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Computational Fluid Dynamics (CFD) plays a crucial role in investigating new physical phenomena and exploring the principles of fluid mechanics. However, CFD numerical methods often face the challenges of long research cycles, high costs, and extensive human-computer interactions due to the growing complexity of computational tasks. To meet the burgeoning requirements of contemporary physical sciences, in recent years, the coupling of traditional scientific computing techniques with promising deep learning techniques well-known from computer science have emerged as a new research paradigm. This paradigm aims to create automated, intelligent tools for obtaining valuable insights as well as being able to categorize, predict, and make evidence-based decisions in novel ways. These tools can be used to reduce the reliance on expert experience and laborious computations inherent in existing numerical theories and methods. In this paper, we delve into the essence of science paradigms, the evolution of computing intelligence, and provide a comprehensive overview of the key applications driving the development of a new intelligence paradigm in CFD simulations. In addition, we outline a prototype platform for CFD simulations within this new paradigm. Based on this platform, three intelligent workflows are proposed, anticipating to serve as a reference source for future research and foster the emergence of innovative applications in the field of CFD.Highlights Deep learning techniques emerged as a new method to create automated, intelligent tools for CFD simulations.A review of deep learning methods for mesh pre-processing.A review of deep learning methods for numerical solving.A review of deep learning methods for post-processing visualization.A prototype platform for CFD simulations within the new paradigm.Perspectives on challenges and future directions.

Objectives The study aimed to conduct an in-depth analysis of the influence of PD-L1 status and expression levels and other variables on the effectiveness of immune checkpoint inhibitors (ICIs) in treating breast cancer. Methods A total of 19 articles, involving 16 trials and 7899 patients, were included in the analysis. The outcomes of interest were odds-ratio (OR) for pathological complete response (pCR) in early breast cancer, and hazard ratio (HR) for progression-free survival (PFS) and overall survival (OS) in advanced breast cancer. Results In early breast cancer, individuals with PD-L1-positive tumors were more likely to benefit from ICIs than those with PD-L1-negative tumors. Furthermore, patients with PD-L1 positivity in immune cells (IC) had superior outcomes compared to those scoring positively on combined positive score (CPS), with ORs for ICIs benefit being 2.28 for IC-positive patients vs 1.78 for CPS-positive patients. Regarding the impact of breast cancer subtypes on the efficacy of ICIs, our findings indicated that triple-negative breast cancer (TNBC) exhibits the greatest therapeutic response with OR of 1.93, followed by the hormone receptor-positive (HoR+) / human epidermal growth factor receptor 2-negative (HER2−), while the HER2+ was the worst. Additionally, age was identified as a key predictive factor in responding to ICIs. In advanced breast cancer, there was an upward trend in CPS values associated with enhanced ICIs responsiveness, with the predictive value increasing from 12% at a CPS threshold of 10 to 13.6% at 20. Conclusion The study concluded that the PD-L1 expression scoring system effectively discriminates between patients with breast cancer in terms of the degree of benefit they may attain from ICIs. Patients with little or no PD-L1 expression experienced a diminished therapeutic benefit from ICIs.

Abstract Bacterial infections, especially those caused by multidrug‐resistant pathogens, pose a significant threat to public health. Vaccines are a crucial tool in fighting these infections; however, no clinically available vaccine exists for the most common bacterial infections, such as those caused by Pseudomonas aeruginosa. Herein, a multiantigenic antibacterial nanovaccine (AuNP@HMV@SPs) is reported to combat P. aeruginosa infections. This nanovaccine utilizes the hybrid membrane vesicles (HMVs) created by fusing macrophage membrane vesicles (MMVs) with bacterial outer membrane vesicles (OMVs). The HMVs mitigate the toxic effects of both OMVs and bacterial secreted toxins (SP) adsorbed on the surface of MMVs, while preserving their stimulating properties. Gold nanoparticles (AuNPs) are utilized as adjuvant to enhance immune response without comprising safety. The nanovaccine AuNP@HMV@SPs induces robust humoral and cellular immune responses, leading to destruction of bacterial cells and neutralization of their secreted toxins. In murine models of septicemia and pneumonia caused by P. aeruginosa, AuNP@HMV@SPs exhibits superior prophylactic efficacy compared to control groups including OMVs, or MMVs@SPs and HMV@SPs, achieving 100% survival in septicemia and > 99.9% reduction in lung bacterial load in pneumonia. This study highlights AuNP@HMV@SPs as a safe and effective antibacterial nanovaccine, targeting both bacteria and their secreted toxins, and offers a promising platform for developing multiantigenic antibacterial vaccines against multidrug‐resistant pathogens.

