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This paper investigates C–H bond activation in methane over monometallic Pd, Pt and bimetallic Pd–Pt catalysts via a differential reactor, chemisorption system, HAADF-STEM, TPR and XPS methods.

Background Denitrification (DNF) and dissimilatory nitrate reduction to ammonium (DNRA) share the common intermediate nitrite during nitrogen anaerobic reduction metabolism. Compared with DNF, which incurs nitrogen loss through gas emission and leaching, DNRA is more economical and environmentally friendly due to the conservation of nitrogen in the form of ammonium. However, the competition mechanism between DNF and DNRA is currently unclear. Results Incubations with agricultural soil from the North China Plain (NCP) were conducted under different aeration conditions with the addition of nitrite and different doses of glucose. By measuring the activity of DNF and DNRA, we found that DNF exclusively dominated nitrate reduction when NCP soil was directly anaerobically incubated regardless of the carbon content. However, a significant increase in DNRA activity and attenuation of that of DNF was detected by 15N stable isotopic measurement when the soil samples were incubated aerobically with the addition of glucose prior to anaerobic incubation; specifically, as much as 24 ~ 56% DNF activity was attenuated, and most of that part of nitrite was reduced by DNRA activity under high carbon conditions. Quantitative assays of the nrfA gene and metagenomics data showed a significant increase in DNRA reaction-encoding genes after aerobic incubation. Furthermore, the nrfA sequence analysis showed a significant shift in the composition of DNRA members during aerobic preincubation with an increased relative abundance of specific phylotypes. Hence, environmental changes, especially oxygen and carbon availability, resulted in changes in nitrogen reduction metabolism. Conclusions Results from this study indicated that DNRA could be promoted by regulating abiotic factors, such as aeration and the carbon content, to modulate the competition of two metabolic pathways of nitrate/nitrite reduction in agricultural soil, which could be achieved by shifting the abundance of functional facultative anaerobes.

Eighteen cassane diterpenoids, including five new lactam-type (4-8), 12 new lactone-type (1-3 and 9-17), and one known compound (18), were isolated from Caesalpinia sinensis. To our knowledge, this is the first study on the seed kernels of C. sinensis, and cassane derivatives were discovered in this plant for the first time. Their structures including absolute configurations were established by extensive spectroscopic methods complemented with single-crystal X-ray diffraction analyses and ECD calculations. Compounds 4-8 were identified as a group of rare cassane diterpenoids possessing a lactam D-ring instead of a typical lactone moiety. Biological evaluation revealed that compounds 4-6 exhibited effective inhibitory effects on NO production in the LPS-induced RAW 264.7 macrophages, with IC50 values in the range 8.2-11.2 μM. Compound 4 suppressed the excessive production of NO by down-regulating the expression of inducible nitric oxide synthase enzymes (iNOS) and reducing the enzymatic activity of iNOS. Moreover, the intermolecular interaction and binding mode between compound 4 and iNOS were elaborated by conducting a molecular docking study.

Individuals belong to multiple groups across various domains of life, which in aggregate constitute a portfolio of potentially distinct levels of experienced status. We propose a two-factor model for assessing the effects of an individual’s status portfolio, based on status average (mean status level across groups) and status variance (degree to which status varies across those groups). Five studies using samples in general-life and work-specific contexts reveal the importance of both status average and status variance, the latter of which has been largely unexplored by status researchers to date. Individuals experiencing higher status variance show greater perspective taking, which in turn increases interpersonal helping. However, higher status variance also increases anxiety, decreasing intrapersonal well-being. Our results provide evidence of the additional explanatory power of accounting for status variance alongside status average, and highlight the importance of considering individuals’ aggregate experience of status across the multiple groups to which they belong.

