Avian Tembusu virus infection effectively triggers host innate immune response through MDA5 and TLR3-dependent signaling pathways

Additional file 4 ATMUV infection causes significant up-regulation of TLR3 and MDA5. RT-PCR was performed to examine the mRNA expression of TLR3 and MDA5 in CEF (A), chickens (B) and 293T cells (C) at the indicated time after ATMUV infection, respectively

Traffic Feature Selection and Distributed Denial of Service Attack Detection in Software-Defined Networks Based on Machine Learning

As 5G technology becomes more widespread, the significant improvement in network speed and connection density has introduced more challenges to network security. In particular, distributed denial of service (DDoS) attacks have become more frequent and complex in software-defined network (SDN) environments. The complexity and diversity of 5G networks result in a great deal of unnecessary features, which may introduce noise into the detection process of an intrusion detection system (IDS) and reduce the generalization ability of the model. This paper aims to improve the performance of the IDS in 5G networks, especially in terms of detection speed and accuracy. It proposes an innovative feature selection (FS) method to filter out the most representative and distinguishing features from network traffic data to improve the robustness and detection efficiency of the IDS. To confirm the suggested method’s efficacy, this paper uses four common machine learning (ML) models to evaluate the InSDN, CICIDS2017, and CICIDS2018 datasets and conducts real-time DDoS attack detection on the simulation platform. According to experimental results, the suggested FS technique may match 5G network requirements for high speed and high reliability of the IDS while also drastically cutting down on detection time and preserving or improving DDoS detection accuracy

Effect of Sc and Zr Additions on Recrystallization Behavior and Intergranular Corrosion Resistance of Al-Zn-Mg-Cu Alloys

The recrystallization and intergranular corrosion behaviors impacted by the additions of Sc and Zr in Al-Zn-Mg-Cu alloys are investigated. The stronger effect of coherent Al3(Sc1−xZrx) phases on pinning dislocation resulted in a lower degree of recrystallization in Al-Zn-Mg-Cu-Sc-Zr alloy, while the subgrain boundaries can escape from the pinning of Al3Zr phases and merge with each other, bringing about a higher degree of recrystallization in Al-Zn-Mg-Cu-Zr alloy. A low degree of recrystallization promotes the precipitation of grain boundary precipitates (GBPs) with a discontinuous distribution, contributing to the high corrosion resistance of Al-Zn-Mg-Cu-Sc-Zr alloy in the central layer. The primary Al3(Sc1−xZrx) phase promotes recrystallization due to particle-stimulated nucleation (PSN), and acts as the cathode to stimulate an accelerated electrochemical process between the primary Al3(Sc1−xZrx) particles and GBPs, resulting in a sharp decrease of the corrosion resistance in the surface layer of Al-Zn-Mg-Cu-Sc-Zr alloy

The Prediction of the Undercooling Degree of As-Cast Irons and Aluminum Alloys via Machine Learning

As-cast irons and aluminum alloys are used in various industrial fields and their phase and microstructure properties are strongly affected by the undercooling degree. However, existing studies regarding the undercooling degree are mostly limited to qualitative analyses. In this paper, a quantitative analysis of the undercooling degree is performed by collecting experimental data and employing machine learning. Nine machining learning models including Random Forest (RF), eXtreme Gradient Boosting (XGBOOST), Ridge Regression (RIDGE) and Gradient Boosting Regressor (GBDT) methods are used to predict the undercooling degree via six features, which include the cooling rate (CR), mean atomic covalence radius (MAR) and mismatch (MM). Four additional effective models of machine learning algorithms are then selected for a further analysis and cross-validation. Finally, the optimal machine learning model is selected for the dataset and the best combination of features is found by comparing the prediction accuracy of all possible feature combinations. It is found that RF model with CR and MAR features has the optimal performance results for predicting the undercooling degree

A correlational study of scoliosis and trunk balance in adult patients with mandibular deviation.

