Plasma transferred arc surface alloying of Cr-Ni-Mo powders on compacted graphite iron

A Cr-Ni-Mo overlayer was deposited on the surface of compacted graphite iron (CGI) by the plasma transferred arc (PTA) alloying technique. The microstructure of Cr-Ni-Mo overlayer was characterized by optical microscopy (OM), scanning electron microscopy (SEM) equipped with energy dispersive spectroscopy (EDS), and X-ray diffractometer (XRD). Results show that the cross-section consists of four regions: alloying zone (AZ), molten zone (MZ), heat affected zone (HAZ), and the substrate (SUB). The microstructure of AZ mainly consists of cellular γ-(Fe, Ni) solid solution, residual austenite and a network of eutectic Cr7C3 carbide while the MZ area has a typical feature of white cast iron (M3C-type cementite). The martensite/ledeburite double shells are observed in the HAZ. With decreasing the concentration of Cr-Ni-Mo alloys, the fracture mode changes from ductile in the AZ to brittle in the MZ. The maximum hardness of the AZ (450 HV0.2) is lower than that of the MZ (800 HV0.2). The eutectic M3C and M7C3 carbides increase the microhardness, while the austenite decreases that of the AZ

Fast Learning-Based Split Type Prediction Algorithm for VVC

International audienceAs the latest video coding standard, Versatile Video Coding (VVC) is highly efficient at the cost of very high coding complexity, which seriously hinders its widespread application. Therefore, it is very crucial to improve its coding speed. In this paper, we propose a learning-based fast split type (ST) prediction algorithm for VVC using a deep learning approach. We first construct a large-scale database containing sufficient STs with diverse video resolution and content. Next, since the ST distributions of coding units (CUs) of different sizes are significantly distinct, so we separately design neural networks for all different CU sizes. Then, we merge ambiguous STs into four merged classes (MCs) to train models to obtain probabilities of MCs and skip unlikely ones. Experimental results demonstrate that the proposed algorithm can reduce the encoding time of VVC by 67.53% with 1.89% increase in Bjøntegaard delta bit-rate (BDBR) on average

FAST LEARNING-BASED SPLIT TYPE PREDICTION ALGORITHM FOR VVC

As the latest video coding standard, Versatile Video Coding (VVC) is highly efficient at the cost of very high coding com- plexity, which seriously hinders its widespread application. Therefore, it is very crucial to improve its coding speed. In this paper, we propose a learning-based fast split type (ST) prediction algorithm for VVC using a deep learning approach. We first construct a large-scale database containing sufficient STs with diverse video resolution and content. Next, since the ST distributions of coding units (CUs) of different sizes are significantly distinct, so we separately design neural net- works for all different CU sizes. Then, we merge ambiguous STs into four merged classes (MCs) to train models to obtain probabilities of MCs and skip unlikely ones. Experimental results demonstrate that the proposed algorithm can reduce the encoding time of VVC by 67.53% with 1.89% increase in Bjøntegaard delta bit-rate (BDBR) on average

Enhanced Nasal Mucosal Delivery and Immunogenicity of Anti-Caries DNA Vaccine through Incorporation of Anionic Liposomes in Chitosan/DNA Complexes

<div><p>The design of optimized nanoparticles offers a promising strategy to enable DNA vaccines to cross various physiological barriers for eliciting a specific and protective mucosal immunity via intranasal administration. Here, we reported a new designed nanoparticle system through incorporating anionic liposomes (AL) into chitosan/DNA (CS/DNA) complexes. With enhanced cellular uptake, the constructed AL/CS/DNA nanoparticles can deliver the anti-caries DNA vaccine pGJA-P/VAX into nasal mucosa. TEM results showed the AL/CS/DNA had a spherical structure. High DNA loading ability and effective DNA protection against nuclease were proved by gel electrophoresis. The surface charge of the AL/CS/DNA depended strongly on pH environment, enabling the intracellular release of loaded DNA via a pH-mediated manner. In comparison to the traditional CS/DNA system, our new design rendered a higher transfection efficiency and longer residence time of the AL/CS/DNA at nasal mucosal surface. These outstanding features enable the AL/CS/DNA to induce a significantly (<i>p</i><0.01) higher level of secretory IgA (SIgA) than the CS/DNA in animal study, and a longer-term mucosal immunity. On the other hand, the AL/CS/DNA exhibited minimal cytotoxicity. These results suggest that the developed nanoparticles offer a potential platform for DNA vaccine packaging and delivery for more efficient elicitation of mucosal immunity.</p></div

Analysis of nasal residence time and mucin adsorption ability of CS-based NPs.