Humans are a fundamentally social species, having evolved in groups with status hierarchies. However, research on the dimensions of individual ability has largely overlooked the domain of status. Building upon research on the individual-level benefits of accurate status perceptions, we propose that there exists an individual dispositional ability to perceive groups' informal status hierarchies, which we call

The first investigation of chemical composition in seed kernels of Caesalpinia cucullata Roxb. resulted in the separation of four new cassane diterpenes, including three furanoditerpenes (1, 2 and 4) and one tricyclic type (3). It was the first found cassane diterpenoids from this plant. Their gross structures were elucidated by means of comprehensive spectroscopic analysis (UV, IR, NMR, HRESIMS) as well as electronic circular dichroism (ECD) calculation. The compound 1 possess a rare isomerized dihydrofuran ring in structure framework. In bioassay, compounds 1-4 exhibited moderate anti-inflammatory activity by inhibiting production of NO in LPS-induced RAW 264.7 cells.

Catalysts are needed for electrochemical and photochemical reduction of CO2 into valuable chemicals. Diamond and its related materials have been regarded as a promising class of catalysts for CO2 reduction. With varied forms, structures, dopants, sp2/sp3 ratios, surface functionalities, and the combination with different co-catalysts, a great diversity of diamond-based catalysts have been fabricated for CO2 reduction. This review summarizes state-of-art progress and achievements on the electrochemical CO2 reduction using different diamond and related electrodes, along with the selection of electrolytes and applied conditions. The applications of diamond nanoparticles and diamond related films for photochemical CO2 reduction are highlighted. The advanced strategies to further improve the catalytic activity of diamond towards CO2 reduction have been discussed and outlined.

Geological factors are key elements to control shale gas enrichment and influence the accurate estimation of shale gas reserve. However, the impact of the main geological factors, such as porosity, mineralogy, and organic matter, on marine shale gas enrichment and reserve calculation has not yet been fully clarified. Herein, we measured gas adsorption, porosity, mineralogical composition, and total organic carbon content of the marine shale samples from the Jiaoshiba area of Fuling gas field in Sichuan Basin, South China, and investigate the relationships between the geological factors and the adsorbed gas content. The results show that adsorbed gas content is positively correlated with total organic carbon and porosity; the larger specific surface area of samples with more clay minerals essentially contributes to shale gas enrichment. Additionally, the sealing of faults imposes a significant impact on shale gas accumulation. The probability volume method was applied to calculate the shale gas reserve. The reserves of P90 (the most pessimistic reserve), P50 (the most likely reserve), and P10 (the most optimistic reserve) were calculated, respectively, which provides useful information to reduce the risk in shale gas development.

Denitrifying nitrous oxide (N

Denitrifying nitrous oxide (N2O) emissions in agroecosystems result from variations in microbial composition and soil properties. However, the microbial mechanisms of differential N2O emissions in agricultural soils are less understood. Microcosm experiments of two types of Chinese farmland soil were conducted with nitrate (250 mg/kg) and a combination of glucose (1000 mg/kg) and nitrate, and a case with no addition was used as the control. The results show that N2O accumulation in black soil (BF) was significantly higher than that in fluvo-aquic soil (FF) independent of carbon and nitrogen supply. The abundance of denitrifying genes was significantly higher in FF, but the ratios of genes responsible for N2O production (narG, nirS, and nirK) to the gene for N2O reduction (nosZ) did not significantly differ between the two soils. However, the soils showed obvious discrepancies in denitrifying bacterial communities. High accumulation of N2O was verified by the isolates of Rhodanobacter, which is predominant in BF due to its truncated denitrifying genes and lack of N2O reduction capacity. The dominance of complete denitrifiers such as Castellaniella in FF led to a rapid reduction in N2O and reduced N2O accumulation, as demonstrated when its corresponding isolate was inoculated into both studied soils. Therefore, the different phenotypes of N2O metabolism of the distinct denitrifiers maintained in the two soils caused their differing N2O accumulation. This knowledge could guide the regulation of the denitrifying bacterial community and the phenotypes of N2O metabolism in agricultural soils to reduce N2O emissions.

