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Dry reforming of methane (DRM) is a highly endothermic process, with its development hindered by the harsh thermocatalytic conditions required. We propose an innovative DRM approach utilizing a 16 W pulsed laser in combination with a cost-effective Mo2C catalyst, enabling DRM under milder conditions. The pulsed laser serves a dual function by inducing localized high temperatures and generating *CH plasma on the Mo2C surface. This activates CH4 and CO2, significantly accelerating the DRM reaction. Notably, the laser directly generates *CH plasma from CH4 through thermionic emission and cascade ionization, bypassing the traditional step-by-step dehydrogenation process and eliminating the rate-limiting step of methane cracking. This method maintains a carbon-oxygen balanced environment, thus preventing the deactivation of the Mo2C catalyst due to CO2 oxidation. The laser-catalytic DRM achieves high yields of H2 (14300.8 mmol h−1 g−1) and CO (14949.9 mmol h−1 g−1) with satisfactory energy efficiency (0.98 mmol kJ−1), providing a promising alternative for high-energy-consuming catalytic systems. Dry reforming of methane (DRM) is a highly endothermic process, often limited by the severe thermocatalytic conditions it demands. Here the authors introduce a novel DRM method that employs a 16 W pulsed laser along with a cost-effective Mo2C catalyst, allowing DRM to proceed under milder conditions. [ABSTRACT FROM AUTHOR]

Purpose: Coffee intake and apolipoprotein B levels have been linked to gastric, colorectal, and esophageal cancers in numerous recent studies. However, whether these associations are all causal remains unestablished. This study aimed to assess the potential causal associations of apolipoprotein B and coffee intake with the risk of gastric, colorectal, and esophageal cancers using Mendelian randomization analysis. Methods: In this study, we utilized a two-sample Mendelian randomization analysis to access the causal effects of coffee intake and apolipoprotein B on gastric, colorectal, and esophageal cancers. The summary statistics of coffee intake (n = 428,860) and apolipoprotein B (n = 439,214) were obtained from the UK Biobank. In addition, the summary statistics of gastric cancer, colorectal cancer, and esophageal cancer were obtained from the FinnGen biobank (n = 218,792). Inverse variance weighted, MR–Egger, weighted median, and weighted mode were applied to examine the causal relationship between coffee intake, apolipoprotein B and gastric, colorectal, and esophageal cancers. MR–Egger intercept test, Cochran's Q test, and leave-one-out analysis were performed to evaluate possible heterogeneity and pleiotropy. Steiger filtering and bidirectional mendelian randomization analysis were performed to evaluate the possible reverse causality. Results: The result of the inverse variance weighted method indicated that apolipoprotein B levels were significantly associated with a higher risk of gastric cancer (OR = 1.392, 95% CI 1.027–1.889, P = 0.0333) and colorectal cancer (OR = 1.188, 95% CI 1.001–1.411, P = 0.0491). Furthermore, multivariable Mendelian randomization analysis also revealed a positive association between apolipoprotein B levels and colorectal cancer risk, but the effect of apolipoprotein B on gastric cancer risk disappeared after adjustment of coffee intake, body mass index or lipid-related traits. However, we did not discover any conclusive evidence linking coffee intake to gastric, colorectal, or esophageal cancers. Conclusions: This study suggested a causal association between genetically increased apolipoprotein B levels and higher risk of colorectal cancer. No causal relationship was observed between coffee intake and gastric, colorectal, or esophageal cancers. [ABSTRACT FROM AUTHOR]

Disputes over the tectonic setting of the volcanic rocks of the Carboniferous Dahalajunshan Formation in the Western Tianshan Mountains mainly focus on "island arcs" or "continental rifts." In recent years, analyzing geochemical data based on machine learning method and inferring the tectonic background of basalt is one of the important development directions in the application of geological big data technology. In this paper, 30 elements are selected in a targeted manner, and a total of 5683 basalt samples are obtained from two types of tectonic backgrounds, including island arcs and rifts, of which 4595 samples of island arcs and 1088 samples of rifts are used as samples. The random forest algorithm is used for model training and subsequent prediction of the tectonic background of the Dahalajunshan Formation. The prediction results show that most of the Dahalajunshan Formation volcanic rocks were mainly formed in the island arc environment, and these rocks appeared during the closure of the oceanic basin in the Late Paleozoic. However, a few samples have a high probability of rift valley, which may indicate that they are in the transition from subduction to collision. Through further analysis of the predicted results, it can be inferred that the Dahalajunshan Formation was formed in an active continental margin environment, and was appeared in the final stage of plate subduction-collision from northwest to southeast. The advantage of the new method used in this paper is that it can predict the tectonic background more accurately, and open up ideas for further research on the origin of magmatic rocks, and it can be popularized in the field of geology, and it can promote the further development of geological big data experimental technology. [ABSTRACT FROM AUTHOR]

