Influence of Financialization of Heavily Polluting Enterprises on Technological Innovation under the Background of Environmental Pollution Control

In the wake of the acceleration of China’s industrialization and rapid economic growth, environmental pollution has also attracted great attention. The technological innovation of heavily polluting enterprises is conducive to reducing pollution emissions and promoting environmental health. The financial investment tendency and behavior of real enterprises have a significant impact on the technological innovation decision-making of enterprises. A panel model is used in this paper in order to empirically test the impact of financialization of Chinese heavily polluting enterprises on technological innovation based on the data of Listed Companies in Chinese heavily polluting industries from 2008 to 2019. The + results show that the financialization of heavily polluting enterprises has a significant crowding out effect on technological innovation. After introducing arbitrage motivation as the regulating variable, further research finds that arbitrage motivation weakens the inhibitory effect of enterprise financialization on technological innovation, that is, the stronger the arbitrage motivation, the smaller the negative effect of financialization on enterprise technological innovation, which weakens this crowding out effect. Finally, the listed enterprises in heavily polluting industries are divided into state-owned enterprises and non-state-owned enterprises according to their corporate attributes. Compared with state-owned enterprises, the financialization of non-state-owned enterprises has a greater squeeze out of technological innovation; and arbitrage motivation has a more significant regulatory effect on the impact of enterprise financialization on technological innovation

In situ generated 3D hierarchical Co3O4@MnO2 core-shell hybrid materials: self-assembled fabrication, morphological control and energy applications

A simple in situ self-assembly strategy for a novel series of highly ordered 3D hierarchical Co3O4@MnO2 core-shell hybrid materials with peculiar morphologies, uniform size and high quality has been successfully developed. The mechanisms of the morphology control, reaction process, product generation, calcining process, as well as the morphology evolution of Co3O4, the intermediates of Co3O4@C and Co3O4@MnO2 hybrid materials, have been investigated and clarified in detail. The core-shell Co3O4@MnO2 hybrid architectures have the advantages of morphological features, synergistic effects between core and shell, alternative products of Co3O4@C@MnO2 or Co3O4@MnO2, and facilitate electrolyte reactions. The 3D hierarchical Co3O4@MnO2 core-shell hybrid materials are used, for the first time, for two typical Co-based energy applications in photoelectric conversion devices of dye-sensitized solar cells (DSSCs) and the decomposition of an important solid rocket propellant, ammonium perchlorate (AP). With the 3D hierarchical Co3O4 core and ultrathin MnO2 shell, the developed hybrid materials exhibit superior performances and remarkable catalytic properties. As the alternative counter electrode of DSSCs, the developed Co3O4@MnO2 core-shell hybrid system exhibited an impressive performance with the conversion efficiency of 7.08%, which was improved by 26.4% and 13.3% as compared with the Co3O4 and Co3O4@C counterparts, respectively. As the catalyzer of AP decomposition, the Co3O4 material obviously decreased the decomposition temperatures by about 118-143 degrees C and increased the exothermic heat to 933-1228 J g(-1). For the Co3O4@C counterpart, the decomposition temperatures were decreased by 120-131 degrees C with the increased exothermic heat of 1254-1306 J g(-1). The addition of Co3O4@MnO2 core-shell hybrid materials decreased the decomposition temperatures by about 107-112 degrees C and remarkably increased the exothermic heat to 1311-1452 J g(-1). To the best of our knowledge, this is the first 3D hierarchical Co3O4@MnO2 core-shell hybrid material series developed in situ and used for the energy applications of DSSCs and AP decomposition. These results provide a simple and effective strategy for designing new types of 3D hierarchical hybrid materials towards high catalytic activity in energy applications

Discharge and Temperature Controls of Dissolved Organic Matter (DOM) in a Forested Coastal Plain Stream

Streams in the southeastern United States Coastal Plains serve as an essential source of energy and nutrients for important estuarine ecosystems, and dissolved organic matter (DOM) exported from these streams can have profound impacts on the biogeochemical and ecological functions of fluvial networks. Here, we examined hydrological and temperature controls of DOM during low-flow periods from a forested stream located within the Coastal Plain physiographic region of Alabama, USA. We analyzed DOM via combining dissolved organic carbon (DOC) analysis, fluorescence excitation–emission matrix combined with parallel factor analysis (EEM-PARAFAC), and microbial degradation experiments. Four fluorescence components were identified: terrestrial humic-like DOM, microbial humic-like DOM, tyrosine-like DOM, and tryptophan-like DOM. Humic-like DOM accounted for ~70% of total fluorescence, and biodegradation experiments showed that it was less bioreactive than protein-like DOM that accounted for ~30% of total fluorescence. This observation indicates fluorescent DOM (FDOM) was controlled primarily by soil inputs and not substantially influenced by instream production and processing, suggesting that the bulk of FDOM in these streams is transported to downstream environments with limited in situ modification. Linear regression and redundancy analysis models identified that the seasonal variations in DOM were dictated primarily by hydrology and temperature. Overall, high discharge and shallow flow paths led to the enrichment of less-degraded DOM with higher percentages of microbial humic-like and tyrosine-like compounds, whereas high temperatures favored the accumulation of high-aromaticity, high-molecular-weight, terrestrial, humic-like compounds in stream water. The flux of DOC and four fluorescence components was driven primarily by water discharge. Thus, the instantaneous exports of both refractory humic-like DOM and reactive protein-like DOM were higher in wetter seasons (winter and spring). As high temperatures and severe precipitation are projected to become more prominent in the southeastern U.S. due to climate change, our findings have important implications for future changes in the amount, source, and composition of DOM in Coastal Plain streams and the associated impacts on downstream carbon and nutrient supplies and water quality