Fabricating 0D/2D heterojunctions is considered to be an efficient mean to improve the photocatalytic activity of g-C3N4, whereas their applications are usually restricted by complex preparation process. Here, the 0D/2D SnO2/g-C3N4 heterojunction photocatalyst is prepared by a simple one-step polymerization strategy, in which SnO2 nanodots in-situ grow on the surface of g-C3N4 nanosheets. It shows the outstanding photocatalytic H2 production activity relative to g-C3N4 under the visible light, which is due to the formation of 0D/2D heterojunction significantly contributing to the separation of photogenerated charge carriers. In particular, the H2 production rate over the optimal SnO2/g–C3N4–1 sample is 1389.2 μmol h−1 g−1, which is 6.06 times higher than that of g-C3N4 (230.8 μmol h−1 g−1). Meanwhile, the AQE value of H2 production over the SnO2/g–C3N4–1 sample reaches up to a maximum of 4.5% at 420 nm. This work develops a simple approach to design and fabricate g–C3N4–based 0D/2D heterojunctions for the high-efficiency H2 production from water splitting.

It is a considerable research focus to introduce a new conjugate structure into the graphitic carbon nitride (g-C3N4) molecule by polymerization to improve the photocatalytic and photoelectrochemical performance. Herein, the 1,4-cyclohexadiene (CHD) group-doped g-C3N4 (CHD-g-C3N4) nanosheet photocatalyst is prepared by a simple thermal polymerization, which dramatically improves the photocatalytic H2 production and photoelectrochemical performance. Surprisingly, the optimal H2 production rate and the photocurrent density of CHD-g-C3N4 reaches up to 522.0 μmol h−1 g−1 and 31.19 μA cm−2, which is 6.29 and 6.96 times higher than that of pure g-C3N4, respectively. The high-efficiency and stable photocatalytic H2 production and photoelectrochemical performance of CHD-g-C3N4 originate from the doping effect of conjugated groups in g-C3N4 molecule, which extends the π-conjugated system to accelerate electron mobility. This work indicates that the introduction of metalloid groups with conjugation effect is of great significance for improving photocatalytic and photoelectric performance, and provides a new idea for the non-metallic modification of g-C3N4 as a highly active catalyst.

Problem, research strategy, and findings: In this study we analyze plan integration for flood resilience in the city of Nijmegen, the site of the largest Room for the River project in The Netherlan...

Transition-metal phosphides have been demonstrated as cocatalysts with great promise for photocatalytic H2 production materials, but the insurmountable issue remains maintaining outstanding stabili...

Understanding the relationship between social vulnerability and traffic crashes is a cornerstone for promoting social justice in transportation planning and policymaking. However, few studies have examined the disparities in traffic crashes by systemically considering the influence of social vulnerability via spatial analysis approaches. This study puts forward a new approach to assess the inequity in transportation safety by spatially examining the relationships between crash risks and the social vulnerability index (SVI) established by the Centers for Disease Control and Prevention (CDC). We performed spatial autocorrelation analyses to identify the clusters of high-risk and high-vulnerable census tracts in Texas. Meanwhile, we innovatively applied the Multiscale Geographically Weighted Regression model (MGWR) to assess the impacts of CDC SVI on crash risks spatially and statistically. The results demonstrate that the crash rate and the social vulnerability are significantly correlated in the highly urbanized regions as well as the southern border along the Rio Grande in Texas. The MGWR results indicate the minority status of census tracts is strongly correlated with overall crashes in north-central and northeastern Texas, and the socioeconomic status is tightly correlated with fatal crashes across Texas. The outcomes from this study have significant implications for transportation planning and policymaking.

Fructose, especially industrial fructose (sucrose and high fructose corn syrup) is commonly used in all kinds of beverages and processed foods. Liver is the primary organ for fructose metabolism, recent studies suggest that excessive fructose intake is a driving force in non-alcoholic fatty liver disease (NAFLD). Dietary fructose metabolism begins at the intestine, along with its metabolites, may influence gut barrier and microbiota community, and contribute to increased nutrient absorption and lipogenic substrates overflow to the liver. Overwhelming fructose and the gut microbiota-derived fructose metabolites (e.g., acetate, butyric acid, butyrate and propionate) trigger thede novolipogenesis in the liver, and result in lipid accumulation and hepatic steatosis. Fructose also reprograms the metabolic phenotype of liver cells (hepatocytes, macrophages, NK cells, etc.), and induces the occurrence of inflammation in the liver. Besides, there is endogenous fructose production that expands the fructose pool. Considering the close association of fructose metabolism and NAFLD, the drug development that focuses on blocking the absorption and metabolism of fructose might be promising strategies for NAFLD. Here we provide a systematic discussion of the underlying mechanisms of dietary fructose in contributing to the development and progression of NAFLD, and suggest the possible targets to prevent the pathogenetic process.