Background: Growing studies have indicated an association between dietary factors and gastroesophageal reflux disease (GERD). However, whether these associations refer to a causal relationship and the potential mechanism by which dietary factors affect GERD is still unclear.A two-step mendelian randomization analysis was performed to obtain causal estimates of dietary factors, blood lipids on GERD. Independent genetic variants associated with 13 kinds of dietary factors and 5 kinds of blood lipids at the genome-wide significance level were selected as instrumental variables. The summary statistics for GERD were obtained from European Bioinformatics Institute, including 129,080 cases and 473,524 controls. Inverse variance weighted was utilized as the main statistical method. MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis were performed to evaluate possible heterogeneity and pleiotropy. And the potential reverse causality was assessed using Steiger filtering.The results of the inverse variance weighted method indicated that genetically predicted total pork intake (OR = 2.60, 95% CI: 1.21–5.58, p = 0.0143), total bread intake (OR = 0.68, 95% CI: 0.46–0.99, p = 0.0497), total cereal intake (OR = 0.42, 95% CI: 0.31–0.56, p = 2.98E-06), and total cheese intake (OR = 0.41, 95% CI: 0.27–0.61, p = 1.06E-05) were associated with the risk of GERD. Multivariable Mendelian randomization analysis also revealed a negative association between total cereal intake, total cheese intake and the risk of GERD, but the effect of total pork intake and total bread intake on GERD disappeared after adjustment of smoking, alcohol consumption, use of calcium channel blockers, BMI, physical activity levels, and biological sex (age adjusted). Furthermore, the concentration of low-density lipoprotein cholesterol (LDL-C) is negatively correlated with total cheese intake, which mediates the impact of total cheese intake on GERD. The proportion mediated by LDL-C is 2.27% (95%CI: 1.57%, 4.09%).This study provides evidence that an increase in total cereal intake and total cheese intake will decrease the risk of GERD. Additionally, LDL-C mediates the causal effect of total cheese intake on GERD. These results provide new insights into the role of dietary factors and blood lipids in GERD, which is beneficial for disease prevention.Methods: Growing studies have indicated an association between dietary factors and gastroesophageal reflux disease (GERD). However, whether these associations refer to a causal relationship and the potential mechanism by which dietary factors affect GERD is still unclear.A two-step mendelian randomization analysis was performed to obtain causal estimates of dietary factors, blood lipids on GERD. Independent genetic variants associated with 13 kinds of dietary factors and 5 kinds of blood lipids at the genome-wide significance level were selected as instrumental variables. The summary statistics for GERD were obtained from European Bioinformatics Institute, including 129,080 cases and 473,524 controls. Inverse variance weighted was utilized as the main statistical method. MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis were performed to evaluate possible heterogeneity and pleiotropy. And the potential reverse causality was assessed using Steiger filtering.The results of the inverse variance weighted method indicated that genetically predicted total pork intake (OR = 2.60, 95% CI: 1.21–5.58, p = 0.0143), total bread intake (OR = 0.68, 95% CI: 0.46–0.99, p = 0.0497), total cereal intake (OR = 0.42, 95% CI: 0.31–0.56, p = 2.98E-06), and total cheese intake (OR = 0.41, 95% CI: 0.27–0.61, p = 1.06E-05) were associated with the risk of GERD. Multivariable Mendelian randomization analysis also revealed a negative association between total cereal intake, total cheese intake and the risk of GERD, but the effect of total pork intake and total bread intake on GERD disappeared after adjustment of smoking, alcohol consumption, use of calcium channel blockers, BMI, physical activity levels, and biological sex (age adjusted). Furthermore, the concentration of low-density lipoprotein cholesterol (LDL-C) is negatively correlated with total cheese intake, which mediates the impact of total cheese intake on GERD. The proportion mediated by LDL-C is 2.27% (95%CI: 1.57%, 4.09%).This study provides evidence that an increase in total cereal intake and total cheese intake will decrease the risk of GERD. Additionally, LDL-C mediates the causal effect of total cheese intake on GERD. These results provide new insights into the role of dietary factors and blood lipids in GERD, which is beneficial for disease prevention.Results: