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Current learning platforms generally have problems such as fragmented knowledge, redundant information, and chaotic learning routes, which cannot meet learners' autonomous learning requirements. This paper designs a learning path recommendation system based on knowledge graphs by using the characteristics of knowledge graphs to structurally represent subject knowledge. The system uses the node centrality and node weight to expand the knowledge graph system, which can better express the structural relationship among knowledge. It applies the particle swarm fusion algorithm of multiple rounds of iterative simulated annealing to achieve the recommendation of learning paths. Furthermore, the system feeds back the students' learning situation to the teachers. Teachers check and fill in the gaps according to the performance of the learners in the teaching activities. Aiming at the weak links of students' knowledge points, the particle swarm intelligence algorithm is used to recommend learning paths and learning resources to fill in the gaps in a targeted manner.

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. [ABSTRACT FROM AUTHOR]

Objective To explore the role of apoliprotein D (APOD) in the proliferation and migration of human dental pulp cells (DPCs) and to provide a basis for the use of APOD to promote pulp regeneration. Methods APOD ex⁃ pression in human dental pulp cells was inhibited by siRNA. The inhibition effect of APOD was confirmed by qPCR and Western blot. After APOD inhibition, colony formation experiments and CCK8 assays were employed to confirm the proliferation ability of dental pulp cells. Transwell assays were used to verify the cell migration ability after the inhibi⁃ tion of APOD expression. Results After inhibiting APOD expression, the colony formation rate in the si⁃apod group was reduced compared with the NC group, and the difference was statistically significant (t＝7.624, P＝0.002). The CCK8 experiment showed that the OD value in the si⁃apod group decreased at 3, 5 and 7 d compared with that in the NC group (P < 0.05). Transwell results showed that the number of cell divisions was 57.25 ± 4.03 in the si⁃apod group and 154.50 ± 8.39 in the NC group, and the difference was statistically significant (t＝10.45, P < 0.001). Conclusion Inhibition of APOD expression in dental pulp cells inhibits their proliferation and migration ability.

Image aesthetics processing (IAP) is used primarily to enhance the aesthetic quality of images. However, IAP faces several issues, including its failure to analyze the influence of visual scene information and the difficulty of deploying IAP capabilities to mobile devices. This study proposes an automatic IAP system (IAPS) for mobile devices that integrates machine learning and traditional image-processing methods. First, we employ an extremely computation-efficient deep learning model, ShuffleNet, designed for mobile devices as our scene recognition model. Then, to enable computational inferencing on resource-constrained edge devices, we use a modern mobile machine-learning library, TensorFlow Lite, to convert the model type to TFLite format. Subsequently, we adjust the image contrast and color saturation using group filtering, respectively. These methods enable us to achieve maximal aesthetic enhancement of images with minimal parameter adjustments. Finally, we use the InceptionResNet-v2 aesthetic evaluation model to rate the images. Even when employing the benchmark model with an accuracy of 70%, the score of the IAPS processing image is verified to be higher and more effective compared with a state-of-the-art smartphone's beautification function. Additionally, an anonymous questionnaire survey with 100 participants is conducted, and the result shows that IAPS enhances the aesthetic appeal of images based on the public's preferences. [ABSTRACT FROM AUTHOR]

Helicobacter pylori (H. pylori) has infected more than half of the world's population, and is the major cause of gastric cancer. The efficacy of standard antibiotic‐based triple therapy is declining due to drug resistance development. Herein, a pH‐responsive reactive oxygen species (ROS) nanogenerator (Fe‐HMME@DHA@MPN) composed of acid‐responsive metal polyphenol network (MPN) shell and mesoporous metal‐organic nanostructure core [Fe‐HMME (hematoporphyrin monomethyl ether, sonosensitizer)] loaded with dihydroartemisinin (DHA) is reported. These nanoparticles generate more ROS singlet oxygen than sonosensitizer HMME under ultrasonication, and this sonodynamic process is fueled by oxygen generated through Fenton/Fenton‐like reactions of the degraded product in gastric acid Fe (II) and hydrogen peroxide (H2O2) in the infection microenvironment. The encapsulated DHA, as a hydroperoxide source, is found to enhance the peroxidase‐like activity of the Fe‐HMME@DHA@MPN to generate ROS hydroxyl radical, beneficial for the microenvironment without sufficient H2O2. In vitro experiments demonstrate that the ROS nanogenerators are capable of killing multidrug‐resistant H. pylori and removing biofilm, and ROS nanogenerators show high therapeutic efficacy in a H. pylori infection mouse model. Unlike the triple therapy, the nanogenerators display negligible side effects toward the normal gut microbiota. Taken together, these self‐enhanced ROS nanogenerators have a great potential for treatment of H. pylori infection. [ABSTRACT FROM AUTHOR]

