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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.

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.

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.

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.

Gram-positive organisms with their thick envelope cannot be lysed by complement alone. Nonetheless, antibody-binding on the surface can recruit complement and mark these invaders for uptake and killing by phagocytes, a process known as opsonophagocytosis. The crystallizable fragment of immunoglobulins (Fc?) is key for complement recruitment. The cell surface of S. aureus is coated with Staphylococcal protein A (SpA). SpA captures the Fc? domain of IgG and interferes with opsonization by anti-S. aureus antibodies. In principle, the Fc? domain of therapeutic antibodies could be engineered to avoid the inhibitory activity of SpA. However, the SpA-binding site on Fc? overlaps with that of the neonatal Fc receptor (FcRn), an interaction that is critical for prolonging the half-life of serum IgG. This evolutionary adaptation poses a challenge for the exploration of Fc? mutants that can both weaken SpA-IgG interactions and retain stability. Here, we use both wildtype and transgenic human FcRn mice to identify antibodies with enhanced half-life and increased opsonophagocytic killing in models of S. aureus infection and demonstrate that antibody-based immunotherapy can be improved by modifying Fc?. Our experiments also show that by competing for FcRn-binding, staphylococci effectively reduce the half-life of antibodies during infection. These observations may have profound impact in treating cancer, autoimmune, and asthma patients colonized or infected with S. aureus and undergoing monoclonal antibody treatment. [ABSTRACT FROM AUTHOR]

Hydrovoltaic technology has achieved notable breakthroughs in electric output via using the moving boundary of electric double layer, but the output voltage induced by droplets is saturated around 350 volts, and the underlying mechanism remains to be further clarified. Here, we show that fallingwater droplets can stably spark an unprecedented voltage up to 1200 volts within microseconds that they contact an electrode placed on top of an electret surface, approaching the theoretical upper limit. This sparking potential can be explained and described by a comprehensive model considering thewater-electrode contact dynamics from both the macroscale droplet spreading and the microscale electric double layer formation, as well as the presence of a circuit capacitance. It is demonstrated that a droplet-induced electric spark is sufficient to directly ionize gas at atmospheric pressure and split water into hydrogen and oxygen, showing wide application potential in fields of green energy and intelligence. [ABSTRACT FROM AUTHOR]