Effects of field soil warming on the growth and physiology of Juglans mandshurica seedlings.

Global climate change will increase surface soil temperature, with consequences on plant seedling growth and population dynamics. In this study, we carried out a field experiment to investigate the effects of 2 ℃ soil warming on the growth and physiological characteristics of 1- and 2-year-old seedlings of a dominant tree species in broadleaved Korean pine forest, Juglans mandshurica. The results showed that soil warming significantly increased basal diameter, root length, total leaf area, leaf dry weight, root dry weight, total biomass, apparent photosynthetic electron transfer rate (ETR), PSⅡ actual photochemical efficiency (ΦPSⅡ), and apparent photosynthetic electrophotochemical quenching coefficient (qP) of 1-year-old seedlings by 18.3%, 66.7%, 94.4%, 105.9%, 95.8%, 37.8%, 89.5%, 100.0%, and 71.4%, respectively. Soil warming significantly increased basal diameter, total leaf area, leaf dry weight, total biomass, leaf superoxide dismutase activity, peroxidase activity, catalase activity and free proline content, ETR, ΦPSⅡ, and qP of 2-year-old seedlings by 12.5%, 180.5%, 97.3%, 42.5%, 23.9%, 20.4%, 14.9%, 20.7%, 66.7%, 283.3% and 284.6%, respectively. There was an interaction between seedling age and soil warming on the root-shoot ratio and the ΦPSⅡ and qP in chlorophyll fluorescence parameters, in that soil warming significantly reduced the root-shoot ratio of 2-year-old seedlings and that the increase of chlorophyll fluorescence parameters of 2-year-old seedlings (4.1-4.6 times) was much higher than that of 1-year-old seedlings (1.5-1.8 times). Soil warming of 2 ℃ was beneficial to the growth of 1- and 2-year-old J. mandshurica seedlings and increased their regeneration potential. In particular, 2-year-old J. mandshurica seedlings responded to soil warming by increasing leaf area, improving leaf photochemical efficiency, and enhancing protective enzyme activity to increase resistance.

Simulating a combined lysis-cryptic and biological nitrogen removal system treating domestic wastewater at low C/N ratios using artificial neural network.

In this study, a combined alkaline (ALK) and ultrasonication (ULS) sludge lysis-cryptic pretreatment and anoxic/oxic (AO) system (AO + ALK/ULS) was developed to enhance biological nitrogen removal (BNR) in domestic wastewater with a low carbon/nitrogen (C/N) ratio. A real-time control strategy for the AO + ALK/ULS system was designed to optimize the sludge lysate return ratio (RSLR) under variable sludge concentrations and variations in the influent C/N (⩽ 5). A multi-layered backpropagation artificial neural network (BPANN) model with network topology of 1 input layer, 3 hidden layers, and 1 output layer, using the Levenberg-Marquardt algorithm, was developed and validated. Experimental and predicted data showed significant concurrence, verified with a high regression coefficient (R2 = 0.9513) and accuracy of the BPANN. The BPANN model effectively captured the complex nonlinear relationships between the related input variables and effluent output in the combined lysis-cryptic + BNR system. The model could be used to support the real-time dynamic response and process optimization control to treat low C/N domestic wastewater.

Network of Interactions Between Gut Microbiome, Host Biomarkers, and Urine Metabolome in Carotid Atherosclerosis.

Comprehensive analyses of multi-omics data may provide insights into interactions between different biological layers concerning distinct clinical features. We integrated data on the gut microbiota, blood parameters and urine metabolites of treatment-naive individuals presenting a wide range of metabolic disease phenotypes to delineate clinically meaningful associations. Trans-omics correlation networks revealed that candidate gut microbial biomarkers and urine metabolite feature were covaried with distinct clinical phenotypes. Integration of the gut microbiome, the urine metabolome and the phenome revealed that variations in one of these three systems correlated with changes in the other two. In a specific note about clinical parameters of liver function, we identified Eubacteriumeligens, Faecalibacteriumprausnitzii and Ruminococcuslactaris to be associated with a healthy liver function, whereas Clostridium bolteae, Tyzzerellanexills, Ruminococcusgnavus, Blautiahansenii, and Atopobiumparvulum were associated with blood biomarkers for liver diseases. Variations in these microbiota features paralleled changes in specific urine metabolites. Network modeling yielded two core clusters including one large gut microbe-urine metabolite close-knit cluster and one triangular cluster composed of a gut microbe-blood-urine network, demonstrating close inter-system crosstalk especially between the gut microbiome and the urine metabolome. Distinct clinical phenotypes are manifested in both the gut microbiome and the urine metabolome, and inter-domain connectivity takes the form of high-dimensional networks. Such networks may further our understanding of complex biological systems, and may provide a basis for identifying biomarkers for diseases. Deciphering the complexity of human physiology and disease requires a holistic and trans-omics approach integrating multi-layer data sets, including the gut microbiome and profiles of biological fluids. By studying the gut microbiome on carotid atherosclerosis, we identified microbial features associated with clinical parameters, and we observed that groups of urine metabolites correlated with groups of clinical parameters. Combining the three data sets, we revealed correlations of entities across the three systems, suggesting that physiological changes are reflected in each of the omics. Our findings provided insights into the interactive network between the gut microbiome, blood clinical parameters and the urine metabolome concerning physiological variations, and showed the promise of trans-omics study for biomarker discovery.

[Infection of the mononuclear cell subpopulations in murine bone marrow with murine cytomegalovirus].

This study was aimed to explore the infection characteristics of murine mononuclear cell subpopulations in bone marrow with murine cytomegalovirus (MCMV). Subpopulations of mononuclear cells, including lin(+), lin(-), lin(-)CD117(+) and lin(-)CD117(-) cells, were infected with MCMV after being separated by MACS, and induced to differentiation by adding cytokines or inducer, then nucleic acid and proteins were detected. The results indicated that the MCMV DNA, IE transcripts and IE protein could be detected in the lin(+) cells infected with MCMV; no virus products were detected in infected lin(-) cells without adding any stimulating factors, while IE and E transcripts and proteins were detected after adding GM-CSF, rhEPO or phorbol ester in the lin(-) cells infected with MCMV. Furthermore, no IE or E gene transcripts were detected in the lin(-)CD117(+) and lin(-)CD117(-) cells, but the cell colony formation of lin(-)CD117(+) hematopoietic stem and progenitor cells was inhibited after MCMV infection and expression of CD117 antigen on cell surface of the lin(-) cells was downregulated. It is concluded that MCMV can latently infect subpopulations of mononuclear cells in the murine bone marrow. Cells which are of characteristics of primitive stem and progenitor cells are not susceptible to MCMV, but infection of these cells with MCMV can inhibit functions of cells and downregulate the expression of antigen on cells surface.