The post‐charging antibacterial therapy is highly promising for treatment of Gram‐negative bacterial wound infections. However, the therapeutic efficacy of the current electrode materials is yet unsatisfactory due to their low charge storage capacity and limited reactive oxygen species (ROS) yields. Herein, the design of MnOOH decorated Co3O4 nanoneedles (MCO) with exceptional post‐charging antibacterial effect against Gram‐negative bacteria at a low charge voltage and their implementation as a robust antibacterial electrode for skin wound treatment are reported. Taking advantaging of the increased active sites and enhanced OH− adsorption capability, the charge storage capacity and ROS production of the MCO electrode are remarkably boosted. As a result, the MCO electrode after charging at an ultralow voltage of 1.4 V gives a 5.49 log and 5.82 log bacterial reduction in Escherichia coli (E. coli) and Pseudomonas aeruginosa (P. aeruginosa) within an incubation time of only 5 min, respectively. More importantly, the antibacterial efficiency of the MCO electrode against multi‐drug resistant (MDR) bacteria including Klebsiella pneumoniae (K. pneumoniae) and Acinetobacter baumannii (A. baumannii) also reaches 99.999%. In addition, the MCO electrode exhibits excellent reusability, and the role of extracellular ROS in enhancing post‐charging antibacterial activity is also unraveled. [ABSTRACT FROM AUTHOR]

System capacity is an important metric for evaluating the performance of edge-caching networks. In most previous studies, the caching content was assumed to remain unchanged in a base station. Hence, the requested probability for each content was treated as stable; In fact, both the number of content and users can change overtime, which impacts the system performance. This article firstly constructs a new user-content evolution model to infer such performance changes, and analyzes the number of links between users and content (i.e., degree). Then, the requested probability of the content and the optimal copy number are calculated by using the content degree. Next, the minimum average transmission distance of the content and system capacity under the cache scheme are given. Finally, a user-content time-varying system is simulated. The results show that the proposed model can characterize the evolution relationship between users and content and achieve better performance in terms of system capacity. [ABSTRACT FROM AUTHOR]

Users can invoke various composite services in Edge Computing (EC) with the development of Service Computing. Generally, users cannot invoke the optimal composite service due to the migration of component service or failure in the edge device. In this study, a Composite Service Provisioning mechanism in EC (CSP-EC) is proposed. It starts when the location of component service changes and terminates when the optimal composite service is obtained. CSP-EC employs dynamic optimization process to meet the real-time requirements. It first caches m optimized intermediate solutions from the first m users, which can be reused by other users to get the final optimal solution. Moreover, a multipopulation Estimation of Distribution Algorithm is used to reduce the probability of falling into a local optimum, and the roulette mechanism is used to accelerate the optimization process. Extensive simulations are conducted based on the real-world dataset. The results show that the proposed mechanism achieves higher quality, better stability, and shorter execution time compared with other schemes. [ABSTRACT FROM AUTHOR]

The efficacy of conventional antibiotics therapeutics has declined rapidly due to the emerged antibiotic resistance. There is an urgent need to develop novel approaches to address the problem of antibiotic shortage, particularly for Gram‐negative bacteria. Herein, a biomimetic nanodelivery system is proposed to enhance the bacterial targeting and uptake of rifampicin (Rif), a traditional antibiotic but not effective against Gram‐negative bacteria. The biomimetic nanodelivery system (Rif@MSN@OMV) is composed of outer membrane vesicles (OMVs) isolated from E. coli as shell and rifampicin‐loaded mesoporous silica nanoparticles (MSNs) as core. The OMVs greatly improve the uptake of MSNs in E. coli, but not in Gram‐positive bacteria S. aureus, owing to the homotypic targeting function of the OMVs. The Rif@MSN@OMV exhibits enhanced antimicrobial activity against E. coli and completely eradicates bacteria at an equivalent rifampicin concentration (4 µg mL−1) while free rifampicin shows weak bactericidal activity. Meanwhile, the Rif@MSN@OMV maintains good biocompatibility both in vitro and in vivo. More importantly, the Rif@MSN@OMV elevates survival rate of infected mice and reduces bacterial load in intraperitoneal fluid and organs. Overall, the OMVs‐coated nanodelivery system provides a novel strategy to improve the antimicrobial efficacy of conventional antibiotic or repurpose drugs for treatment of Gram‐negative bacterial infections. [ABSTRACT FROM AUTHOR]