Training image (TI) is important for multipoint statistics simulation method (MPS), since it captures the spatial geological pattern of target reservoir to be modeled. Generally, one optimal TI is selected before applying MPS by evaluating the similarities between many TIs and the well interpretations of target reservoir. In this paper, we propose a new training image optimization approach based on the convolutional neural network (CNN). First, candidate TIs were randomly sampled several times to obtain the sample dataset. Then, the CNN was used to conduct transfer learning for all samples, and finally, the optimal TI of the conditioning well data is selected through the trained CNN model. By taking advantage of the strong learning ability of CNN in image feature recognition, the proposed method can automatically identify differences in spatial features between the conditioning well data and the samples of the training image. Hence, it effectively resolves the difficulty of spatial matching between discrete datapoints and grid structures. We demonstrated the applicability of our model via 2D and 3D training image selection examples. The proposed methods effectively selected the appropriate TI, and then the pretreatment techniques for improving the accuracy of continuous TI selection were achieved. Moreover, the proposed method was successfully applied to training image selection of a discrete fracture network model. Finally, sensitivity analysis was carried out to show that sufficient conditioning data volume can reduce the uncertainty of the optimization results. By comparing with the improved MDevD method, the advantages of the new method are verified in terms of efficiency and reliability.

Urban plans often ignore the uneven impacts of hazards on socially vulnerable populations. We evaluate the degree to which equity policies in local networks of plans support risk reduction for socially vulnerable populations, and examine the relationship between equity policies scores and the level of social vulnerability in six cities exposed to floods and projected sea level rise. We find high variability in equity policy support for risk reduction, and that equity policies in plans actually actively increase risk of loss in neighborhoods with high levels of social vulnerability, which in turn could disrupt further development.

Brain decoding is a hot spot in cognitive science, which focuses on reconstructing perceptual images from brain activities. Analyzing the correlations of collected data from human brain activities and representing activity patterns are two problems in brain decoding based on functional magnetic resonance imaging (fMRI) signals. However, existing correlation analysis methods mainly focus on the strength information of voxel, which reveals functional connectivity in the cerebral cortex. They tend to neglect the structural information that implies the intracortical or intrinsic connections; that is, structural connectivity. Hence, the effective connectivity inferred by these methods is relatively unilateral. Therefore, we proposed a correlation network (CorrNet) framework that could be flexibly combined with diverse pattern representation models. In the CorrNet framework, the topological correlation was introduced to reveal structural information. Rich correlations were obtained, which contributed to specifying the underlying effective connectivity. We also combined the CorrNet framework with a linear support vector machine (SVM) and a dynamic evolving spike neuron network (SNN) for pattern representation separately, thus providing a novel method for decoding cognitive activity patterns. Experimental results verified the reliability and robustness of our CorrNet framework and demonstrated that the new method achieved significant improvement in brain decoding over comparable methods., 10 pages, 8 figures, IEEE Transactions on Cognitive and Developmental Systems(TCDS)

