length E3-Insert-eGFP virus showed reduced expression compared with the ΔE3 variants. Our work identifies the 12.5K ORF as essential for PAdV-5 replication and provides an optimized vaccine engineering platform that balances genomic payload capacity with replicative fitness. Full article

The acquisition of nighttime remote-sensing visible-light images is often accompanied by low-illumination effects and haze interference, resulting in significant image quality degradation and greatly affecting subsequent applications. Existing low-light enhancement and dehazing algorithms can handle each problem individually, but their simple cascade cannot effectively address unknown real-world degradations. Therefore, we design a joint processing framework, WFDiff, which fully exploits the advantages of Fourier–wavelet dual-domain features and innovatively integrates the inverse diffusion process through differentiable operators to construct a multi-scale degradation collaborative correction system. Specifically, in the reverse diffusion process, a dual-domain feature interaction module is designed, and the joint probability distribution of the generated image and real data is constrained through differentiable operators: on the one hand, a global frequency-domain prior is established by jointly constraining Fourier amplitude and phase, effectively maintaining the radiometric consistency of the image; on the other hand, wavelets are used to capture high-frequency details and edge structures in the spatial domain to improve the prediction process. On this basis, a cross-overlapping-block adaptive smoothing estimation algorithm is proposed, which achieves dynamic fusion of multi-scale features through a differentiable weighting strategy, effectively solving the problem of restoring images of different sizes and avoiding local inconsistencies. In view of the current lack of remote-sensing data for low-light haze scenarios, we constructed the Hazy-Dark dataset. Physical experiments and ablation experiments show that the proposed method outperforms existing single-task or simple cascade methods in terms of image fidelity, detail recovery capability, and visual naturalness, providing a new paradigm for remote-sensing image processing under coupled degradations. Full article

In order to improve the inversion accuracy of electrical resistivity tomography (ERT) and overcome the limitations of traditional linear methods, this paper proposes an improved shuffled frog leaping algorithm (SFLA). First, an equilibrium grouping strategy is designed to balance the contribution weight of each subgroup to the global optimal solution, suppressing the local optimum traps caused by the dominance of high-quality groups. Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. Additionally, the root mean square error is reduced by 57%. In the engineering validation at the Baota Mountain mining area in Jurong, the improved SFLA inversion clearly reveals the undulating bedrock morphology. At a measuring point 55 m along the profile, the bedrock depth is 14.05 m (ZK3 verification value 12.0 m, error 17%), and at 96 m, the depth is 6.9 m (ZK2 verification value 6.7 m, error 3.0%). The characteristic of deeper bedrock to the south and shallower to the north is highly consistent with the terrain and drilling data (RMSE = 1.053). This algorithm provides reliable technical support for precise detection of complex geological structures using ERT. Full article

Aiming at the problems of complex control processes, strong model dependence, and difficult engineering application when the existing active suspension control strategy is applied to multi-axle vehicles, an active suspension control strategy based on the inverse position solution of a parallel mechanism is proposed. First, the active suspension of the three-axle emergency rescue vehicle is grouped and interconnected within the group, and it is equivalently constructed into a 3-DOF parallel mechanism. Then, the displacement of each equivalent suspension actuating hydraulic cylinder is calculated by using the method of the inverse position solution of a parallel mechanism, and then the equivalent actuating hydraulic cylinder is reversely driven according to the displacement, thereby realizing the effective control of the attitude of the vehicle body. To verify the effectiveness of the proposed control strategy, a three-axis vehicle experimental platform integrating active suspension and hydro-pneumatic suspension was built, and a pulse road experiment and gravel pavement experiment were carried out and compared with hydro-pneumatic suspension. Both types of road experimental results show that compared to hydro-pneumatic suspension, the active suspension control strategy based on the inverse position solution of a parallel mechanism proposed in this paper exhibits different degrees of advantages in reducing the peak values of the vehicle vertical displacement, pitch angle, and roll angle changes, as well as suppressing various vibration accelerations, significantly improving the vehicle’s driving smoothness and handling stability. Full article

To address the challenges in modeling and optimization caused by nonlinear dynamic coupling and real-time measurement difficulties of key biological parameters in Pichia pastoris fermentation processes, this study proposes a soft-sensing method based on Adam-Fully Connected Neural Network inverse. Firstly, a non-deterministic mechanism model is constructed to characterize the dynamic coupling relationships among multiple variables in the fermentation process, and the reversibility of the system and the construction method of the inverse extended model are analyzed. Further, by leveraging the nonlinear fitting capabilities of the Fully Connected Neural Network to identify the inverse extended model, an adaptive learning rate optimization algorithm is introduced to dynamically adjust the learning rate of the Fully Connected Neural Network, thereby enhancing the convergence and robustness of the nonlinear system. Finally, a composite pseudo-linear system is formed by cascading the inverse model with the original system, achieving decoupling and the high-accuracy prediction of key parameters. Experimental results demonstrate that the proposed method significantly reduces prediction errors and enhances generalization capabilities compared to traditional models, validating the effectiveness of the proposed method in complex bioprocesses. Full article

