Effects of Diesel Oxidation Catalyst on Nanostructure and Reactivity of Diesel Soot

In order to investigate the nanostructure changes of diesel soot during the oxidation process, two different types of diesel soot were collected, and their nanostructures were studied on the basis of thermogravimetric analysis and high-resolution transmission electron microscopy analysis. This work shows that the nanostructure alone does not dictate the reactivity of diesel soot, but rather, the oxidation mechanism has a strong effect on the oxidative reactivity. Soot emitted directly from the engine is oxidized under the surface burning mode, which makes the soot retain the typical core–shell structure. However, the diesel oxidation catalyst (DOC) has an influence on the oxidation mechanism of diesel soot as well as the evolution of nanostructure during the oxidation process. Soot sampled after DOC mainly undergoes an internal burning oxidation process that makes the oxidation more rapid, leading to a hollow capsule-like structure during the early stage of oxidation. However, soot becomes less reactive due to the surface burning mode and the more closed outer shell built by the rearrangement of carbon lamellae during the later stage of oxidation

A new phenol glycoside from <i>Physalis angulata</i>

<p>A new phenol glycoside, physanguloside A (<b>1</b>), was isolated from <i>Physalis angulata</i> together with four known compounds. We report herein, for the first time, the presence of compounds <b>2</b>–<b>5</b> in the genus <i>Physalis</i>. The structures of all the compounds were established by NMR, IR, UV and HRESIMS spectroscopic analyses, and comparison with the literature data. All isolated compounds were assayed for inhibitory activity on nitric oxide production by LPS-induced in RAW 264.7 macrophages.</p

Expression levels of significantly changed genes identified by RNA-seq in both Vγ1⁺ and Vγ4⁺ γδ T cells after PMA/Inomycin treatment.

<p>According to the expression abundance, transcripts were divided into 5 categories: “−” (<1 RPKM), “+” (1–10 RPKM), “++”(10–50 RPKM), “+++” (50–100 RPKM), and “++++”(>100 RPKM). RPKM, Reads Per Kilo bases per Million reads.</p><p>Expression levels of significantly changed genes identified by RNA-seq in both Vγ1⁺ and Vγ4⁺ γδ T cells after PMA/Inomycin treatment.</p

Sequencing reads and mapping rates of each sample.

<p>γ1-PBS, Vγ1⁺ γδ T cells treated with PBS; γ1-PMA/Ion, Vγ1⁺ γδ T cells treated with PMA and Ionomycin; γ4-PBS, Vγ4⁺ γδ T cells treated with PBS; γ4-PMA/Ion, Vγ4⁺ γδ T cells treated with PMA and Ionomycin.</p><p>Sequencing reads and mapping rates of each sample.</p

Surface Engineering of Copper Foils for Growing Centimeter-Sized Single-Crystalline Graphene

The controlled growth of high-quality graphene on a large scale is of central importance for applications in electronics and optoelectronics. To minimize the adverse impacts of grain boundaries in large-area polycrystalline graphene, the synthesis of large single crystals of monolayer graphene is one of the key challenges for graphene production. Here, we develop a facile surface-engineering method to grow large single-crystalline monolayer graphene by the passivation of the active sites and the control of graphene nucleation on copper surface using the melamine pretreatment. Centimeter-sized hexagonal single-crystal graphene domains were successfully grown, which exhibit ultrahigh carrier mobilities exceeding 25 000 cm² V^–1 s^–1 and quantum Hall effects on SiO₂ substrates. The underlying mechanism of melamine pretreatments were systematically investigated through elemental analyses of copper surface in the growth process of large single-crystals. This present work provides a surface design of a catalytic substrate for the controlled growth of large-area graphene single crystals

Global Characterization of Differential Gene Expression Profiles in Mouse Vγ1⁺ and Vγ4⁺ γδ T Cells

<div><p>Peripheral γδ T cells in mice are classified into two major subpopulations, Vγ1⁺ and Vγ4⁺, based on the composition of T cell receptors. However, their intrinsic differences remain unclear. In this study, we analyzed gene expression profiles of the two subsets using Illumina HiSeq 2000 Sequencer. We identified 1995 transcripts related to the activation of Vγ1⁺ γδ T cells, and 2158 transcripts related to the activation of Vγ4⁺ γδ T cells. We identified 24 transcripts differentially expressed between the two subsets in resting condition, and 20 after PMA/Ionomycin treatment. We found that both cell types maintained phenotypes producing IFN-γ, TNF-α, TGF-β and IL-10. However, Vγ1⁺ γδ T cells produced more Th2 type cytokines, such as IL-4 and IL-5, while Vγ4⁺ γδ T cells preferentially produced IL-17. Our study provides a comprehensive gene expression profile of mouse peripheral Vγ1⁺ and Vγ4⁺ γδ T cells that describes the inherent differences between them.</p></div

Significantly changed genes between resting and activated Vγ4⁺ γδ T cells enriched for KEGG pathways.

<p>Database for Annotation, Visualization and Integrated Discovery (DAVID), was used to analyze biological pathways associated with the differentially expressed gene transcripts. 2,158 transcripts that were identified to be related to the activation of Vγ4⁺ γδ T cells were enriched for 29 KEGG pathways (p<0.05). KEGG, Kyoto Encyclopedia of Genes and Genomes.</p><p>Significantly changed genes between resting and activated Vγ4⁺ γδ T cells enriched for KEGG pathways.</p

The distribution of gene expression.

<p>The ‘x’ axis represents Log fold-change of differentially expressed genes. The ‘y’ axis represents number of genes. Red region represents genes with expression within 4-fold change; green and blue regions represent genes with more than 4-fold change either up or down regulated, respectively. Library pairs: A, resting Vγ1⁺ vs activated Vγ1⁺ γδ T cells; B, resting Vγ4⁺ vs activated Vγ4⁺ γδ T cells.</p

Cytokines secreted by Vγ1⁺ and Vγ4⁺ γδ T cells.

<p>Both subsets of γδ T cells produce IFN-γ, TNFα, TGF-β and IL-10. Vγ1+ γδ T cells tend to produce Th2 type cytokines IL-4 and IL-5 while Vγ4⁺ γδ T cells tend to produce IL-17.</p

20 transcripts expressed differently between activated Vγ1⁺ and Vγ4⁺ γδ T cells.

<p>GN, Gene name; AN, Accession Number; γ1 RPKM, the RPKM value of gene in activated Vγ1⁺ γδ T cells; γ4 RPKM, the RPKM value of gene in activated Vγ4⁺ γδ T cells; “*”, Gene’s alternatively spliced transcript variants.</p><p>20 transcripts expressed differently between activated Vγ1⁺ and Vγ4⁺ γδ T cells.</p