Promotion of avian endothelial cell differentiation by GATA transcription factors

AbstractIn the avian embryo, endothelial cells originate from several sources, including the lateral plate and somite mesoderm. In this study, we show that Gata transcription factors are expressed in the lateral plate and in vasculogenic regions of the avian somite and are able to promote a vascular endothelial fate when ectopically expressed in somite precursors. A fusion of GATA4 to the transcriptional activator VP16 promoted endothelium formation, indicating that GATA transcription factors promote vasculogenesis via activation of downstream targets, while a fusion of GATA4 to the transcriptional repressor engrailed repressed expression of Vascular Endothelial Growth Factor Receptor 2, a marker of endothelial precursors. These findings indicate a role for GATA transcription factors in the differentiation of the endothelium

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering

Due to the complexity of the various waveforms of microseismic data, there are high requirements on the automatic multi-classification of such data; an accurate classification is conducive for further signal processing and stability analysis of surrounding rock masses. In this study, a microseismic multi-classification (MMC) model is proposed based on the short time Fourier transform (STFT) technology and convolutional neural network (CNN). The real and imaginary parts of the coefficients of microseismic data are inputted to the proposed model to generate three classes of targets. Compared with existing methods, the MMC has an optimal performance in multi-classification of microseismic data in terms of Precision, Recall, and F1-score, even when the waveform of a microseismic signal is similar to that of some special noise. Moreover, semisynthetic data constructed by clean microseismic data and noise are used to prove the low sensitivity of the MMC to noise. Microseismic data recorded under different geological conditions are also tested to prove the generality of the model, and a microseismic signal with Mw ≥ 0.2 can be detected with a high accuracy. The proposed method has great potential to be extended to the study of exploration seismology and earthquakes

Preparation of Cex-Mn0.8Fe0.2O2 Catalysts and Its Anti-Sulfur Denitration Performance

In order to meet the industrial denitrification demands, inexpensive ferrous metals Mn and Fe have been chosen as the raw materials for the catalysts of CO-SCR, and the anti-sulfur denitrification performance of ferromanganese catalysts can be greatly enhanced by Ce doping. In this study, Cex-Mn0.8Fe0.2O2 catalysts were prepared by co-precipitation, and the effects of Ce addition on the structure and morphology of prepared catalysts and their anti-sulfur denitration performance were investigated with X-ray diffraction (XRD), scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). The results showed that the Cex-Mn0.8Fe0.2O2 catalysts consisted of nanoparticles sized 20–100 nm. Specifically, the Ce0.2-Mn0.8Fe0.2O2 catalyst had more active sites and the best anti-sulfur denitration performance, with a denitration rate of 90.36% at 350 °C, while the denitrification performance of the Mn0.8Fe0.2O2 catalyst was only 85%. Furthermore, the denitrification rate of the catalyst was maintained above 80% when the CO:NO:SO2 ratio was 3:1:1 for 4 h at 350 °C

Preparation of Ce_x-Mn_0.8Fe_0.2O₂ Catalysts and Its Anti-Sulfur Denitration Performance

In order to meet the industrial denitrification demands, inexpensive ferrous metals Mn and Fe have been chosen as the raw materials for the catalysts of CO-SCR, and the anti-sulfur denitrification performance of ferromanganese catalysts can be greatly enhanced by Ce doping. In this study, Cex-Mn0.8Fe0.2O2 catalysts were prepared by co-precipitation, and the effects of Ce addition on the structure and morphology of prepared catalysts and their anti-sulfur denitration performance were investigated with X-ray diffraction (XRD), scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). The results showed that the Cex-Mn0.8Fe0.2O2 catalysts consisted of nanoparticles sized 20–100 nm. Specifically, the Ce0.2-Mn0.8Fe0.2O2 catalyst had more active sites and the best anti-sulfur denitration performance, with a denitration rate of 90.36% at 350 °C, while the denitrification performance of the Mn0.8Fe0.2O2 catalyst was only 85%. Furthermore, the denitrification rate of the catalyst was maintained above 80% when the CO:NO:SO2 ratio was 3:1:1 for 4 h at 350 °C