Road surface detection plays a pivotal role in the realm of autonomous vehicle navigation. Contemporary methodologies primarily leverage LiDAR for acquiring three-dimensional data and utilize imagery for chromatic information. However, these approaches encounter significant integration challenges, particularly due to the inherently unstructured nature of 3D point clouds. Addressing this, our novel algorithm, specifically tailored for predicting drivable areas, synergistically combines LiDAR point clouds with bidimensional imagery. Initially, it constructs an altitude discrepancy map via LiDAR, capitalizing on the height uniformity characteristic of planar road surfaces. Subsequently, we introduce an innovative and more efficacious attention mechanism, streamlined for image feature extraction. This mechanism employs adaptive weighting coefficients for the amalgamation of the altitude disparity imagery and two-dimensional image features, thereby facilitating road area delineation within a semantic segmentation framework. Empirical evaluations conducted using the KITTI dataset underscore our methodology’s superior road surface discernment and extraction precision, substantiating the efficacy of our proposed network architecture and data processing paradigms. This research endeavor seeks to propel the advancement of three-dimensional perception technology in the autonomous driving domain.

In response to the problem of enclosed internal spaces in existing honeycomb sandwich panels, the concept of an hourglass honeycomb sandwich panel model is proposed for the first time, which provides a breakthrough approach for achieving the multifunctional integration of honeycomb sandwich panels. Numerical simulation methods are employed to investigate the dynamic performance of the hourglass honeycomb sandwich panels. The focus is on discussing the influences of the geometric parameters on the deformation mode, dynamic response, load uniformity, and energy absorption capacity of the hourglass honeycomb sandwich panel under different impact velocity conditions. The research results indicate that under low-velocity-impact conditions, the influence of the geometric parameters is predominant. In contrast, under high-velocity-impact conditions, the influence of the impact velocity conditions is predominant. Hourglass honeycomb sandwich panels with low density, a large inclination angle of the honeycomb wall, and small contact distances between the hourglass honeycomb cell and the panel have excellent load uniformity, and the distances between the contact points of the hourglass honeycomb cell and the panel have a great influence on the energy absorption capacity of the sandwich panels. This study provides a theoretical basis for the application of honeycombs in aerospace and other engineering areas.

Vortex detection plays a fundamental role in turbulence research and engineering problems. However, due to the lack of a mathematically rigorous vortex definition, as well as the absence of any vortex-oriented database, both traditional and machine learning detection methods achieve only limited performance. In this paper, we develop a deep learning model for vortex detection using a weak supervision approach. In order to avoid the need for a vast amount of manual labeling work, we employ an automatic clustering approach to encode vortex-like behavior as the basis for programmatically generating large-scale, highly reliable training labels. Moreover, to speed up the clustering method, a multi-view U-Net (MVU-Net) model is proposed to approximate the clustering results using the knowledge distillation technique. A multi-view learning strategy is further applied to integrate the information across multiple variables. In addition, we propose a physics-informed loss function, which enables our model to explicitly consider the characteristics of flow fields. The results on eight real-world scientific simulation applications show that the proposed MVU-Net model significantly outperforms other state-of-the-art methods on both efficiency and accuracy.