Human Activity Coupled With Climate Change Strengthens the Role of Lakes as an Active Pipe of Dissolved Organic Matter

Abstract Dissolved organic matter (DOM) composition in lakes is controlled by multiple environmental drivers, but the relative importance of various drivers remains poorly understood at the continental scale. Here, we established a model resolving possible influencing pathways of climate, land cover, societal development, and water retention time of lakes on the quantity and quality of chromophoric DOM (CDOM) from 182 lakes spanning across strong climatic and economic gradients in China. Our results indicate that land cover and societal development both exhibit positive direct effects on lake CDOM quantity, highlighting the significant role of well‐vegetated soils and anthropogenic activities as sources of lake DOM on a continental scale. Climate has two strong but opposite effects—a warming and wet climate facilitates soil OM production and export, while it also enhances CDOM in‐lake transformation. Three proxies are proposed as indicators of the magnitude of biogeochemical drivers influencing lake DOM across different ecoclimatic zones, including fluorescent DOM/DOC indicating economic activity, percentages of a degraded fluorescent DOM component indicating solar irradiation, and percent tyrosine‐like DOM reflecting DOM processing time within the watershed and lake. Collectively, our findings indicate that the effects of climate change and rapid societal development will result in increased loadings of terrestrial and anthropogenic DOM into lakes and drive higher rates of within‐lake processing of DOM. Consequently, lakes will play a more important role as an “active pipe” in mediating the flux and transformation of organic carbon, intensifying the coupling between terrestrial and aquatic carbon cycles on a continental scale

Identification of Modules Related to Programmed Cell Death in CHD Based on EHEN

The formation and death of macrophages and foam cells are one of the major factors that cause coronary heart disease (CHD). In our study, based on the Edinburgh Human Metabolic Network (EHMN) metabolic network, we built an enzyme network which was constructed by enzymes (nodes) and reactions (edges) called the Edinburgh Human Enzyme Network (EHEN). By integrating the subcellular location information for the reactions and refining the protein-reaction relationships based on the location information, we proposed a computational approach to select modules related to programmed cell death. The identified module was in the EHEN-mitochondria (EHEN-M) and was confirmed to be related to programmed cell death, CHD pathogenesis, and lipid metabolism in the literature. We expected this method could analyze CHD better and more comprehensively from the point of programmed cell death in subnetworks

Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer

<div><p>Gene expression profiles have drawn broad attention in deciphering the pathogenesis of human cancers. Cancer-related gene modules could be identified in co-expression networks and be applied to facilitate cancer research and clinical diagnosis. In this paper, a new method was proposed to identify lung cancer-risk modules and evaluate the module-based disease risks of samples. The results showed that thirty one cancer-risk modules were closely related to the lung cancer genes at the functional level and interactional level, indicating that these modules and genes might synergistically lead to the occurrence of lung cancer. Our method was proved to have good robustness by evaluating the disease risk of samples in eight cancer expression profiles (four for lung cancer and four for other cancers), and had better performance than the WGCNA method. This method could provide assistance to the diagnosis and treatment of cancers and a new clue for explaining cancer mechanisms.</p></div

Lung cancer-risk modules.

<p>Risk is modules category, ID indicate the identifier of cancer-risk modules, size is the module scale, namely the number of genes in the module, genes is the genes in the modules and the genes which were marked * were DE-genes, M_risk is the cancer risk of modules, p-value is significance p value of random randomized test.</p

The robustness of our method and comparison with the WGCNA method.

<p><b>a</b>) X-axis is samples. Y-axis is the lung cancer risk score of individual samples using our method, and it is ranked from the smallest to the largest. Blue represents GSE10072; green represents GSE21933; red represents GSE27262; and brown represents GSE4079. Full lines represent lung cancer samples; and dashed lines represent normal samples. The different experiment data sets have different numbers of the normal samples and the disease samples. In order to show the disease risk of every sample in four expression profiles intuitively, all samples of each expression profiles are distributed uniformly throughout x-axis. <b>b</b>) The figure is plotted the same way as a). The lung cancer risk of each sample is evaluated by the WGCNA method. <b>c</b>) Receiver operator characteristic curve using our method for the four lung cancer expression profiles (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092395#pone-0092395-g007" target="_blank">Figure 7a</a>). The areas under curve provided at lower right of each diagram. <b>d</b>) Receiver operator characteristic curve using the WGCNA method for the four lung cancer expression profiles (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092395#pone-0092395-g007" target="_blank">Figure 7b</a>).</p