Previous studies have confirmed that patients with mandibular deviation often have abnormal morphology of their cervical vertebrae. However, the relationship between mandibular deviation, scoliosis, and trunk balance has not been studied. Currently, mandibular deviation is usually treated as a single pathology, which leads to poor clinical efficiency. We investigated the relationship of spine coronal morphology and trunk balance in adult patients with mandibular deviation, and compared the finding to those in healthy volunteers. 35 adult patients with skeletal mandibular deviation and 10 healthy volunteers underwent anterior X-ray films of the head and posteroanterior X-ray films of the spine. Landmarks and lines were drawn and measured on these films. The axis distance method was used to measure the degree of scoliosis and the balance angle method was used to measure trunk balance. The relationship of mandibular deviation, spine coronal morphology and trunk balance was evaluated with the Pearson correlation method. The spine coronal morphology of patients with mandibular deviation demonstrated an "S" type curve, while a straight line parallel with the gravity line was found in the control group (significant difference, p<0.01). The trunk balance of patients with mandibular deviation was disturbed (imbalance angle >1°), while the control group had a normal trunk balance (imbalance angle <1°). There was a significant difference between the two groups (p<0.01). The degree of scoliosis and shoulder imbalance correlated with the degree of mandibular deviation, and presented a linear trend. The direction of mandibular deviation was the same as that of the lateral bending of thoracolumbar vertebrae, which was opposite to the direction of lateral bending of cervical vertebrae. Our study shows the degree of mandibular deviation has a high correlation with the degree of scoliosis and trunk imbalance, all the three deformities should be clinically evaluated in the management of mandibular deviation

Synthesis and Modification of Polycarboxylate Superplasticizers—A Review

The molecular-scale structural changes in polycarboxylic superplasticizer (PCE) can influence dispersion and water retention. Polycarboxylate superplasticizer, synthesized using different methods, may alter dispersion and water-reducing effects. The synthesis of PCE involves creating a novel macromolecular monomer with a controllable molecular mass, adjustable lipophilic, and hydrophilic moieties, as outlined in this study. This article reviews processes for synthesizing polycarboxylates and identifies the optimal method through orthogonal experiments to produce a modified polycarboxylate superplasticizer (PCE-P). The study investigated the effects of different PCE types and concentrations on the surface tension, fluidity, and ζ potential of cement paste. PCE-P, synthesized at room temperature, showed comparable performances in initial hydration and conversion rate in cement to PCE synthesized at high temperatures. PCE-P exhibited an increased slump but had a wider molecular weight distribution and longer main and side chains, leading to a 24.04% decrease in surface tension, indicating a good dispersibility

Microscopic Observation of Metal-Containing Particles from Chinese Continental Outflow Observed from a Non-Industrial Site

Atmospheric metal-containing particles adversely affect human health because of their physiological toxicity. Mixing state, size, phase, aspect ratio, and sphericity of individual metal-containing particles collected in Hong Kong air in winter are examined through transmission electron microscopy (TEM). Eighteen percent of the sulfate particles have one or more tiny metal inclusions. Size distributions of metal and fly ash particles (or inclusions) with diameters from 15 nm to 2.7 μm show the same peak at 210 nm. The major metal particles were classified as Fe-rich (e.g., hematite), Zn-rich (e.g., zinc sulfate and zinc oxide), Pb-rich (e.g., anglesite), Mn-rich, and As-rich, which were likely emitted from industries and coal-fired power plants at high temperatures in mainland China. Compared to fly ash and S-rich particles, metal particles display a lower sphericity of 0.51 and a higher aspect ratio of 1.47, which means their shapes are poorly defined. The elemental mapping of individual particles reveal that sulfate areas without metal inclusions also contain minor Fe, Mn, or Zn. Therefore, the internal mixing of metals and acidic constituents likely solubilize metals and modify metal inclusion shapes. Solubilization of metals in airborne particles can extend their toxicity into nontoxicity parts in the particles. The structure of the metal-containing particles may provide important information for assessing health effects of fine sulfate and nitrate particles with metal inclusions in urban areas

Standing anteroposterior X-ray film of the full-length spine.

<p>The midpoint of the 7th cervical vertebra was designated as point D, the midpoint of pubic symphysis point E, and the shoulder peaks as points F and G. In the cervical and thoracic vertebrae, the midpoints of the upper most scoliotic vertebra were designated as points H and I.</p