Room-temperature extraction of direct coal liquefaction residue by liquefied dimethyl ether

Direct coal liquefaction residue (DCLR) is the main byproduct during the direct coal liquefaction process. The efficient recovery of organic components from DCLR at low temperatures is beneficial for improving the economy and reducing energy consumption and environmental pollution. Here, DCLR was extracted using liquefied dimethyl ether (DME), acetone, and hexane as the solvents. Compared with the other two solvent Soxhlet extraction, the DME extraction process was performed at room temperature with the shortest extraction time, the lowest energy consumption, and the highest extraction yield (16.2%). Owing to the high carbon contents, low sulfur and oxygen contents, and low ash contents (< 0.1%), the extracts obtained using liquefied DME and acetone naturally became the feedstock of carbon materials. Based on the results of the gas chromatography-mass spectrometry analysis, the extracts obtained using the three different solvents had similar compositions in light compounds and were abundant in polycyclic aromatic hydrocarbons with two-, three-, four-, five-, and six-membered benzene rings, indicating that all three DCLR extracts are potential raw materials for preparing high value-added carbon materials. Furthermore, the molecular composition analysis revealed that the room-temperature extraction using liquefied DME was as good as high-temperature Soxhlet extraction using acetone, considering the similarity of their compositions in high molecular weight species and the considerably higher efficiency than that of high-temperature Soxhlet extraction using hexane. Due to the low energy consumption, short extraction time, high extraction yield, and high performance of the extract, liquefied DME is an efficient and economic solvent for extracting DCLR

Linkage between tree species richness and soil microbial diversity improves phosphorus bioavailability

Increased availability of soil phosphorus (P) has recently been recognised as an underlying driving factor for the positive relationship between plant diversity and ecosystem function. The effects of plant diversity on the bioavailable forms of P involved in biologically mediated rhizospheric processes and how the link between plant and soil microbial diversity facilitates soil P bioavailability, however, remain poorly understood. - This study quantified four forms of bioavailable P (CaCl2-P, citric-P, enzyme-P and HCl-P) in mature subtropical forests using a novel biologically based approach, which emulates how rhizospheric processes influence the release and supply of available P. Soil microbial diversity was measured by Illumina high-throughput sequencing. - Our results suggest that tree species richness significantly affects soil microbial diversity (p < 0.05), increases litter decomposition, fine-root biomass and length and soil organic carbon and thus increases the four forms of bioavailable P. A structural equation model that links plants, soil microbes and P forms indicated that soil bacterial and fungal diversity play dominant roles in mediating the effects of tree species richness on soil P bioavailability. - An increase in the biodiversity of plants, soil bacteria and fungi could maintain soil P bioavailability and alleviate soil P limitations. Our results imply that biodiversity strengthens plant and soil feedback and increases P recycling

Tree species identity surpasses richness in affecting soil microbial richness and community composition in subtropical forests

Plant interactions and feedbacks with soil microorganisms play an important role in sustaining the functions and stability of terrestrial ecosystems, yet the effects of tree species diversity on soil microbial community in forest ecosystems are still not well understood. Here, we examined the effects of tree species richness (1-12 species) and the presence of certain influential tree species (sampling effect) on soil bacterial and fungal communities in Chinese subtropical forests, using high-throughput Illumine sequencing for microbial identification. We observed that beta rather than alpha diversities of tree species and soil microorganisms were strong coupled. Multivariate regression and redundancy analyses revealed that the effects of tree species identity dominated over tree species richness on the diversity and composition of bacterial and fungal communities in both organic and top mineral soil horizons. Soil pH, nutrients and topography were always identified as significant predictors in the best multivariate models. Tree species have stronger effect on fungi than bacteria in organic soil, and on ectomycorrhizal fungi than saprotrophic fungi in mineral topsoil. Concluding, tree species identity, along with abiotic soil and topographical conditions, were more important factors determining the soil microbial communities in subtropical forests than tree diversity per se