Growing studies have indicated an association between dietary factors and gastroesophageal reflux disease (GERD). However, whether these associations refer to a causal relationship and the potential mechanism by which dietary factors affect GERD is still unclear.A two-step mendelian randomization analysis was performed to obtain causal estimates of dietary factors, blood lipids on GERD. Independent genetic variants associated with 13 kinds of dietary factors and 5 kinds of blood lipids at the genome-wide significance level were selected as instrumental variables. The summary statistics for GERD were obtained from European Bioinformatics Institute, including 129,080 cases and 473,524 controls. Inverse variance weighted was utilized as the main statistical method. MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis were performed to evaluate possible heterogeneity and pleiotropy. And the potential reverse causality was assessed using Steiger filtering.The results of the inverse variance weighted method indicated that genetically predicted total pork intake (OR = 2.60, 95% CI: 1.21–5.58, p = 0.0143), total bread intake (OR = 0.68, 95% CI: 0.46–0.99, p = 0.0497), total cereal intake (OR = 0.42, 95% CI: 0.31–0.56, p = 2.98E-06), and total cheese intake (OR = 0.41, 95% CI: 0.27–0.61, p = 1.06E-05) were associated with the risk of GERD. Multivariable Mendelian randomization analysis also revealed a negative association between total cereal intake, total cheese intake and the risk of GERD, but the effect of total pork intake and total bread intake on GERD disappeared after adjustment of smoking, alcohol consumption, use of calcium channel blockers, BMI, physical activity levels, and biological sex (age adjusted). Furthermore, the concentration of low-density lipoprotein cholesterol (LDL-C) is negatively correlated with total cheese intake, which mediates the impact of total cheese intake on GERD. The proportion mediated by LDL-C is 2.27% (95%CI: 1.57%, 4.09%).This study provides evidence that an increase in total cereal intake and total cheese intake will decrease the risk of GERD. Additionally, LDL-C mediates the causal effect of total cheese intake on GERD. These results provide new insights into the role of dietary factors and blood lipids in GERD, which is beneficial for disease prevention.Conclusions: Growing studies have indicated an association between dietary factors and gastroesophageal reflux disease (GERD). However, whether these associations refer to a causal relationship and the potential mechanism by which dietary factors affect GERD is still unclear.A two-step mendelian randomization analysis was performed to obtain causal estimates of dietary factors, blood lipids on GERD. Independent genetic variants associated with 13 kinds of dietary factors and 5 kinds of blood lipids at the genome-wide significance level were selected as instrumental variables. The summary statistics for GERD were obtained from European Bioinformatics Institute, including 129,080 cases and 473,524 controls. Inverse variance weighted was utilized as the main statistical method. MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis were performed to evaluate possible heterogeneity and pleiotropy. And the potential reverse causality was assessed using Steiger filtering.The results of the inverse variance weighted method indicated that genetically predicted total pork intake (OR = 2.60, 95% CI: 1.21–5.58, p = 0.0143), total bread intake (OR = 0.68, 95% CI: 0.46–0.99, p = 0.0497), total cereal intake (OR = 0.42, 95% CI: 0.31–0.56, p = 2.98E-06), and total cheese intake (OR = 0.41, 95% CI: 0.27–0.61, p = 1.06E-05) were associated with the risk of GERD. Multivariable Mendelian randomization analysis also revealed a negative association between total cereal intake, total cheese intake and the risk of GERD, but the effect of total pork intake and total bread intake on GERD disappeared after adjustment of smoking, alcohol consumption, use of calcium channel blockers, BMI, physical activity levels, and biological sex (age adjusted). Furthermore, the concentration of low-density lipoprotein cholesterol (LDL-C) is negatively correlated with total cheese intake, which mediates the impact of total cheese intake on GERD. The proportion mediated by LDL-C is 2.27% (95%CI: 1.57%, 4.09%).This study provides evidence that an increase in total cereal intake and total cheese intake will decrease the risk of GERD. Additionally, LDL-C mediates the causal effect of total cheese intake on GERD. These results provide new insights into the role of dietary factors and blood lipids in GERD, which is beneficial for disease prevention. [ABSTRACT FROM AUTHOR]