Industrial image anomaly detection plays a critical role in intelligent manufacturing by automatically identifying defective products through visual inspection. While unsupervised approaches eliminate dependency on annotated anomaly samples, current teacher–student framework-based methods still face two fundamental limitations: insufficient discriminative capability for structural anomalies and suboptimal anomaly feature decoupling efficiency. To address these challenges, we propose a Synthetic-Anomaly Contrastive Distillation (SACD) framework for industrial anomaly detection. SACD comprises two pivotal components: (1) a reverse distillation (RD) paradigm whereby a pre-trained teacher network extracts hierarchically structured representations, subsequently guiding the student network with inverse architectural configuration to establish hierarchical feature alignment; (2) a group of feature calibration (FeaCali) modules designed to refine the student’s outputs by eliminating anomalous feature responses. During training, SACD adopts a dual-branch strategy, where one branch encodes multi-scale features from defect-free images, while a Siamese anomaly branch processes synthetically corrupted counterparts. FeaCali modules are trained to strip out a student’s anomalous patterns in anomaly branches, enhancing the student network’s exclusive modeling of normal patterns. We construct a dual-objective optimization integrating cross-model distillation loss and intra-model contrastive loss to train SACD for feature alignment and discrepancy amplification. At the inference stage, pixel-wise anomaly scores are computed through multi-layer feature discrepancies between the teacher’s representations and the student’s refined outputs. Comprehensive evaluations on the MVTec AD and BTAD benchmark demonstrate that our method is effective and superior to current knowledge distillation-based approaches. Full article

Acoustic metamaterials have been widely applied in fields such as sound insulation and noise reduction due to their controllable band structures and unique abilities to manipulate low-frequency sound waves. However, there exists a highly nonlinear mapping relationship between their structural parameters and performance responses, which causes traditional design methods to face the problems of inefficiency and poor generalization. Therefore, this paper proposes a bidirectional modeling framework based on deep learning. We constructed a forward prediction network that integrates an attention mechanism, a multi-scale feature fusion, and a reverse design model that combines an improved autoencoder and cascaded neural network to efficiently model the dispersion performance of acoustic metamaterials. In the feedforward network, the improved forward prediction model shows superior performance compared to the traditional Convolutional Neural Network model and the model based only on the Convolutional Block Attention Module attention mechanism, with a prediction accuracy of 99.65%. It has better fitting ability and stability in the high-frequency part of the dispersion curve. In the inverse network part, compression of the high-dimensional dispersion curves by an improved autoencoder reduces the training time by about 13.5% without significant degradation of the inverse prediction accuracy. The proposed network model provides a more efficient method for the design of metamaterials. Full article

To address the inherent nonlinearity and strong coupling among rotor displacement, speed, and flux linkage in the composite cage rotor bearingless induction motor (CCR-BIM), an inverse system decoupling control strategy based on a support vector machine (SVM) optimized by the improved simulated annealing-genetic algorithm (ISA-GA) is proposed. First, based on the structure and working principle of CCR-BIM, the mathematical model of CCR-BIM is derived, and its reversibility is rigorously analyzed. Subsequently, an SVM regression equation is established, and the SVM kernel function parameters are optimized using the ISA-GA to train a high-precision inverse system decoupling control model. Finally, the inverse system is cascaded with the original system to construct a pseudo-linear system model, achieving linearization and decoupling control of CCR-BIM. To verify the effectiveness and practicability of the proposed decoupling control strategy, the proposed control method is compared with the traditional inverse system decoupling control strategy through simulation and experimentation. Both simulation and experimental results demonstrate that the proposed decoupling control strategy can effectively achieve decoupling control of rotor displacement, rotational speed, and flux linkage in CCR-BIM. Full article

Reverse osmosis membranes have been predominantly made from aromatic polyamide composite thin-films, although significant research efforts have been dedicated to discovering new materials and synthesis technologies to enhance the water–salt selectivity of membranes in the past decades. The lack of significant breakthroughs is partly attributed to the limited comprehensive understanding of the relationships between membrane features and their performance. Insights into the intrinsic features of reverse osmosis (RO) membranes based on metadata were obtained using explainable artificial intelligence to understand the relationships and unify the research efforts. The features related to the chemistry, membrane structure, modification methods, and membrane performance of RO membranes were derived from the dataset of more than 1000 RO membranes. Seven machine learning (ML) models were constructed to evaluate the membrane performances, and their applicability for the tasks was assessed using the metadata. The contribution of the features to RO performance was analyzed, and the ranking of their importance was revealed. This work holds promise for metadata analysis, evaluating the RO membrane against the state of the art and developing an inverse design strategy for the discovery of high-performance RO membranes. Full article

