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Enterobacter cloacae is widely distributed in the aquatic environment, and has been determined as a novel pathogen of various aquatic animals recently. Our previous studies have indicated E. cloacae caused repeated infections in Macrobrachium rosenbergii, suggesting a high survival ability of the bacteria, and rpoS gene has been known to regulate stress response and virulence of many bacteria. In this study, the E. cloacae-rpoS RNAi strain was constructed by RNAi technology, and the regulation role of rpoS in stress resistance and virulence of E. cloacae was explored by transcriptomic and phenotype analysis. The transcriptome analysis showed a total of 488 differentially expressed genes (DEGs) were identified between rpoS-RNAi and wild-type strains, including 30 up-regulated genes and 458 down-regulated genes, and these down-regulated DEGs were mainly related to environmental response, biofilm formation, bacterial type II secretory system, flagellin, fimbrillin, and chemotactic protein which associated with bacterial survival and virulence. The phenotype changes also showed the E. cloacae-rpoS RNAi strain exhibited significantly decreasing abilities of survival in environmental stresses (starvation, salinity, low pH, and oxidative stress), biofilm production, movement, adhesion to cells, pathogenicity, and colonization to M. rosenbergii. These results reveal that rpoS plays an important regulatory role in environmental stress adaptation and virulence of E. cloacae.

Objective To determine the potent and broad neutralizing monoclonal antibody (mAb) against enterovirus A (EV-A) in vitro and in vivo induced by enterovirus A71(EVA71) and coxsackievirus 16 (CVA16) co-immunization. Methods The mAb was Generated by co-immunization with EVA71 and CVA16 through hybridomas technology. The characteristics and neutralizing ability of mAb were analysed in vitro and in mice. Results We screened three mAb, the IgM antibody M20 and IgG antibody B1 and C31. All three antibodies showed cross-reactivity against tetra-EV-As. However, M20 showed potent and broad neutralizing ability against tetra-EV-As than B1 and C31. Meanwhile, M20 provided cross-antiviral efficacy in tetra-EV-As orally infected mice. Moreover, M20 binds to a conserved neutralizing epitope within the GH loop of tetra-EV-As VP1. Conclusions M20 and its property exhibited potent and broad antiviral activity against tetra-EV-As, and that is expected to be a potential preventive and therapeutic candidate against EV-As.

In this study, MgO/Mg composites were prepared using direct melt oxidation to verify the effects of elastic deformation and microplastic deformation on the damping properties. It was found that the composites have high damping properties at a certain strain amplitude, which indicated that the damping properties of the magnesium matrix were effectively enhanced by the in situ-synthesized oxide particle. In addition, other damping mechanisms different from the G–L dislocation damping mechanism exist in MgO/Mg composites, i.e., the damping mechanism of the microplastic deformation, leading to a model of microplastic deformation damping established and its mechanistic analysis.

Graph Neural Networks (GNNs) have attracted increasing attention in recent years and have achieved excellent performance in semi-supervised node classification tasks. The success of most GNNs relies on one fundamental assumption, i.e., the original graph structure data is available. However, recent studies have shown that GNNs are vulnerable to the complex underlying structure of the graph, making it necessary to learn comprehensive and robust graph structures for downstream tasks, rather than relying only on the raw graph structure. In light of this, we seek to learn optimal graph structures for downstream tasks and propose a novel framework for semi-supervised classification. Specifically, based on the structural context information of graph and node representations, we encode the complex interactions in semantics and generate semantic graphs to preserve the global structure. Moreover, we develop a novel multi-measure attention layer to optimize the similarity rather than prescribing it a priori, so that the similarity can be adaptively evaluated by integrating measures. These graphs are fused and optimized together with GNN towards semi-supervised classification objective. Extensive experiments and ablation studies on six real-world datasets clearly demonstrate the effectiveness of our proposed model and the contribution of each component.

Additional file 1: Table S1. NMR Data for epicoccamide A (1). Table S2. Comparison of the signature residues for amino acid selection by adenylation (A) domains of fungal PKS-NRPS (NRPS). Figure S1. The 1H NMR spectrum of compound 1. Figure S2. The 13C NMR spectrum of compound 1. Figure S3. The 1H-1H COSY spectrum of compound 1 (in DMSO). Figure S4. The HSQC spectrum of compound 1 (in DMSO). Figure S5. The HMBC spectrum of compound 1 (in DMSO). Figure S6. HRESIMS spectrum of compound 1. Figure S7. ESI MS/MS (-) spectrum of compound 1.

In recent years, energy storage power stations gradually merge into data centers. This paper studies the traffic scheduling problem of the energy storage data center network to improve network performance. A traffic scheduling scheme based on differential algorithms and congestion is proposed with the idea of separating the control plane from the data plane of the Software Defined Network. This scheme is divided into two parts: the introduction and improvement of the differential algorithm for unified scheduling of multiple traffic with the same sources and sinks; the concept of congestion degree is proposed for the remaining single traffic to be processed. This paper uses the Mininet network simulation platform and Ryu controller to perform simulation verification in four aspects of standardized throughput, link utilization, load balancing, and link bandwidth utilization. The comparative experiments show that the strategy proposed in this paper can greatly improve the network performance in the above aspects.