International audience; Electrochemical capacitors (ECs), including electrical-double-layer capacitors and pseudocapacitors, feature high power densities but low energy densities. To improve the energy densities of ECs, redox electrolyte-enhanced ECs (R-ECs) or supercapbatteries are designed through employing confined soluble redox electrolytes and porous electrodes. In R-ECs the energy storage is based on diffusion-controlled faradaic processes of confined redox electrolytes at the surface of a porous electrode, which thus take the merits of high power densities of ECs and high energy densities of batteries. In the past few years, there has been great progress in the development of this energy storage technology, particularly in the design and synthesis of novel redox electrolytes and porous electrodes, as well as the configurations of new devices. Herein, a full-screen picture of the fundamentals and the state-of-art progress of R-ECs are given together with a discussion and outlines about the challenges and future perspectives of R-ECs. The strategies to improve the performance of R-ECs are highlighted from the aspects of their capacitances and capacitance retention, power densities, and energy densities. The insight into the philosophies behind these strategies will be favorable to promote the R-EC technology toward practical applications of supercapacitors in different fields.

It is difficult for controlling the micro/nanostructures of composite unit on the surface of two-dimensional (2D) materials to fabricate the new high-efficiency photocatalysts. The mes-Fe3O4/g-C3N4 composite is fabricated by the modification of mesoporous Fe3O4 (mes-Fe3O4) nanospheres on the surface of 2D graphitic carbon nitride (g-C3N4) nanosheets. The mes-Fe3O4 nanospheres as the natural nanoreactors enlarge the specific surface area to increase reaction active sites, as well as provide the confined space to accelerate degradation reaction. Meanwhile, the physical and photoelectrochemical properties of mes-Fe3O4/g-C3N4 composite are distinctly improved owing to the electron collection effect of mes-Fe3O4 nanoreactors. The mes-Fe3O4/g-C3N4 composites exhibit the improved degradation performance for removing tetracycline hydrochloride (TC-HCl) relative to single g-C3N4. Moreover, the possible intermediate product and photocatalytic reaction mechanism are revealed in depth. This work gives a new guidance for the controlled fabrication of mesoporous nanoreactors on the surface of 2D materials.

Antibiotic drugs have become the important organic pollutants in the water resources, the high-efficient removal of which is one of the foremost works for protecting water environment. The new Z-scheme mes-Sn3O4/g-C3N4 heterostructure was obtained in present work, compared with single g-C3N4, which exhibits more superior photocatalytic performance for degrading and mineralizing tetracycline hydrochloride in water. The investigations of microstructure, physical properities and photoelectrochemical behaviors indicate that the modification effect mesoporous Sn3O4 on the surface of g-C3N4 nanosheets fabricates close heterostructure, which enlarges distinctly the specific surface area and improves dramatically the separation efficiency of charge carriers. Furthermore, the possible photocatalytic reaction mechanisms including transfer behaviors of charge carriers, generation of reactive species, degradation intermediate products of TC-HCl are also revealed in depth.

The first investigation of chemical composition in seed kernels of

Background and Purpose Liver failure is often associated with psychiatric alterations, partly resulting from the increased dopamine levels in brain. We aimed to investigate relationship between increased dopamine levels and mental abnormalities using bile duct ligation (BDL) rats and document mechanism that liver failure increased dopamine levels in SH-SY5Y cells. Experimental Approach Psychiatric alterations were operated following 14-day BDL. Dopamine and its metabolite levels in cortex, expressions of enzymes and transporters related to dopamine metabolism and transport in cortex and hippocampus were measured. SH-SY5Y cells were used to investigate whether NH4Cl, bile acids and bilirubin affected expression of tyrosine hydroxylase (TH) or not. TH expression in SH-SY5Y cells co-incubated with bilirubin and signal pathway inhibitors was measured. Key Results Open-field test results showed a remarkable increase in exploratory behavior following BDL. BDL increased dopamine levels and expression of TH protein in cortex. MAO-A and Mb-COMT slightly but significantly decreased. Data from SH-SY5Y cells showed that increased bilirubin levels was a factor in inducing TH expression. Both inhibitor of NF-κB pathway BAY117082 and silencing p65 remarkably reversed bilirubin-induced upregulation of TH protein. NF-κB activator TNF-α increased expression of TH protein. Roles of bilirubin in TH expression and increases in dopamine levels were documented using hyperbilirubinemia rats. Significant increases in dopamine levels, expressions of TH, p65 and p-p65 protein were observed in hyperbilirubinemia rats. Conclusion and Implications BDL significantly increased dopamine levels in rat cortex partly due to bilirubin-mediated TH induction. Increased bilirubin induced TH expression via activating NF-κB signaling pathway.