In the context of 3D geometric reconstruction from sequential inverse synthetic aperture radar (ISAR) images, the accurate scatterer trajectory association is a critical step. Aiming at the above problem, an enhanced scatterer trajectory association method is proposed by designing a modified label Gaussian mixture probability hypothesis density (ML-GM-PHD) filtering algorithm. The algorithm commences by constructing a general motion model for scatterers across sequential ISAR images, followed by an in-depth analysis of their motion characteristics. Subsequently, the actual projected positions and measurements of the scattering centers of the observed target are treated as random finite sets, which allows us to reformulate the scatterer trajectory association into a maximum a posteriori (MAP) estimation problem. After that, a ML-GM-PHD filtering algorithm is proposed to realize the scatterer trajectory association. Furthermore, the proposed method is applied to ISAR images in both the forward and reverse directions, enabling the fusion of trajectories from opposing directions to bolster the completeness of the scatterer trajectories. Finally, the factorization method is performed on the scatterer trajectory matrix to implement the 3D geometry reconstruction of the scattering centers in the observed target. Experimental results based on random points and electromagnetic data verify the effectiveness and performance priority of the proposed algorithm. Full article

This paper explores the application of Inverse Reinforcement Learning (IRL) in robotics, focusing on inferring reward functions from expert demonstrations of robot arm manipulation tasks. By leveraging IRL, we aim to develop efficient and adaptable techniques for learning robust solutions to complex tasks in continuous state spaces. Our approach combines Apprenticeship Learning via IRL with Proximal Policy Optimization (PPO), expert-trajectory-based features, and the application of a reverse discount. The feature space is constructed by sampling expert trajectories to capture essential task characteristics, enhancing learning efficiency and generalizability by concentrating on critical states. To prevent the vanishing of feature expectations in goal states, we introduce a reverse discounting application to prioritize feature expectations in final states. We validate our methodology through experiments in a simple GridWorld environment, demonstrating that reverse discounting enhances the alignment of the agent’s features with those of the expert. Additionally, we explore how the parameters of the proposed feature definition influence performance. Further experiments on robotic manipulation tasks using the TIAGo robot compare our approach with state-of-the-art methods, confirming its effectiveness and adaptability in complex continuous state spaces across diverse manipulation tasks. Full article

High-resolution seismic imaging allows for the better interpretation of subsurface geological structures. In this study, we employ least-squares reverse time migration (LSRTM) as a seismic imaging method to delineate the subsurface geological structures from the field dataset for understanding the status of Arctic subsea permafrost structures, which is pertinent to global warming issues. The subsea permafrost structures in the Arctic continental shelf, located just below the seafloor at a shallow water depth, have an abnormally high P-wave velocity. These structural conditions create internal multiples and noise in seismic data, making it challenging to perform seismic imaging and construct a seismic P-wave velocity model using conventional methods. LSRTM offers a promising approach by addressing these challenges through linearized inverse problems, aiming to achieve high-resolution, subsurface imaging by optimizing the misfit between the predicted and the observed seismic data. Synthetic experiments, encompassing various subsea permafrost structures and seismic survey configurations, were conducted to investigate the feasibility of LSRTM for imaging the Arctic subsea permafrost from the acquired seismic field dataset, and the possibility of the seismic imaging of the subsea permafrost was confirmed through these synthetic numerical experiments. Furthermore, we applied the LSRTM method to the seismic data acquired in the Canadian Beaufort Sea (CBS) and generated a seismic image depicting the subsea permafrost structures in the Arctic region. Full article

Fracture network fracturing is pivotal for achieving the economical and efficient development of shale gas, with the connectivity among fracture networks playing a crucial role in reservoir stimulation effectiveness. However, flow back data that reflect fracture network connectivity information are often ignored, resulting in an inaccurate prediction of the effective fracture network volume (EFNV). The accurate calculation of the EFNV has become a key and difficult issue in the field of shale fracturing. For this reason, the accurate shale gas effective fracture network volume inversion method needs to be improved. Based on the flow back characteristics of fracturing fluids, a tree-shaped fractal fracture flow back mathematical model for inversion of EFNV was established and combined with fractal theory. A genetic algorithm workflow suitable for EFNV inversion of shale gas was constructed based on the flow back data after fracturing, and the fracture wells in southern Sichuan were used as an example to carry out the EFNV inversion. The reliability of the inversion model was verified by testing production, cumulative gas production, and microseismic results. The field application showed that the inversion method proposed in this paper can obtain tree-shaped fractal fracture network structure parameters, fracture system original pressure, matrix gas breakthrough pressure, fracture compressibility coefficient, reverse imbibition index, equivalent main fracture half length, and effective initial fracture volume (EIFV). The calculated results of the model belong to the same order of magnitude as those of the HD model and Alkouh model, and the model has stronger applicability. This research has important theoretical guiding significance and field application value for improving the accuracy of the EFNV calculation. Full article

The full waveform inversion at this stage still has many problems in the recovery of deep background velocities. Velocity modeling based on end-to-end deep learning usually lacks a generalization capability. The proposed method is a multi-scale convolutional neural network velocity inversion (Ms-CNNVI) that incorporates a multi-scale strategy into the CNN-based velocity inversion algorithm for the first time. This approach improves the accuracy of the inversion by integrating a multi-scale strategy from low-frequency to high-frequency inversion and by incorporating a smoothing strategy in the multi-scale (MS) convolutional neural network (CNN) inversion process. Furthermore, using angle-domain reverse time migration (RTM) for dataset construction in Ms-CNNVI significantly improves the inversion efficiency. Numerical tests showcase the efficacy of the suggested approach. Full article