Phosphatidylcholine-soybean protein isolate (PC-SPI) nanoemulsions were prepared by ultrasonication. The effects of preparation conditions (SPI and PC addition, ultrasonic power and time) on the structural properties of the nanoemulsions and their storage stability were investigated. The results showed that the most optimal adsorption capacity and adsorption tightness at the oil–water interface under optimal conditions (1.5% SPI, 0.20% PC, 500 W ultrasonic power and 9 min ultrasonic time) were exhibited by the SPI-PC conjugate, which demonstrated that this nanoemulsions can be categorized as a high-quality emulsion suitable for research. To test its stability, and the high-quality nanoemulsion of β-carotene was stored. After degradation of the nanoemulsions during storage, β-carotene was released. The β-carotene retention rate of the high-quality emulsion was maintained above 86% at different temperatures in the absence of light for up to 30 days. This study provides new information for the development of transport and stability systems for nanoemulsions.

As the scale of data center networks (DCNs) continues to expand, software-defined network (SDN), as an innovative network architecture, gradually applies to data centers. SDN realizes flexible control of network traffic by separating control from forwarding. The traditional traffic scheduling algorithm in the network cannot meet the requirements of DCN service, and cannot fully utilize the redundant bandwidth in DCN topology. Therefore, in this paper, for the specific topology and traffic characteristics of DCNs, we propose a traffic scheduling scheme based on SDN. The scheme initially aggregates the elephant flows with the same source and destination, and then exploits the improved fruit fly optimization algorithm to uniformly schedule the aggregated elephant flows. The simulation results show that compared with the classical traffic scheduling algorithm, the improved fruit fly optimized scheduling algorithm (IFOSA) effectively improves the network performance, especially in terms of load balancing, average throughput, and link bandwidth utilization.

Objectives: Enterovirus 71 (EV71) and Coxsackievirus A16 (CA16) remain the major pathogens in hand, foot, and mouth disease (HFMD) cases, but the mechanisms of the different pathogeneses that follow EV71 and CA16 infection remain largely unknown. Methods: Herein, we utilized microRNA (miRNA) deep sequencing to investigate the roles of novel differentially expressed miRNAs in peripheral blood mononuclear cells (PBMCs) infected with EV71 and CA16. Results: The results identified 13 novel differentially expressed miRNAs in each group. Additionally, the target genes were predicted by the miRanda and RNAhybrid programs, and a total of 2,501 targets were found in the two databases. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that these targets were mainly involved in cell development and were associated with nervous system development, system development, multicellular organism development, the Wnt signaling pathway, the PDGF signaling pathway, and the EGF receptor signaling pathway. Finally, a coexpression regulatory network was built with the key targets to further extrapolate the functional interactions of the targets and their coexpressed genes. Conclusion: Our results not only revealed potential biomarkers or targets for the diagnosis and treatment of HFMD, but also provided new insights to explore the mechanisms of EV71 and CA16 pathogenesis.

With the advancement of the times, the combination of energy storage power stations and data centers (DCs) has become increasingly close. Software-defined network (SDN), as an advanced network architecture, is also gradually applied to the data center network (DCN), which plays a huge role in the network traffic scheduling. DC has the specific topology and traffic characteristics. Therefore, in the context of energy storage DC, we propose a network traffic scheduling strategy for processing long flows based on SDN in this paper. In the strategy, long flows can be divided into two parts: aggregated long flows and non-aggregated long flows. We use different algorithms to schedule these two kinds of flows. The aggregated long flows here refer to the long flows with the same source and destination. We compare our proposed strategy with the other classical traffic scheduling algorithms, the simulation results perform better in average throughput, load balancing, and link bandwidth utilization.

Tree shrews (Tupaia belangeri) are small squirrel-like mammals closely related to primates. Due to their susceptibility to several human viruses, tree shrews have been proposed as potential animal models for the study of human viral infections. However, there are no standardized assays currently available for the detection of tree shrew-specific interferon (IFN)-γ, a major cytokine secreted during the antiviral immune response. Herein, we developed a novel enzyme-linked immunosorbent assay (ELISA) for the quantification of IFN-γ in tree shrew serum samples. Tree shrew-specific IFN-γ was expressed in Escherichia coli via fusion with glutathione S-transferase (GST-TS-IFN-γ) to obtain recombinant IFN-γ. To generate anti-IFN-γ monoclonal antibodies, mice were immunized with the GST-TS-IFN-γ recombinant fusion protein, and hybridoma cell lines were established. Similarly, anti-IFN-γ polyclonal antibodies were obtained from immunized rabbits, purified, and conjugated to horseradish peroxidase (HRP). Based on the results obtained from the antibody matching test, we optimized the monoclonal antibody (1:2000) and the HRP-conjugated polyclonal antibody (1:8000) as coating and detection antibodies, respectively. Titration curves were generated with recombinant IFN-γ to develop a sensitive sandwich ELISA; the lowest detection limit of the assay was 20 ng/mL. We also tested mitogen-stimulated tree shrew blood samples in this ELISA, and found significantly higher levels of IFN-γ in the stimulated versus the unstimulated samples. Most importantly, our ELISA system detected native IFN-γ in serum samples from 50 healthy tree shrews. We have thus developed a novel ELISA, and have demonstrated the first ELISA-based measurement of IFN-γ in tree shrew serum samples.