Abstract Deep neural networks (DNNs) have recently shown great potential in solving partial differential equations (PDEs). The success of neural network-based surrogate models is attributed to their ability to learn a rich set of solution-related features. However, learning DNNs usually involves tedious training iterations to converge and requires a very large number of training data, which hinders the application of these models to complex physical contexts. To address this problem, we propose to apply the transfer learning approach to DNN-based PDE solving tasks. In our work, we create pairs of transfer experiments on Helmholtz and Navier-Stokes equations by constructing subtasks with different source terms and Reynolds numbers. We also conduct a series of experiments to investigate the degree of generality of the features between different equations. Our results demonstrate that despite differences in underlying PDE systems, the transfer methodology can lead to a significant improvement in the accuracy of the predicted solutions and achieve a maximum performance boost of 97.3% on widely used surrogate models.

Background: Methicillin-resistant Staphylococcus aureus (MRSA) has now become a major nosocomial pathogen bacteria and resistant to many antibiotics. Therefore, Development of novel approaches to combat the disease is especially important. The present study aimed to provide a novel approach involving the use of nucleotide-mediated metabolic reprogramming to tackle intractable methicillin-resistant S. aureus (MRSA) infections.Objective: This study aims to explore the bacterial effects and mechanism of uracil and gentamicin in S. aureus.Methods: Antibiotic bactericidal assays was used to determine the synergistic bactericidal effect of uracil and gentamicin. How did uracil regulate bacterial metabolism including the tricarboxylic acid (TCA) cycle by GC-MS-based metabolomics. Next, genes and activity of key enzymes in the TCA cycle, PMF, and intracellular aminoglycosides were measured. Finally, bacterial respiration, reactive oxygen species (ROS), and ATP levels were also assayed in this study.Results: In the present study, we found that uracil could synergize with aminoglycosides to kill MRSA (USA300) by 400-fold. Reprogramming metabolomics displayed uracil reprogrammed bacterial metabolism, especially enhanced the TCA cycle to elevate NADH production and proton motive force, thereby promoting the uptake of antibiotics. Furthermore, uracil increased cellular respiration and ATP production, resulting the generation of ROS. Thus, the combined activity of uracil and antibiotics induced bacterial death. Inhibition of the TCA cycle or ROS production could attenuate bactericidal efficiency. Moreover, uracil exhibited bactericidal activity in cooperation with aminoglycosides against other pathogenic bacteria. In a mouse mode of MRSA infection, the combination of gentamicin and uracil increased the survival rate of infected mice.Conclusion: Our results suggest that uracil enhances the activity of bactericidal antibiotics to kill Gram-positive bacteria by modulating bacterial metabolism.

Abstract Partial differential equations (PDEs) are ubiquitous in natural science and engineering problems. Traditional discrete methods for solving PDEs are usually time-consuming and labor-intensive due to the need for tedious mesh generation and numerical iterations. Recently, deep neural networks have shown new promise in cost-effective surrogate modeling because of their universal function approximation abilities. In this paper, we borrow the idea from physics-informed neural networks (PINNs) and propose an improved data-free surrogate model, DFS-Net. Specifically, we devise an attention-based neural structure containing a weighting mechanism to alleviate the problem of unstable or inaccurate predictions by PINNs. The proposed DFS-Net takes expanded spatial and temporal coordinates as the input and directly outputs the observables (quantities of interest). It approximates the PDE solution by minimizing the weighted residuals of the governing equations and data-fit terms, where no simulation or measured data are needed. The experimental results demonstrate that DFS-Net offers a good trade-off between accuracy and efficiency. It outperforms the widely used surrogate models in terms of prediction performance on different numerical benchmarks, including the Helmholtz, Klein–Gordon, and Navier–Stokes equations.