The trend of land transfer in China is parallel to the risk of food security. The "factors-countermeasures" method used in the general research is not effective for the symbionism within land transfer. Based on the Logit model, this paper empirically analyzes the impact of non-grain planting on land transfer. Research shows that: (1) In the regression analysis, the inflows and outflows, non-staple planting will be negative effects of land circulation (2) Plants grown planting intensive features suppresses the will of the individual farmers out of land, and grain planting scale features will enhance farmers into the land (3) Age, quality of labor force and total income of family also have significant influence on land transfer

The low energy density of traditional supercapacitors has strongly restricted their applications. The utilization of novel capacitor electrodes to enhance the energy densities of supercapacitors is thus of great significance. Herein, a binder-free Ni12P5/Ni/TiC nanocomposite film is synthesized and further employed as the capacitor electrode. This nanocomposite film is grown by means of a chemical vapor deposition process, where Ni5TiO7 nanowires and a TiO2 layer are in situ converted into hierarchical interconnected three-dimensional (3D) Ni/Ni12P5 nanoparticles and a porous TiC matrix, respectively. Such a nanocomposite film exhibits an extremely high specific surface area and excellent conductivity, leading to its high capacitive performance. Remarkably, the multiple redox states of Ni species, namely two pairs of redox waves are observed in neutral aqueous solutions. At a current density of 10 mA cm-2, its specific capacitance in 1 M Na2SO4 aqueous solution is as high as 160.0 mF cm-2. The maximal energy density of a supercapacitor fabricated with this nanocomposite capacitor electrode is 42.6 W h kg-1 at a power density of 1550 W kg-1. Such an ultra-high energy density is even comparable with that of Li-batteries. The proposed supercapacitor thus has high potential for industrial applications.

With the deepening of China's electric power system reform and the development of energy utilization technology, diversified electricity sellers have stepped onto the stage of the power market. How to formulate efficient purchasing and selling strategies and effectively avoid their own risks in the fierce market competition is an urgent problem for each selling entity at present. This paper firstly analyzes the related background of China's electricity sales market liberalization in chronological order and makes a prospect for the future electricity sales market. Then, it illustrates the research status of multi-dimensional competition in power sales companies from two aspects: risk aversion and power purchasing and selling strategies. Finally, based on the SWOT analysis method, the SWOT model is established with the independent power selling company as an example to analyze the opportunities and challenges faced by independent power selling companies comprehensively in market competition under the open power selling market environment.

Multi-point geostatistics has been widely used, but it involves many parameters when applying the method. These modeling parameters are usually sensitive to the quality of the model, such as the template size of the simpat algorithm, the number of nodes in the neighborhood searched of the Snesim algorithm, and the multigrid in common use. What kind of evaluation criteria for selecting the value and ranges of the parameters has become an urgent problem to be solved. Some methods to optimize these modeling parameters have been proposed. These methods either statistics the pattern feature distribution of training image, or use some certain spatial structure as the index to quantitatively evaluate the quality of training image reconstruction of stochastic realizations, or use cross-validation to compare the sample matching degree between the realization and the training image. However, these methods have their limitations in optimizing modeling parameters. In this paper, two methods to optimize the modeling parameters of multi-point geostatistics were proposed based on gray level co-occurrence matrix and convolutional neural network. Both methods can optimize the appropriate parameters by analyzing the influence of the parameters on modeling quality. Taking the frequently used parameters of Snesim and simpat as an example, the parameter optimization results of the GLCM-based method and the Deep Learning-based method are very close, which indicates that both methods can effectively extract and distinguish the spatial features of the model. In addition, the relationship between modeling parameters and evaluation indexes accords with the visual features of stochastic models. Therefore, the method proposed in this paper will be helpful to solve the difficulty of parameter setting in multi-point geostatistical modeling.