Hydrogen storage by means of catalytic hydrogenation of suitable organic substrates helps to elevate the volumetric density of hydrogen energy. In this regard, utilizing cheaper industrial crude hydrogen to fulfill the goal of hydrogen storage would show economic attraction. However, because CO impurities in crude hydrogen can easily deactivate metal active sites even in trace amounts such a process has not yet been realized. Here, we develop a robust RuNi/TiO2 catalyst that enables the efficient hydrogenation of toluene to methyl-cyclohexane under simulated crude hydrogen feeds with 1000–5000 ppm CO impurity at around 180 °C under atmospheric pressure. We show that the co-localization of Ru and Ni species during reduction facilitated the formation of tightly coupled metallic Ru-Ni clusters. During the catalytic hydrogenation process, due to the distinct bonding properties, Ru and Ni served as the active sites for CO methanation and toluene hydrogenation respectively. Our work provides fresh insight into the effective utilization and purification of crude hydrogen for the future hydrogen economy. Efficient storage of crude hydrogen, through toluene hydrogenation to methylcyclohexane, is often inhibited by CO impurities. Here, the authors develop a RuNi/TiO2 catalyst which avoids deactivation through promoting simultaneous CO methanation. [ABSTRACT FROM AUTHOR]

It is very attractive yet underexplored to synthesize heterocyclic moieties pertaining to biologically active molecules from biomass-based starting compounds. Herein, we report an electrocatalytic Achmatowicz reaction for the synthesis of hydropyranones from furfuryl alcohols, which can be readily produced from biomass-derived and industrially available furfural. Taking advantage of photo-induced polymerization of a bipyridyl ligand, we demonstrate the facile preparation of a heterogenized nickel electrocatalyst, which effectively drives the Achmatowicz reaction electrochemically. A suite of characterization techniques and density functional theory computations were performed to aid the understanding of the reaction mechanism. It is rationalized that the unsaturated coordination sphere of nickel sites in our electrocatalyst plays an important role at low applied potential, not only allowing the intimate interaction between the nickel center and furfuryl alcohol but also enabling the transfer of hydroxide from nickel to the bound furfuryl alcohol. The synthesis of heterocyclic moieties pertaining to biologically active molecules from biomass-based starting compounds is very attractive yet underexplored. Herein, the authors report an electrocatalytic Achmatowicz reaction for the synthesis of hydropyranones from biomass-derived furfuryl alcohols. [ABSTRACT FROM AUTHOR]

Network embedding is a classical topic in network analysis. Current network embedding methods mostly focus on deterministic embedding, which maps each node as a low-dimensional vector. Thus, the network uncertainty and the possible multiple roles of nodes cannot be well expressed. In this paper, we propose to embed a single node as a mixture of Gaussian distribution in a low-dimensional space. Each Gaussian component corresponds to a latent role that the node plays. The proposed approach thus can characterize network nodes in a comprehensive representation, especially bridging nodes, which are relevant to different communities. Experiments on real-world network benchmarks demonstrate the effectiveness of our approach, outperforming the state-of-the-art network embedding methods. Also, we demonstrate that the number of components learned for each node is highly related to its topology features, such as node degree, centrality and clustering coefficient. [ABSTRACT FROM AUTHOR]

Long-chain alcohols synthesis (LAS, C5+OH) from syngas provides a promising route for the conversion of coal/biomass/natural gas into high-value chemicals. Cu-Fe binary catalysts, with the merits of cost effectiveness and high CO conversion, have attracted considerable attention. Here we report a nano-construct of a Fe5C2-Cu interfacial catalyst derived from Cu4Fe1Mg4-layered double hydroxide (Cu4Fe1Mg4-LDH) precursor, i.e., Fe5C2 clusters (~2 nm) are immobilized onto the surface of Cu nanoparticles (~25 nm). The interfacial catalyst exhibits a CO conversion of 53.2%, a selectivity of 14.8 mol% and a space time yield of 0.101 g gcat−1 h−1 for long-chain alcohols, with a surprisingly benign reaction pressure of 1 MPa. This catalytic performance, to the best of our knowledge, is comparable to the optimal level of Cu-Fe catalysts operated at much higher pressure (normally above 3 MPa). Long-chain alcohols synthesis from syngas conversion is a promising route for the production of high-value chemicals. Here the authors show that a heterogeneous Fe5C2/Cu catalyst derived from layered double hydroxides precursor exhibits excellent performance with a space time yield of 0.101 g gcat−1 h−1 at a low pressure of 1 MPa. [ABSTRACT FROM AUTHOR]

In this paper we investigate the top- k-selection problem, i.e. to determine and sort the top k elements, in the dynamic data model. Here dynamic means that the underlying total order evolves over time, and that the order can only be probed by pair-wise comparisons. It is assumed that at each time step, only one pair of elements can be compared. This assumption of restricted access is reasonable in the dynamic model, especially for massive data sets where it is impossible to access all the data before the next change occurs. Previously only two special cases were studied (Anagnostopoulos et al. in 36th international colloquium on automata, languages and programming (ICALP). LNCS, vol 5566, pp 339-350, 2009) in this model: selecting the element of a given rank, and sorting all elements. This paper systematically deals with $$1\le k\le n$$ . Specifically, we identify the critical point $$k^*$$ such that the top- k-selection problem can be solved error-free with probability $$1-o(1)$$ if and only if $$k=o(k^*)$$ . A lower bound of the error when $$k=\varOmega (k^*)$$ is also determined, which actually is tight under some conditions. In contrast, we show that the top- k-set problem, which means finding the top k elements without sorting them, can be solved error-free with probability $$1-o(1)$$ for all $$1\le k\le n$$ . Additionally, we consider some extensions of the dynamic data model and show that most of these results still hold. [ABSTRACT FROM AUTHOR]

Provides information on the interaction model in computing technology. Relationship between interaction and algorithm; Simulation of the Turing machine paradigm; Illustration of theorems.