Infections caused by antibiotic-resistant bacteria (ARB) are one of the major global health challenges of our time. In addition to developing new antibiotics to combat ARB, sensitizing ARB, or pursuing alternatives to existing antibiotics are promising options to counter antibiotic resistance. This review compiles the most promising anti-ARB strategies currently under development. These strategies include the following: (i) discovery of novel antibiotics by modification of existing antibiotics, screening of small-molecule libraries, or exploration of peculiar places; (ii) improvement in the efficacy of existing antibiotics through metabolic stimulation or by loading a novel, more efficient delivery systems; (iii) development of alternatives to conventional antibiotics such as bacteriophages and their encoded endolysins, anti-biofilm drugs, probiotics, nanomaterials, vaccines, and antibody therapies. Clinical or preclinical studies show that these treatments possess great potential against ARB. Some anti-ARB products are expected to become commercially available in the near future.

One of the difficult requirements imposed on high-quality CFD mesh generation has been the ability to evaluate the mesh quality efficiently. Due to the lack of a general and effective evaluating criterion, the current mesh quality evaluation task mainly relies on various quality metrics for the shape of mesh elements, such as angle, radius, edge and contextual information collected by pre-processing software. However, this line of methods greatly increases the pre-processing cost and may not guarantee a precise quality result. In this paper, we provide a solution to solve the mentioned issues, resulting in a CNN model GridNet and the first mesh dataset NACA-Market. GridNet takes the mesh file as input and then automatically evaluates the mesh quality. Experiment results show that GridNet is capable of performing automatic mesh quality evaluation and outperforms the widely used classifiers. We hope that the proposed large benchmark collection and network could fill in the gaps in the fields of CNN-based mesh quality evaluation and provide potential future research directions in this field.

Evaluating mesh quality prior to performing the computational fluid dynamics (CFD) simulation is an essential step to ensure the acceptable accuracy of cylinder modelling. However, traditional mesh quality indicators are often insufficient since they only check geometric information on individual distorted elements. To yield more accurate results, the current evaluation process usually requires careful manual re-evaluation for quality properties such as mesh distribution and local refinement, which heavily increase the meshing overhead. In this paper, we introduce an efficient quality indicator for varisized cylinder meshes, consisting of a mesh pre-processing method and a neural network-based indicator, Mesh-Net. We also publish a cylinder mesh benchmark dataset. The proposed indicator is trained to study the role of CFD meshes on the accuracy of numerical simulations. It considers both the effect of element geometry (e.g., orthogonality) and quality properties (e.g., smoothness and distribution). Thereafter, the well-trained indicator is used as a black-box to predict the overall quality of the input mesh automatically. Experimental results demonstrate that the proposed indicator is accurate and can be applied in the mesh quality evaluation process without manual interactions.

Staphylococcus aureus is a notorious bacterial pathogen that often causes soft tissue and bloodstream infections and invariably garners resistance mechanisms against new antibiotics. Modulation of the host immune response by metabolites is a powerful tool against bacterial infections, but has not yet been used against S. aureus infections. In this study, we identified four metabolite biomarkers: L-proline, L-isoleucine, L-leucine, and L-valine (PILV), through a metabolomics study using animal models of S. aureus bloodstream infection. The exogenous administration of each metabolite or of PILV showed anti-infective effects, and a higher protection was achieved with PILV in comparison to individual metabolites. During the staphylococcal infection, the expression of most host arginase and nitric oxide synthase (NOS) isozymes was simultaneously induced in mouse liver, kidney, and blood samples. However, the induction of arginase isozymes was dramatically stronger than that of NOS isozymes. This elevated arginase activity was inhibited by the metabolite biomarkers thus killing S. aureus, and PILV exhibited the strongest inhibition of arginase activity and bacterial inhibition. The suppression of arginase activity also contributed to the metabolite-mediated phagocytic killing of S. aureus in mouse and human blood. Our findings demonstrate the metabolite-mediated arginase inhibition as a therapeutic intervention for S. aureus infection.