<p>(A) Fluorescence images of nasal clearance of Cy5.5-CS/DNA and AL/Cy5.5-CS/DNA after i.n. administration. (B) Clearance derived from spectra. Fluorescence at t = 0 was set as 100%. (C) Adsorption of mucin on the CS/DNA and AL/CS/DNA. Formulations were prepared at pH 6.4; **<i>p</i><0.01 Mean ± SD (n = 4).</p

Representative Z-scan images of the FITC-CS/DNA and AL/FITC-CS/DNA treated rat nasal mucosa.

<p>(A) FITC-CS/DNA, treated for 2 h. (B) AL/FITC-CS/DNA, treated for 2 h. Both the 24 images were from the apical side to depth of 17.6 µm beneath mucosa and in successive steps with 0.8 µm apart. Images of the x, z and y, z sections captured in the depth of 8.8 µm inside mucosa from the apical side were shown below. Red arrow represents FITC-CS/DNA; Blue arrow represents AL/FITC-CS/DNA.</p

Physicochemical characteristics of CS/DNA and AL/CS/DNA.

<p>Particle size, polydispersity index (PDI), zeta potential and encapsulation efficiency of tested NPs with different N/P ratios and lipid/DNA ratios under pH 6.4 (n = 3).</p

Transgene expression by HEK 293 cells in vitro using CS/DNA, AL/CS/DNA and PEI/DNA NPs.

<p>(A) CS/Luc, at different N/P ratios with fixed 1 µg plasmid pcDNA3.0-Rluc/well and fixed transfection medium pH of 6.4; (B) AL/CS/Luc, at different lipid/DNA ratios (0, 1, 2, 3, 4, w/w) with fixed N/P ratio of 7 and fixed transfection medium pH of 6.4 and 1 µg plasmid pcDNA3.0-Rluc/well. PEI/Luc NPs at N/P ratio of 10 were used as the positive control; (C) Transfection images of CS/GFP and AL/CS/GFP NPs with different N/P ratios and lipid/DNA ratios using GFP as a report gene. The relative light units (RLU) were normalized to the protein content of each sample. **p<0.01 Mean ± SD (n = 4–6).</p

Gel retardation analysis of CS-based NPs using plasmid pGJA-p/VAX as the DNA model.

<p>(A) CS/DNA, at different N/P ratios. (B) AL/CS/DNA, at different lipid/DNA (w/w) ratios with a fixed N/P ratio of 7. (C) AL/CS/DNA, treated with DNase I at the lipid/DNA (w/w) ratio of 3 with a fixed N/P ratio of 7. lane 1: DNA ladder; lane 2: pGJA-p/VAX; lane 3: pGJA-p/VAX+DNase I (50 U/ml); lane 4: AL/CS/DNA; lane 5: AL/CS/DNA+DNase I (50 U/ml); lane 6: AL/CS/DNA+DNase I (100 U/ml); lane 7: AL/CS/DNA+DNase I (200 U/ml); lane 8: PEI/DNA+DNase I (200 U/ml); lane 9: PEI/DNA.</p

Characterization of the CS/DNA and AL/CS/DNA NPs.

<p>(A) Size and zeta potential of CS/DNA and AL/CS/DNA at pH 7.4, 7.1, 6.8 and 6.4. (B) TEM images of the CS/DNA at pH 6.4; (C) TEM images of the AL/CS/DNA at pH 6.4. (D) TEM images of the AL/CS/DNA at pH 7.4; (E) Local amplification of (D); (F) Release profile of DNA from AL/CS/DNA and CS/DNA in PBS (pH 6.4 or 7.4) at 37°C. Mean ± SD (n = 3).</p