Search

The Chinese traditional medicinal plants Rheum palmatum L., Scutellaria baicalensis Georgi, and Houttuynia cordata Thunb in a ratio of 108:65:27 form a compound named Dahuang Qinyu San (DQS), which inhibits and kills Escherichia coli and Salmonella to a certain extent in fish and shrimp aquaculture environments. The active ingredients quercetin, emodin, baicalin, and aloe-emodin are obtained from the semi-biomimetic extract of DQS (SEDQS). However, the antibacterial mechanism of SEDQS against Salmonella is still unclear. This study used the microwell-plate method to determine the Minimum Inhibitory Concentration (MIC) of SEDQS against Salmonella enteritidis (S. enteritidis) isolated from geese. In addition, the effect of SEDQS on the growth curve, respiratory metabolic system, cell wall, soluble protein, and nucleic acid in bacterial liquid of S. enteritidis was detected by spectrophotometer and reagent kit. The effects of SEDQS on S. enteritidis DNA, binding gel blocking, virulence gene expression, and pathogenicity-related proteins were determined by gel electrophoresis, SDS–PAGE, and fluorescence quantitative PCR. The study found that a concentration of 1/4 MIC−2 MIC (2.27–18.2 mg/ml) SEDQS can significantly inhibit the normal growth of S. enteritidis, destroy the cell membrane structure of bacteria resulting in the leak of nucleic acid, protein, and other contents (P < 0.01). It also significantly inhibited the activities of succinate dehydrogenase (SDH) and malate dehydrogenase (MDH; P < 0.01) in a concentration-dependent manner. When the concentration of SEDQS was 1/2 MIC to 2 MIC (4.55–18.2 mg/ml), the expression levels of gyrB, fimA, filC, spvR, Hcp, and vgrG virulence genes (P < 0.01) all decreased by more than 31, 11, 18, 30, 34, and 21% respectively compared with the control group. SEDQS could significantly inhibit the expression of six virulence genes and play an important role in the pathogenicity of the S. enteritidis infected host. The SEDQS could exert antibacterial pharmacological effects by inhibiting the growth and metabolism of S. enteritidis and inhibiting the expression of major virulence factors. It has potential application value as an antibiotic alternative.

Additional file 1: Supplementary Fig. 1. Levels of immune cell infiltration, expression of tumor regulatory moleculesin lung cancer patients with different mast cell proportions.

Splitting traffic packets from one stream into multiple network paths is a novel hotspot for defending eavesdropping attacks. It can increase the difficulties of eavesdropping effectively. However, due to heterogeneous network characteristics, forwarding traffic packets via different network paths causes an extremely severe out-of-order problem. Whats worse, the problem becomes a stumbling block to the bandwidth aggregation of multiple network paths. It is wasteful for bandwidth resources of multiple network paths. Therefore, this paper provides an adaptive multipath scheduling (AMS) mechanism to not only increase difficulties of eavesdropping but also aggregate network bandwidths of multiple paths effectively. Specifically, AMS has two general forwarding blocks: network-paths-selection block and packets-scheduling block. The network-paths-selection block predicts network characteristics for multiple available paths, chooses three network paths whose characteristics are similar, and distributes traffic packets into the selected paths to defend against eavesdropping attacks. According to the network path characteristics, the packets-scheduling block decides how many and whats order traffic packets should be split into each selected path to mitigate the out-of-order problem. Using a paradigm of programmable data plane—P4, We implement the proposed mechanism and evaluate its performances. Experimental results show that AMS decreases the out-of-order ratio by 48% and increases transmission throughput by 74% compared with the baseline mechanism.

With the rapid development of network, network monitoring is a significance means to ensure network security and reliability. In-band network telemetry (INT) can collect items in line-rate, and support large traffic volumes and rates network telemetry. However, existing INT monitoring schemes are quite limited in flexibly expanding the execution monitoring tasks. In this paper, we propose Fast-INT, an efficient network monitoring framework combined with learning. The goal of Fast-INT is to design a light-weight INT network collection framework by quickly implementing dynamic and scalable collection of network status information. In our approach, an INT scheduling algorithm based on reinforcement learning is designed to dynamically deploy and adjust INT monitoring tasks when dealing with network inner change event. Particularly, Fast-INT can implement specific INT monitoring tasks on target point to shorten the time of monitoring and make the network monitoring more efficient. The evaluate results show that Fast-INT has a good performance on network monitoring and achieves the goal of intelligently deploying network monitoring tasks.

Distributed message systems (DMSs) serve as the communication backbone for many real-time streaming data processing applications. To support the vast diversity of such applications, DMSs provide a large number of parameters to configure. However, It overwhelms for most users to configure these parameters well for better performance. Although many automatic configuration approaches have been proposed to address this issue, critical challenges still remain: 1) to train a better and robust performance prediction model using a limited number of samples, and 2) to search for a high-dimensional parameter space efficiently within a time constraint. In this paper, we propose AutoConfig -- an automatic configuration system that can optimize producer-side throughput on DMSs. AutoConfig constructs a novel comparison-based model (CBM) that is more robust that the prediction-based model (PBM) used by previous learning-based approaches. Furthermore, AutoConfig uses a weighted Latin hypercube sampling (wLHS) approach to select a set of samples that can provide a better coverage over the high-dimensional parameter space. wLHS allows AutoConfig to search for more promising configurations using the trained CBM. We have implemented AutoConfig on the Kafka platform, and evaluated it using eight different testing scenarios deployed on a public cloud. Experimental results show that our CBM can obtain better results than that of PBM under the same random forests based model. Furthermore, AutoConfig outperforms default configurations by 215.40% on average, and five state-of-the-art configuration algorithms by 7.21%-64.56%.